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 .
DETAILED ACTION
Response to Amendment
The present Office Action is in response to the Request for Continued Examination dated 04/15/2026.
In the amendment dated 04/15/2026, the following occurred: Claims 1, 13 and 14 were amended. Claims 5 and 18 were canceled.
Claims 1-4, 6-17 and 19-20 are currently pending.
Request for Continued Examination
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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 04/15/2026 has been entered.
Claim Objections
Claim 13 is objected to for the following informality: “…determining a persona of a user based on received user-generated data through a persona clustering machine learning model;…” should read “…determining a persona of a user based on received user-generated data through the persona clustering machine learning model;…”
Claim 14 is objected for the following informality: “A targeted content system for delivering real-time targeted content to the target user for end of life advance care planning, comprising: one or more processors communicatively coupled to one or more EMR providers and a user device of a target user undergoing end of life advance care planning located at a healthcare setting;…” should read “A targeted content system for delivering real-time targeted content to a target user for end of life advance care planning, comprising: one or more processors communicatively coupled to one or more EMR providers and a user device of the target user undergoing end of life advance care planning located at a healthcare setting;…”
Claim 14 is also objected to for the following informality: “…determining a persona of a user, based on user-generated data received via a user interface of the user device in the healthcare setting and EMR data received from an EMR provider, through a persona clustering machine learning model;…” should read “…determining a persona of a user, based on user-generated data received via a user interface of the user device in the healthcare setting and EMR data received from an EMR provider, through the persona clustering machine learning model;...”
Appropriate corrections is required.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-4, 6-17 and 19-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
Claims 1, 13 and 14 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1
The claim recites a method, one or more non-transitory computer-readable storage media and system for delivering targeted content with care preference suggestions, which are within a statutory category.
Step 2A1
Regarding claims 1, 13 and 14, the limitation of (claim 1 being representative) training a persona clustering machine learning model to correlate at least readiness values to a persona; determining a persona of a user based on received user-generated data through the persona clustering machine learning model; calculating one or more key motivators to engage in advance care planning according to the persona, wherein the one or more key motivators comprise family independence of the user, financial security of the user, or a combination thereof; determining targeted content for the user based on the persona and the one or more key motivators, the targeted content comprising care preference suggestions determined based on care preferences relating to advance care directives for which the users in the set of similar users have previously opted; receiving interactions of the targeted content; updating the persona clustering machine learning model based on the interactions; and adjusting the targeted content via the persona clustering machine learning model in real time based on the interactions of the targeted content received as drafted, is a process that, under the broadest reasonable interpretation, covers certain methods of organizing human activity (i.e., managing personal behavior including following rules or instructions) but for the recitation of generic computer components. The claims encompass a series of rules or instructions for a person or persons to follow, with or without the aid of a computer, to train a model, determine a persona of a user, calculate key motivators, determine targeted content, receive interactions, update the model and adjust the targeted content in the manner described in the identified abstract idea, supra. The rules or instructions are the claimed steps of “training… determining… calculating… determining… receiving… updating… and adjusting targeted content” as indicated supra.
Other than reciting generic computer components (discussed infra), i.e., one or more processors (in claim 1), one or more non-transitory computer-readable storage media and one or more processors (in claim 13) and a targeted content system, one or more processors, and a computer-readable storage device (in claim 14), the claimed invention amounts to managing personal behavior or interaction between people. The Examiner notes that certain “method[s] of organizing human activity” includes a person’s interaction with a computer (see MPEP 2106.04(a)(2)(II)). If a claim limitation, under its broadest reasonable interpretation, covers managing personal behavior or interactions between people, but for the recitation of generic computer components, then it falls within the “Certain Methods of Organizing Human Activity – Managing Personal Behavior Relationships, Interactions Between People (e.g. social activities, teaching, following rules or instructions)” grouping of abstract ideas.
The claims further recite a “training a persona clustering machine learning model.” When given their broadest reasonable interpretation in light of the disclosure, the training of persona clustering machine learning model represents the creation of mathematical interrelationships between data. Specification at Para. 055 describes the training as relationships between variables are determined and weighted. As such, the training of the persona clustering machine learning model represents a mathematical concept that is interpreted to be part of the identified abstract idea, supra. The types of identified abstract ideas are considered together as a single abstract idea for analysis purposes.
Step 2A2
This judicial exception is not integrated into a practical application. In particular, claims 1 recites the additional element of one or more processors. Claim 13 recites the additional elements of one or more non-transitory computer-readable storage media and one or more processors. Claim 14 recites the additional elements of a targeted content system, one or more processors, and a computer-readable storage device. These additional element are not exclusively defined by the applicant and are recited at a high-level of generality (i.e., a generic computer component for enabling access to medical information or for performing generic computer functions, see written description at para. [033] that states “the computer includes one or more hardware central processing units (CPUs) or general purpose graphics processing units (GPGPUs) that carry out the device's functions”) such that they amount to no more than mere instructions to apply the exception using a generic computer component. As set forth in MPEP 2106.04(d) “merely including instructions to implement an abstract idea on a computer” is an example of when an abstract idea has not been integrated into a practical application. Accordingly, even in combination, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea.
Claims 1, 13 and 14 recites the additional element of training a persona clustering machine learning model to output a persona of a user. This represents a mathematical concept as described in the Specification at Para. 055 (Examples of machine learning algorithms may include a support vector machine (SVM), a naive Bayes classification, a random forest, a neural network, deep learning, or other supervised learning algorithm or unsupervised learning algorithm for classification and regression. The machine learning algorithms may be trained using one or more training datasets. For example, previously received contextual data may be employed to train various algorithms. Moreover, as described above, these algorithms can be continuously trained/retrained using real-time user data as it is received. In some embodiments, the machine learning algorithm employs regression modelling where relationships between variables are determined and weighted. In some embodiments, the machine learning algorithm employ regression modelling, wherein relationships between predictor variables and dependent variables are determined and weighted.). This mathematical concept is applied to (“apply it’) the abstract idea. MPEP 2106.04(d)(I) indicates that merely saying “apply it” or equivalent to the abstract idea cannot provide a practical application.
Claims 1, 13 and 14 further recite the additional element of a user-interface. Claims 1 and 13 recited the additional element of a targeted content platform. Claim 14 recited the additional element of a user device. These additional elements merely generally links the abstract idea to a particular technological environment or field of use. MPEP 2106.04(d)(I) indicates that generally linking an abstract idea to a particular technological environment or field of use cannot provide a practical application.
Accordingly, even in combination, these additional elements do not integrate the abstract idea into a practical application.
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 element of one or more processors, one or more non-transitory computer-readable storage media, a targeted content system, and a computer-readable storage device to perform the noted steps amounts to no more than mere instructions to apply the exception using a generic computer component. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept (“significantly more”). Moreover, using generic computer components to perform abstract ideas does not provide a necessary inventive concept. See Alice, 573 U.S. at 223 (“mere recitation of a generic computer cannot transform a patent-ineligible abstract idea into a patent-eligible invention”). Therefore, whether considered alone or in combination, the additional elements do not amount to significantly more than the abstract idea.
As discussed above with respect to integration of the abstract idea into a practical application, the additional element of training a persona clustering machine learning model to determine a persona of a user was determined to be the application of trained machine learning model to the identified abstract idea. This has been re-evaluated under the “significantly more” analysis and has also been found insufficient to provide significantly more. MPEP2106.05(1)(A) indicates that merely saying “apply it’ or equivalent to the abstract idea cannot provide an inventive concept (“significantly more’). As such the claim is not patent eligible.
Also, as discussed above with respect to integration of the abstract idea into a practical application, the additional elements of a user-interface, a targeted content platform and a user device were determined to generally link the abstract idea to a particular technological environment or field of use. This has been re-evaluated under “significantly more” analysis and has also been found insufficient to provide significantly more. MPEP 2106.05(A) indicates that generally linking an abstract idea to a particular technological environment or field of use cannot provide significantly more. Accordingly, even in combination, these additional elements do not provide significantly more. As such the claim is not patent eligible.
The examiner notes that: A well-known, general-purpose computer has been determined by the courts to be a well-understood, routine and conventional element (see, e.g., Alice Corp. v. CLS Bank; see also MPEP 2106.05(d)); Receiving and/or transmitting data over a network (“a communications network”) has also been recognized by the courts as a well - understood, routine and conventional function (see, e.g., buySAFE v. Google; MPEP 2016(d)(II)); and Performing repetitive calculations is/are also well-understood, routine and conventional computer functions when they are claimed in a merely generic manner (see, e.g., Parker v. Flook; MPEP 2016.05(d)).
Claims 2-3, 4-12, 15-17 and 19-20 are similarly rejected because they either further define the abstract idea and/or do not further limit the claim to a practical application or provide as inventive concept such that the claims are subject matter eligible even when considered individually or as an ordered combination. Dependent claims 2 and 15 further define determining physician recommendation. Dependent claims 3 and 16 further define receiving and inputting a plurality of responses and calculating the persona. Dependent claims 4, 6 and 17 further define retraining the persona clustering machine learning model. Dependent claim 7 further defines receiving a selection of care preferences and persisting the selected care preferences to a data store. Claim(s) 7 also include the additional element of “a data store” which is recited at high level generality and amount to extra solution activity. MPEP 2106.04(d)(I) indicates that extra-solution data gathering activity cannot provide a practical application and prior art of record indicates that a data store is well-understood, routine and conventional activity (see Malik at [0024] and [0025] and Dias at [0009]). Well-understood, routine, conventional activity cannot provide an inventive concept (“significantly more”). Dependent claims 8 and 19 further define verifying the selection of care preferences and generating an advance directive. Dependent claim 9 further defines further verifying the selection of acre preferences. Dependent claim 10 further defines providing the advance directive, receiving a digital mark and verifying the digital mark. Dependent claims 11 and 20 further define receiving EMR data and determining persona. Claim(s) 11 and 20 also include the additional element of “an application program interface (API)” which merely generally links the abstract idea to a particular technological environment or field of use. MPEP 2106.04(d)(I) indicates that generally linking an abstract idea to a particular technological environment or field of use cannot provide a practical application. MPEP 2106.05(A) indicates that generally linking an abstract idea to a particular technological environment or field of use cannot provide significantly more. Dependent claims 12 and 20 further define the EMR data. Dependent claim 19 further defines receiving a selection of care preferences, verifying the selection, providing an advance directive and receiving a digital mark. Claims 2-3, 4-12, 15-17 and 19-20 further define the abstract idea and are rejected for the same reason presented above with respect to claims 1, 13 and 14.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 1-13 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claims 1 and 13 recite the limitation "receiving, via the user-interface, interactions of the targeted content at a targeted content platform" in page 2, line 15-16. There is insufficient antecedent basis for this limitation in the claim. A user-interface was never previously recited. The Examiner suggests amendments to recite ‘a user-interface’ instead of ‘the user-interface’.
Dependent claims are rejected by virtue of dependency.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 1-4, 6-17 and 19-20 are rejected under 35 U.S.C. 103 as being unpatentable over Mandaviya (US 10853424 B1), in view of Abedini (US 2017/0177814), in view of Dias (US 2018/0060494) and in further view of Malik (US 2019/0236736).
REGARDING CLAIM 1
Mandaviya discloses a computer-implemented method for providing targeted content comprising suggestions for care preference, the method being executed by one or more processors and comprising: training a persona clustering machine learning model to correlate at least readiness values to a persona (Mandaviya at [5:28-30] teaches input to a machine learning algorithm for topic modeling to identify one or more persona segments (interpreted by examiner as means for correlate at least readiness values to a persona) and [14:18-24] teaches data is obtained from multiple user accounts (interpreted by examiner as the received user-generated data of Abedini below) to train persona model (interpreted by examiner as training the persona clustering machine learning model));
Mandaviya does not explicitly disclose determining a persona of a user based on received user-generated data through a persona clustering machine learning model, receiving, via the user-interface, interactions of the targeted content at a targeted content platform; updating the persona clustering machine learning model based on the interactions; and adjusting the targeted content via the persona clustering machine learning model in real time based on the interactions of the targeted content received, however Abedini discloses:
determining a persona of a user based on received user-generated data through a persona clustering machine learning model (Abedini at [0059] teaches a user interface. [0060] teaches one or more hardware processors may communicate with one or more servers over a computer communications network to obtain data associated with one or more of the user and may analyze the data to determine preferences of the user, for example, including analyzing for personality traits of the user to determine the preferences (interpreted by examiner as the received user-generated data). [0033] teaches historical data on previous treatment experience associated with the patient may be obtained to build a patient's treatment philosophical profile. For instance, in case of partially built profiles (or not complete profile) of a patient, the methodology of the present disclosure in one embodiment may use similarity and/or clustering methods to find patients with similar personality and augment their preferences and/or approaches to start with. For example, patient preference or personality profile may be inferred from textual data and based on the textual data, patients with similar preference or personality may be detected (interpreted by examiner as determining a persona of a user based on received user-generated data through a persona clustering machine learning model of Mandaviya); receiving, via the user-interface, interactions of the targeted content at a targeted content platform; updating the persona clustering machine learning model based on the interactions; and adjusting the targeted content via the persona clustering machine learning model in real time based on the interactions of the targeted content received (Abedini at [0058] teaches the machine learning may include building and updating the treatment philosophical profile and preferences of patients gradually through the incremental learning process and that over time, interaction and feedback from patients updates the profiles of the individual (interpreted by examiner as receiving interactions of the targeted content, updating the persona clustering machine learning model based on the interactions and adjusting the targeted content via the persona clustering machine learning model in real time based on the interactions of the targeted content received)).
It would have been obvious for one of the ordinary skill in the art before the effective filling date of the claimed invention to have modified the persona clustering machine learning model of Mandaviya to incorporate determining a persona of a user based on received user-generated data through a persona clustering machine learning model, receiving, via the user-interface, interactions of the targeted content at a targeted content platform; updating the persona clustering machine learning model based on the interactions; and adjusting the targeted content via the persona clustering machine learning model in real time based on the interactions of the targeted content received as taught by Abedini, with the motivation of improving the quality life of the patient and bringing measurable success in the treatment process. (Abedini at [0002]).
Mandaviya and Abedini do not explicitly disclose determining targeted content for the user based the persona and the one or more key motivators, however Dias discloses:
determining targeted content for the user based the persona and the one or more key motivators (Dias at [0143] teaches using a clustering algorithm (interpreted by examiner as the persona clustering machine learning model of Mandaviya and Abedini). [0052] teaches particular monitoring actions to be employed are matched to the specific personalized patient care plan that is associated with the patient. That is, for each patient care plan action, there may be a set of one or more possible monitoring actions that may be associated with that type of patient care plan action. Selection from amongst the one or more possible monitoring actions may be performed based on an analysis of the patient's lifestyle information to determine the most appropriate monitoring action that will not interfere with the patient's lifestyle and will most likely result in a positive response from the patient. For example, if it is determined that the patient's lifestyle is such that the patient eats breakfast at 8:30 a.m. and one of the patient care plan actions is to eat oatmeal for breakfast three times a week, then a monitoring action may be selected that involves texting the patient with a message at 8:25 a.m., with the message having content that states “consider eating oatmeal for breakfast today.” [0142] teaches targeted content is the personalized patient care plan that may be outputted to the patient to perform patient actions (interpreted by examiner as determining targeted content for the user based the persona and the one or more key motivators of Malik below)),
It would have been obvious for one of the ordinary skill in the art before the effective filling date of the claimed invention to have modified the persona clustering machine learning model of Mandaviya and the methods for determining a persona of a user, receiving interactions of the targeted content, updating the persona clustering machine learning model based on the interactions and adjusting the targeted content via the persona clustering machine learning model in real time of Abedini to incorporate calculating one or more key motivators to engage in advance care planning according to the persona and determining targeted content for the user based the persona and the one or more key motivators as taught by Dias, with the motivation of preventing or reducing the frequency and magnitude of a catastrophic event such as a hospitalization (Dias at [0002]).
Mandaviya, Abedini and Dias do not explicitly disclose calculating one or more key motivators to engage in advance care planning according to the persona, wherein the one or more key motivators comprise family independence of the user, financial security of the user, or a combination thereof, the targeted content comprising care preference suggestions determined based on care preferences relating to advance care directives for which the users in the set of similar users have previously opted, however Malik discloses:
calculating one or more key motivators to engage in advance care planning according to the persona, wherein the one or more key motivators comprise family independence of the user, financial security of the user, or a combination thereof (Malik at [abstract] teaches a computerized process for advanced care planning of a customer user includes, within a computerized processor, operating programming configured to monitoring designation by the customer user of end of life wishes of the customer user and [0040] teaches designations can be made within the process. For example, a will enabling disposition of personal possessions and funds can be entered in the process. Wishes regarding custody of minor children and pets can be communicated. Links including access information to financial funds, life insurance policies, security boxes held at banks, financial trust details, and other similar information can be securely held (interpreted by examiner as calculating one or more key motivators to engage in advance care planning according to the persona wherein the one or more key motivators comprise financial security of the user)); the targeted content comprising care preference suggestions determined based on care preferences relating to advance care directives for which the users in the set of similar users have previously opted (Malik at [0011] teaches the user selects preferences for end of life medical care, including end of life personal care and end of life care arrangements. [0025] teaches choices and wishes database can include data, completed questionnaires, will documents, and other related documentation related to the selections made by users in the disclosed process (interpreted by examiner as the targeted content comprising care preference suggestions determined based on care preferences relating to advance care directives for which the users in the set of similar users have previously opted))
It would have been obvious for one of the ordinary skill in the art before the effective filling date of the claimed invention to have modified the persona clustering machine learning model of Mandaviya, the methods for determining a persona of a user, receiving interactions of the targeted content, updating the persona clustering machine learning model based on the interactions and adjusting the targeted content via the persona clustering machine learning model in real time of Abedini and the method for determining targeted content for the user based the persona and the one or more key motivators of Dias to incorporate calculating one or more key motivators to engage in advance care planning according to the persona wherein the one or more key motivators comprise family independence of the user, financial security of the user, or a combination thereof, the targeted content comprising care preference suggestions determined based on care preferences relating to advance care directives for which the users in the set of similar users have previously opted as taught by Malik, with the motivation of providing guidance in financial or medical issues (Malik at [0004]).
REGARDING CLAIM 2
Mandaviya, Abedini, Dias and Malik disclose the limitation of claim 1.
Mandaviya, Dias and Malik do not explicitly disclose determining physician recommendation for care preferences based on the user-generated data and physician recommendations for a similar set of users, wherein the targeted content comprises the physician recommendation for care preferences, however Abedini discloses:
The method of claim 1, comprising: determining physician recommendation for care preferences based on the user-generated data and physician recommendations for a similar set of users, wherein the targeted content comprises the physician recommendation for care preferences (Abedini at [0036] teaches the match between patient and doctor at for a specific health problem may be carried out from metrics such as comparing patient's preference and doctor's approach as obtained from the respective treatment philosophical profiles. The match between patient and doctor at for a specific health problem may be determined, for example, by accumulating past feedback on doctor's approaches on the specific health problem from similar patients where similarity is defined on the basis of patient's preference. In one embodiment, if available, both the patient's preference and preference of similar patients may be utilized in performing the matching. Similarly, if available, both doctor's preference and preference of similar doctors may be utilized in performing the matching. The matching may output a recommendation list of doctors/physicians (interpreted by examiner as determining physician recommendation for care preferences based on the user-generated data and physician recommendations for a similar set of users, wherein the targeted content comprises the physician recommendation for care preferences)).
It would have been obvious for one of the ordinary skill in the art before the effective filling date of the claimed invention to have modified the methods of Mandaviya, Dias and Malik to incorporate determining physician recommendation for care preferences based on the user-generated data and physician recommendations for a similar set of users, wherein the targeted content comprises the physician recommendation for care preferences as taught by Abedini, with the motivation of improving the quality life of the patient and bringing measurable success in the treatment process. (Abedini at [0002]).
REGARDING CLAIM 3
Mandaviya, Abedini, Dias and Malik disclose the limitation of claim 1.
Mandaviya, Abedini and Malik do not explicitly disclose receiving a plurality of responses of targeted content from a plurality of users; inputting the plurality of responses into the persona clustering model; and calculating the persona from the persona clustering model based at least in part on the plurality of responses previously received user-generated data for a plurality of other users, wherein the targeted content is determined based on the persona, however Dias discloses:
The method of claim 1, further comprising: receiving a plurality of responses of targeted content from a plurality of users; inputting the plurality of responses into the persona clustering model; and calculating the persona from the persona clustering model based at least in part on the plurality of responses previously received user-generated data for a plurality of other users, wherein the targeted content is determined based on the persona (Dias at [0035] teaches the various lifestyle information data may be obtained directly from the patient, such as via an electronic questionnaire and [0131] teaches the lifestyle information sources may be provided as a database and/or computing system that gathers and stores information from the patient indicating the patient's response to questionnaires. [0039] teaches collect patient demographic and medical data, such as from questionnaires and generate a baseline patient care plan based on an initial diagnosis of the patient's medical condition (interpreted by examiner as calculating the persona from the persona clustering model based at least in part on the plurality of responses)).
It would have been obvious for one of the ordinary skill in the art before the effective filling date of the claimed invention to have modified the methods of Mandaviya, Abedini and Malik to incorporate receiving a plurality of responses of targeted content from a plurality of users; inputting the plurality of responses into the persona clustering model; and calculating the persona from the persona clustering model based at least in part on the plurality of responses previously received user-generated data for a plurality of other users, wherein the targeted content is determined based on the persona as taught by Dias, with the motivation of preventing or reducing the frequency and magnitude of a catastrophic event such as a hospitalization (Dias at [0002]).
REGARDING CLAIM 4
Mandaviya, Abedini, Dias and Malik disclose the limitation of claim 1.
Mandaviya, Dias and Malik do not explicitly disclose wherein the persona clustering model is retrained with the determined persona and the received user-generated data, however Abedini discloses:
The method of claim 3, wherein the persona clustering model is retrained with the determined persona and the received user-generated data (Abedini at [0037] teaches feedback is collected from both patients and doctors to modify the matching algorithm as well as the treatment philosophical profile of each other. Treatment philosophical profile for both patients and doctors may also be reconstructed on regular interval to cater for temporal changes and [0061] teaches one or more hardware processors may also receive feedback from one or more of the user and the health care providers regarding the match. For instance, the user who acted on the recommended doctor, after consulting with the doctor, may provide feedback as to the accuracy of the match. Similarly, a health care provider may invoke a user interface provided in the cognitive health care apparatus to input feedback. One or more hardware processors may modify the machine learning model to retrain the model based on the feedback (interpreted by examiner as wherein the persona clustering model is retrained with the determined persona and the received user-generated data)).
It would have been obvious for one of the ordinary skill in the art before the effective filling date of the claimed invention to have modified the methods of Mandaviya, Dias and Malik to incorporate wherein the persona clustering model is retrained with the determined persona and the received user-generated data as taught by Abedini, with the motivation of improving the quality life of the patient and bringing measurable success in the treatment process. (Abedini at [0002]).
REGARDING CLAIM 6
Mandaviya, Abedini, Dias and Malik disclose the limitation of claim 1.
Mandaviya, Abedini and Dias do not explicitly disclose wherein the targeted content comprises education regarding advance care planning for the user, wherein the user can select care preferences, however Malik further discloses:
The method of claim 5, wherein the targeted content comprises education regarding advance care planning for the user, wherein the user can select care preferences (Malik at [0017] teaches the disclosed process and system can provide educational or advisory materials, aiding a user to make educated decisions about the provided services. In one example, the process can provide videos explaining end of life options and options for estate planning. In another example, an attorney or professional can be made available through video conference or through chat messaging to answer questions (interpreted by examiner as wherein the targeted content comprises education regarding advance care planning for the user, wherein the user can select care preferences) [0033] teaches information about representatives of the customer user, for example, information related to a designated agent or medical provider for the customer user. Step 226 enables entry of a primary care provider such as a physician. [0011] teaches allowing user to create an advance directive by following self-directed prompts and options. The user creates a profile with information including personal contact info and medical provider information. In one embodiment, the user selects preferences for end of life medical care, including end of life personal care and end of life care arrangements. In one embodiment, the user has option to create or import a medical char).
It would have been obvious for one of the ordinary skill in the art before the effective filling date of the claimed invention to have modified the methods of Mandaviya, Abedini and Dias to incorporate wherein the targeted content comprises education regarding advance care planning for the user, wherein the user can select care preferences as taught by Malik, with the motivation of providing guidance in financial or medical issues (Malik at [0004]).
REGARDING CLAIM 7
Mandaviya, Abedini, Dias and Malik disclose the limitation of claim 6.
Mandaviya, Abedini and Dias do not explicitly disclose after providing the targeted content, receiving, from the user-interface, a selection of the care preferences; and persisting the selected care preferences to a data store, however Malik further discloses:
The method of claim 6, comprising: after providing the targeted content, receiving, from the user-interface, a selection of the care preferences; and persisting the selected care preferences to a data store (Malik at [0011] teaches the user selects preferences for end of life medical care, including end of life personal care and end of life care arrangements. [0025] teaches choices and wishes database can include data, completed questionnaires, will documents, and other related documentation related to the selections made by users in the disclosed process (interpreted by examiner as receiving, from the user-interface, a selection of the care preferences; and persisting the selected care preferences to a data store)).
It would have been obvious for one of the ordinary skill in the art before the effective filling date of the claimed invention to have modified the methods of Mandaviya, Abedini and Dias to incorporate after providing the targeted content, receiving, from the user-interface, a selection of the care preferences; and persisting the selected care preferences to a data store as taught by Malik, with the motivation of providing guidance in financial or medical issues (Malik at [0004]).
REGARDING CLAIM 8
Mandaviya, Abedini, Dias and Malik disclose the limitation of claim 7.
Mandaviya, Abedini and Dias do not explicitly disclose verifying the selection of care preferences, and generating an advance directive based on the selection of care preferences, however Malik further discloses:
The method of claim 7, comprising: verifying the selection of care preferences, and generating an advance directive based on the selection of care preferences (Malik at [0018] teaches the disclosed system enables one to go through secure identity confirmation, and the user can then edit his or her plan. [0034] teaches an email or other electronic communication is sent to the designated agent given durable power of attorney. A request is made at step 244 to the agent to confirm acceptance of durable power of attorney. At step 246, the agent is given an opportunity to review the related documents created by the process. At step 248, the agent confirms/signs documentation confirming the durable power of attorney and it is returned to the process. At step 250, the agent is provided a thank you and is given an optional opportunity to register in the system for ease of later contact (interpreted by examiner as verifying the selection of care preferences, and generating an advance directive based on the selection of care preferences)).
It would have been obvious for one of the ordinary skill in the art before the effective filling date of the claimed invention to have modified the methods of Mandaviya, Abedini and Dias to incorporate verifying the selection of care preferences, and generating an advance directive based on the selection of care preferences as taught by Malik, with the motivation of providing guidance in financial or medical issues (Malik at [0004]).
REGARDING CLAIM 9
Mandaviya, Abedini, Dias and Malik disclose the limitation of claim 8.
Mandaviya, Abedini and Dias do not explicitly disclose wherein verifying the selection of care preferences comprises verifying the completeness of the selection of care preferences and verifying a logical consistency of the selection of care preferences, however Malik further discloses:
The method of claim 8, wherein verifying the selection of care preferences comprises verifying the completeness of the selection of care preferences and verifying a logical consistency of the selection of care preferences ([0035] teaches the user is asked to confirm readiness to publish the information provided in the process. Upon confirmation of the user, sub-processes can be initiated. Legal requirements in some states can require a witness for choices and wishes designated in the process. Steps 268, 270, 272, and 274 enable a witness to be contacted, given an opportunity to review the necessary documents, confirm their role as a witness with the required signatures, and provide a thank you to the witness, respectively (interpreted by examiner as wherein verifying the selection of care preferences comprises verifying the completeness of the selection of care preferences and verifying a logical consistency of the selection of care preferences)).
It would have been obvious for one of the ordinary skill in the art before the effective filling date of the claimed invention to have modified the methods of Mandaviya, Abedini and Dias to incorporate wherein verifying the selection of care preferences comprises verifying the completeness of the selection of care preferences and verifying a logical consistency of the selection of care preferences as taught by Malik, with the motivation of providing guidance in financial or medical issues (Malik at [0004]).
REGARDING CLAIM 10
Mandaviya, Abedini, Dias and Malik disclose the limitation of claim 8.
Mandaviya, Abedini and Dias do not explicitly disclose providing, to the user-interface, the advance directive determined based on the selection of care preferences; receiving, from the user-interface, a digital mark of the user for the advance directive; and verifying the digital mark, however Malik further discloses:
The method of claim 8, comprising: providing, to the user-interface, the advance directive determined based on the selection of care preferences; receiving, from the user-interface, a digital mark of the user for the advance directive; and verifying the digital mark (Malik at [0020] teaches the system can utilize e-signature capability to receive signatures. Automatic requests for electronic signature can be utilized based upon technical and legal requirements related to estate planning and advanced care directives and [0050] teaches the disclosed process can include automatically gathering signatures. This can include determining a list of third party signatures required to enact the designated end of life wishes, automatically sending out forms to be signed based upon the list of third party signatures, and receiving and storing returned signed forms (wherein the signature is interpreted by examiner as the digital mark)).
It would have been obvious for one of the ordinary skill in the art before the effective filling date of the claimed invention to have modified the methods of Mandaviya, Abedini and Dias to incorporate providing, to the user-interface, the advance directive determined based on the selection of care preferences; receiving, from the user-interface, a digital mark of the user for the advance directive; and verifying the digital mark as taught by Malik, with the motivation of providing guidance in financial or medical issues (Malik at [0004]).
REGARDING CLAIM 11
Mandaviya, Abedini, Dias and Malik disclose the limitation of claim 1.
Mandaviya, Dias and Malik do not explicitly disclose receiving, via an application program interface (API) provided by an electronic medical records (EMR) provider, EMR data for the user, and determining the persona through the persona clustering model based on EMR data, however Abedini discloses:
The method of claim 1, comprising: receiving, via an application program interface (API) provided by an electronic medical records (EMR) provider, EMR data for the user, and determining the persona through the persona clustering model based on EMR data (Abedini at [0032] teaches generates or develops treatment philosophical profile for both patients and doctors based on patient's preference and doctor's approach towards the treatment regime for a specific health problem and other factors. The various data used in building the treatment philosophical profile may be obtained and used. The methodology of the present disclosure in one embodiment may generate profiles automatically based on natural language, for example, past records (interpreted by examiner as EMR data) [0060] teaches one or more hardware processors may communicate with one or more servers over a computer communications network to obtain data associated with one or more of the user and the health care providers. One or more servers may include, but are not limited to, one or more of social media server, social network server, electronic mail server, and text messaging server. One or more hardware processors may analyze the data to determine preferences of the user and the health care providers, for example, including analyzing for personality traits of the user and the health care providers to determine the preferences (interpreted by examiner as means to receiving, via an application program interface (API) provided by an electronic medical records (EMR) provider, EMR data for the user, since an API must be used to port the data from database and/or server through the network into the hardware processor) [0060] also teaches clustering the data into a predefined set of features as related to the user and the health care providers based on the preferences, for example, and build a computer-implemented machine learning model comprising user preference for a predefined set of features associated with the user's health condition and health care provider preference for the predefined set of features in treating the user's health condition (interpreted by examiner as determining the persona through the persona clustering model based on the EMR data)).
It would have been obvious for one of the ordinary skill in the art before the effective filling date of the claimed invention to have modified the methods of Mandaviya, Dias and Malik to incorporate receiving, via an application program interface (API) provided by an electronic medical records (EMR) provider, EMR data for the user, and determining the persona through the persona clustering model based on EMR data as taught by Abedini, with the motivation of improving the quality life of the patient and bringing measurable success in the treatment process. (Abedini at [0002]).
REGARDING CLAIM 12
Mandaviya, Abedini, Dias and Malik disclose the limitation of claim 1.
Mandaviya, Dias and Malik do not explicitly disclose wherein the EMR data comprises diagnoses, demographics, or visit history for the user, however Abedini discloses:
The method of claim 11, wherein the EMR data comprises diagnoses, demographics, or visit history for the user (Abedini at [0021] teaches the methodology in one embodiment may perform a “match” operation to select the most suited doctors for a patient in regard to the “treatment philosophy” profile for a specific ailment/health concern. The methodology in one embodiment may consider a number of features defining the “treatment philosophy profile”. These features may include, but not limited to, disease controlling or curing, short term or long term solution, surgical or medical treatment, informed or uninformed consent, individual versus (vs.) shared decision, doctor superiority vs. patient empowerment, benefitting or straightforward harshly, and others. The matching may considers variables including medical history and patients' preferred treatment method (interpreted by examiner as wherein the EMR data comprises diagnoses)).
It would have been obvious for one of the ordinary skill in the art before the effective filling date of the claimed invention to have modified the methods of Mandaviya, Dias and Malik to incorporate wherein the EMR data comprises diagnoses, demographics, or visit history for the user as taught by Abedini, with the motivation of improving the quality life of the patient and bringing measurable success in the treatment process. (Abedini at [0002]).
REGARDING CLAIMS 13 and 14
Claims 13 and 14 are analogous to Claims 1, 11 and 12 thus Claims 13 and 14 are similarly analyzed and rejected in a manner consistent with the rejection of Claims 1, 11 and 12.
REGARDING CLAIM 15, 16, 17, 19 and 20
Claims 15, 16, 17, 19 and 20 are analogous to Claims 2-4 and 6-12 thus Claims 15, 16, 17, 19 and 20 are similarly analyzed and rejected in a manner consistent with the rejection of Claims 2-4 and 6-12.
Response to Arguments
Rejection under 35 U.S.C. § 101
Regarding the rejection of claims 1-4, 6-17 and 19-20, the Examiner has considered the Applicant’s arguments, but does not find them persuasive. Applicant argues:
The Office characterizes claim 1 as being directed to "delivering targeted content with care preference suggestions." Applicant respectfully submits that this characterization abstracts claim 1 at an impermissibly high level and fails to account for the specific limitations recited therein… Here, the Office's characterization disregards the specific limitations of claim 1, including training a persona clustering machine learning model to correlate readiness values to a persona, determining a user's persona through that model, calculating key motivators (family independence, financial security, or a combination thereof) to engage in advance care planning, determining targeted content comprising care preference suggestions based on advance care directives for which similar users have previously opted. Each of these limitations imposes meaningful constraints on the scope of claim 1 that are not captured by the Office's generalized characterization. Further, MPEP 2106.04(a) requires that examiners explain how any identified limitations recite an abstract idea. The Office has not provided any such explanation with respect to the specific limitations of claim 1. The Office has not explained how any of the recited limitations, including training a persona clustering machine learning model… or adjusting the targeted content via the persona clustering machine learning model in real time based on the interactions of the targeted content received, recites a method of organizing human activity. The Office's conclusory assertion that claim 1 is directed to an abstract idea, without any particularized analysis of how each recited limitation falls within a recognized category of abstract ideas, is insufficient to establish a prima facie case of patent ineligibility under 35 U.S.C. § 101. (See MPEP 2106.04(a) (requiring examiners to identify the specific abstract idea grouping and explain how the claim limitations fall within that grouping)).
Regarding 1, the Examiner respectfully disagrees. Under Step 1 analysis, the Examiner determined that the claims fall within a statutory category and stated that the claim recites a method, one or more non-transitory computer-readable storage media and system for delivering targeted content with care preference suggestions, which is the title of the application. The Examiner then went on to detail all claim limitations that characterize the abstract idea under step analysis 2A1, which is the bolded portion of the analysis. The Examiner did not disregard any limitation of claim 1. The Examiner provided a detailed explanation of how under the broadest reasonable interpretation, the claims as drafted cover certain methods of organizing human activity (i.e., managing personal behavior including following rules or instructions) and clearly identified the series of rules or instructions that a person or persons would follow, with or without the aid of a computer, to implement the Applicants abstract idea.
Applicant respectfully submits that claim 1 does not recite a method of organizing human activity. The limitations of claim 1 are directed to automated operations performed by one or more processors, including training a persona clustering machine learning model to correlate readiness values to engage in advanced care planning to a persona, determining a persona through the trained model, calculating key motivators via the model, updating the persona clustering machine learning model based on received interactions, and adjusting targeted content via the persona clustering machine learning model in real time. None of these limitations recite, describe, or require the organization, management, or regulation of human activity. The Office's assertion that claim 1 covers managing personal behavior or interactions between people under its broadest reasonable interpretation but for the recitation of generic computing components is without merit, as the claim limitations are not directed to managing any human behavior but rather to the automated training, updating, and deployment of a machine learning model to generate and adjust targeted content. Moreover, the mere possibility that a user may view or interact with the targeted content does not render claim 1 directed to a method of organizing human activity. The claim is directed to the automated technological process by which the persona clustering machine learning model is trained, deployed, and iteratively refined, not to the regulation or management of any human conduct. That a human user may ultimately receive or view the output of the claimed system does not transform the nature of the claimed invention from a technological process into a method of organizing human activity.
Regarding 2, the Examiner respectfully disagrees. The claims encompass a series of rules or instructions for a person or persons to follow, with or without the aid of a computer, to train a model, determine a persona of a user, calculate key motivators, determine targeted content, receive interactions, update the model and adjust the targeted content, which is an abstract idea and is a process that, under the broadest reasonable interpretation, covers certain methods of organizing human activity (i.e., managing personal behavior including following rules or instructions) but for the recitation of generic computer components. The rules or instructions are the claimed steps of “training… determining… calculating… determining… receiving… updating… and adjusting targeted content” as indicated supra. If a claim limitation, under its broadest reasonable interpretation, covers managing personal behavior or interactions between people, but for the recitation of generic computer components, then it falls within the “Certain Methods of Organizing Human Activity – Managing Personal Behavior Relationships, Interactions Between People (e.g. social activities, teaching, following rules or instructions)” grouping of abstract ideas.
The Office alleges claim 1 is directed to mathematical concepts due to recitation of training of the persona clustering model. Applicant respectfully traverses… Applicant respectfully submits that claim 1 does not recite any mathematical relationships, formulas, equations, or calculations and therefore is not directed to any mathematical concepts. Although claim 1 recites training a persona clustering machine learning model, this limitation does not expressly recite a mathematical concept. At most, the training of a machine learning model may be based on or involve underlying mathematical concepts; however, as set forth in MPEP 2106.04(a)(2), "[al claim does not recite a mathematical concept (i.e., the claim limitations do not fall within the mathematical concept grouping), if it is only based on or involves a mathematical concept." See also Thales Visionix, Inc. v. United States, 850 F.3d 1343, 1348-49, 121 USPQ2d 1898, 1902-03 (Fed. Cir. 2017). The fact that a machine learning model may employ mathematical operations in its underlying implementation does not, without more, render the claim limitation itself an abstract mathematical concept. Claim 1 does not recite any specific mathematical formula, equation, or calculation; rather, it recites the functional application of a trained persona clustering machine learning model to correlate readiness values to a persona and to determine targeted content. Accordingly, the training limitation of claim 1 is, at most, merely based on mathematical concepts and does not fall within the mathematical concepts grouping under MPEP 2106.04(a)(2). Applicant further respectfully directs the Examiner's attention to the Memorandum from the Deputy Commissioner for Patents dated August 4, 2025, entitled "Reminders on evaluating subject matter eligibility of claims under 35 U.S.C. 101" (the "August 2025 Memorandum"), which provides important reminders pertaining to the USPTO's subject matter eligibility guidance that are directly applicable to the present claims.
Regarding 3, the Examiner respectfully disagrees. The claims recite “training a persona clustering machine learning model.” When given their broadest reasonable interpretation and in light of the disclosure, the training of persona clustering machine learning model represents the creation of mathematical interrelationships between data. Specification at Para. 055 states that examples of machine learning algorithms may include a support vector machine (SVM), a naive Bayes classification, a random forest, a neural network, deep learning, or other supervised learning algorithm or unsupervised learning algorithm for classification and regression. The machine learning algorithms may be trained using one or more training datasets. For example, previously received contextual data may be employed to train various algorithms. Moreover, as described above, these algorithms can be continuously trained/retrained using real-time user data as it is received. In some embodiments, the machine learning algorithm employs regression modelling where relationships between variables are determined and weighted. In some embodiments, the machine learning algorithm employ regression modelling, wherein relationships between predictor variables and dependent variables are determined and weighted. As such, the training of the persona clustering machine learning model represents a mathematical concept that is interpreted to be part of the identified abstract idea, supra. The types of identified abstract ideas are considered together as a single abstract idea for analysis purposes.
In Here, the Office's rejection fails to consider the claim as a whole and instead evaluates the additional elements in isolation, concluding that each amounts to mere instructions to apply the exception using generic computer components. However, when the limitations of claim 1 are considered in their ordered combination, training a persona clustering machine learning model to correlate readiness values to a persona, determining a user's persona through the trained model, calculating key motivators, determining targeted content comprising care preference suggestions based on advance care directives for which similar users have previously opted, receiving interactions, updating the model based on those interactions, and adjusting the targeted content via the model in real time, the claim as a whole defines a specific technological process that integrates any alleged judicial exception into a practical application. The interaction between these limitations, particularly the feedback mechanism in which the persona clustering machine learning model is updated and the targeted content is dynamically adjusted in real time, reflects an improvement to the functioning of the model itself, namely, enabling the model to iteratively refine its persona determinations and correspondingly adjust the targeted content in real time based on ongoing user interactions, rather than relying on a static, pre-trained model, not merely the application of an abstract idea on a generic computer… Under point (1)… Under point (2)… Under point (3)…
Regarding 4, the Examiner respectfully disagrees. The claims do not provide a practical application and the Examiner has analyzed the additional elements in combination. In particular, the additional element of one or more processors, one or more non-transitory computer-readable storage media, a targeted content system and a computer-readable storage device are not exclusively defined by the applicant and are recited at a high-level of generality (i.e., a generic computer component for enabling access to medical information or for performing generic computer functions such that they amount to no more than mere instructions to apply the exception using a generic computer component. Accordingly, even in combination, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea.
The additional element of training a persona clustering machine learning model to output a persona of a user represents a mathematical concept as described in the Specification at Para. 055. This mathematical concept is applied to (“apply it’) the abstract idea. MPEP 2106.04(d)(I) indicates that merely saying “apply it” or equivalent to the abstract idea cannot provide a practical application. The additional elements of a user-interface, a targeted content platform and a user device merely generally links the abstract idea to a particular technological environment or field of use. MPEP 2106.04(d)(I) indicates that generally linking an abstract idea to a particular technological environment or field of use cannot provide a practical application.
Accordingly, even in combination, these additional elements do not integrate the abstract idea into a practical application.
Moreover, the claims do not provide an improvement to the functioning of machine learning. The claims apply a trained machine learning model to the abstract idea. There is no support in the specification for technical improvement to machine learning technology.
Like BASCOM, claim 1 provides a non-conventional and non-generic arrangement of additional elements when viewed in combination. Claim 1 recites an ordered combination of specific limitations that, when considered together, amount to significantly more than any alleged abstract idea: (1) "training a persona clustering machine learning model to correlate at least readiness values to a persona"; (2) "determining a persona of a user based on received user- generated data through the persona clustering machine learning model"; (3) "calculating one or more key motivators to engage in advance care planning according to the persona, wherein the one or more key motivators comprise family independence of the user, financial security of the user, or a combination thereof'; (4) "determining targeted content for the user based on the persona and the one or more key motivators, the targeted content comprising care preference suggestions determined based on care preferences relating to advance care directives for which the users in the set of similar users have previously opted"; (5) "receiving, via the user-interface, interactions of the targeted content at a targeted content platform"; (6) "updating the persona clustering machine learning model based on the interactions"; and (7) "adjusting the targeted content via the persona clustering machine learning model in real time based on the interactions of the targeted content received." These limitations, when considered as an ordered combination, provide an inventive concept that transforms any alleged abstract idea into a patent-eligible application.
Regarding 5, the Examiner respectfully disagrees. Applicant’s invention is unlike that of BASCOM Glob. Internet Servs. v. AT&T Mobility LLC. Evaluating additional elements to determine whether they amount to an inventive concept requires considering them both individually and in combination to ensure that they amount to significantly more than the judicial exception itself. Because this approach considers all claim elements, the Supreme Court has noted that "it is consistent with the general rule that patent claims ‘must be considered as a whole.’ Whether considered separately or as a whole, Applicant’s claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Regarding the double patenting rejection, the Examiner has dropped the rejection.
Rejection under 35 U.S.C. § 103
Regarding the rejection of claims 1-4, 6-17 and 19-20, the Examiner has considered the Applicant’s arguments, but does not find them persuasive. Applicant argues:
The Office cites Mandaviya as allegedly disclosing training a persona clustering machine learning model to correlate at least readiness values to engage in advance care planning to a persona. Applicant respectfully traverses… Abedini likewise fails to cure this deficiency… Dias fails to disclose this deficiency… Malik also fails to cure this deficiency…
Regarding 1, the Examiner respectfully disagrees. The claim limitation requires “training a persona clustering machine learning model to correlate at least readiness values to a persona”, which Mandaviya teaches. Mandaviya at [5:28-30] teaches input to a machine learning algorithm for topic modeling to identify one or more persona segments (interpreted by examiner as means for correlate at least readiness values to a persona) and [14:18-24] teaches data is obtained from multiple user accounts to train persona model (interpreted by examiner as training the persona clustering machine learning model to correlate at least readiness values to a persona). The Examiner suggest clarifying what the readiness values are. Given the broadest reasonable interpretation, the cited references in combination teach the claimed feature.
he Office admits Mandaviya and Abedini do not disclose "calculating one or more key motivators to engage in advance care planning according to the persona." The Office cites Dias as allegedly curing this deficiency, but the Office's reliance on Dias is misplaced and rests on a fundamental mischaracterization of Dias's disclosure…
Regarding 2, the Examiner agrees that Dias does not disclose key motivators to engage in advance care planning. However, Malik teaches this. Malik at [abstract] teaches a computerized process for advanced care planning of a customer user includes, within a computerized processor, operating programming configured to monitoring designation by the customer user of end of life wishes of the customer user and at [0040] teaches designations can be made within the process. For example, a will enabling disposition of personal possessions and funds can be entered in the process. Wishes regarding custody of minor children and pets can be communicated. Links including access information to financial funds, life insurance policies, security boxes held at banks, financial trust details, and other similar information can be securely held, which is interpreted by examiner as calculating one or more key motivators to engage in advance care planning according to the persona wherein the one or more key motivators comprise financial security of the user. Given the broadest reasonable interpretation, the cited references in combination teach the claimed feature.
Applicant respectfully submits the Office has failed to articulate a sufficient motivation to combine the references… The Office's reliance on a motivation drawn exclusively from one reference, without establishing any nexus to the other, is insufficient to support the proposed combination.
Regarding 3, the Examiner respectfully disagrees. In response to applicant’s argument that there is no teaching, suggestion, or motivation to combine the references, the examiner recognizes that obviousness may be established by combining or modifying the teachings of the prior art to produce the claimed invention where there is some teaching, suggestion, or motivation to do so found either in the references themselves or in the knowledge generally available to one of ordinary skill in the art. See In re Fine, 837 F.2d 1071, 5 USPQ2d 1596 (Fed. Cir. 1988), In re Jones, 958 F.2d 347, 21 USPQ2d 1941 (Fed. Cir. 1992), and KSR International Co. v. Teleflex, Inc., 550 U.S. 398, 82 USPQ2d 1385 (2007). In this case Mandaviya discloses content delivery using persona segments for multiple users and is relied upon to teach “training a persona clustering machine learning model to correlate at least readiness values to a persona”. Abedini discloses machine training and search engine for providing specialized cognitive healthcare apparatus and is relied upon to teach “determining a persona of a user based on received user-generated data through a persona clustering machine learning model; receiving, via the user-interface, interactions of the targeted content at a targeted content platform; updating the persona clustering machine learning model based on the interactions; and adjusting the targeted content via the persona clustering machine learning model in real time based on the interactions of the targeted content received.” It would have been obvious for one of the ordinary skill in the art before the effective filling date of the claimed invention to combine the teachings of Mandaviya with that of Abedini, with the motivation of improving the quality life of the patient and bringing measurable success in the treatment process (Abedini at [0002]). Dias discloses patient treatment recommendations based on medical records and exogenous information and is relied upon to teach “determining targeted content for the user based the persona and the one or more key motivators.” It would have been obvious for one of the ordinary skill in the art before the effective filling date of the claimed invention to combine the teachings of Mandaviya and Abedini with that of Dias, with the motivation of preventing or reducing the frequency and magnitude of a catastrophic event such as a hospitalization (Dias at [0002]). Malik discloses advanced care planning process and is relied upon to teach “calculating one or more key motivators to engage in advance care planning according to the persona, wherein the one or more key motivators comprise family independence of the user, financial security of the user, or a combination thereof; the targeted content comprising care preference suggestions determined based on care preferences relating to advance care directives for which the users in the set of similar users have previously opted.” It would have been obvious for one of the ordinary skill in the art before the effective filling date of the claimed invention to combine the teachings of Mandaviya, Abedini and Dias with that of Malik, with the motivation of providing guidance in financial or medical issues (Malik at [0004]).
Moreover, the four-reference combination is the product of impermissible hindsight reconstruction. The Examiner's proposed combination requires assembling four disparate references, an e-commerce content delivery system (Mandaviya), a cognitive healthcare provider matching apparatus (Abedini), a clinical health recommendation system (Dias), and a wellness plan generation system (Malik), drawn from at least three distinct fields of endeavor, none of which is directed to advance care planning. The need to reach across such fundamentally different technological domains and stitch together four unrelated references is itself strong evidence that the Examiner has used Applicant's own disclosure as a roadmap to reconstruct claim 1.
Regarding 4, In response to applicant’s argument that the examiner’s conclusion of obviousness is based upon improper hindsight reasoning, it must be recognized that any judgment on obviousness is in a sense necessarily a reconstruction based upon hindsight reasoning. But so long as it takes into account only knowledge which was within the level of ordinary skill at the time the claimed invention was made, and does not include knowledge gleaned only from the applicant’s disclosure, such a reconstruction is proper. See In re McLaughlin, 443 F.2d 1392, 170 USPQ 209 (CCPA 1971).
Conclusion
The prior art made of record though not relied upon in the present basis of rejection are noted in the attached PTO 892 and include:
Ryan (US 2015/0363569) discloses customizing personalized patient care plans to facilitate cross-continuum, multi-role care planning. Skocic (US 10296716 B1) discloses system if and method for collecting and transmitting advance care planning and directives documentation.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to LIZA TONY KANAAN whose telephone number is (571)272-4664. The examiner can normally be reached on Mon-Thu 9:00am-6:00pm ET.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Robert Morgan can be reached on 571-272-6773. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/LIZA TONY KANAAN/Examiner, Art Unit 3683
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