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
In the amendment dated 09/29/2025, the following occurred: Claims 1, 6, 9, 13 and 14 were amended.
Claims 1-20 are currently pending.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 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. The claim recites a method, system and storage media for delivering targeted content with care preference suggestions.
Regarding claims 1, 13 and 14, the limitation of (claim 1 being representative) providing targeted content comprising suggestions for care preference: determining a persona of a user based on received user-generated data through the persona clustering models; 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; and 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 drafted, is a process that, under the broadest reasonable interpretation, covers a method organizing human but for the recitation of generic computer components. That is other than reciting (in claim 1) one or more processors, (in claim 13) one or more non-transitory computer-readable storage media and one or more processors and (in claim 14) a targeted content system, one or more processors, and a computer-readable storage device, the claimed invention amounts to managing personal behavior or interaction between people (i.e., rules or instructions). 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 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.
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. This additional element is recited at a high level of generality (i.e. a general means to output/receive/transmit data) and amount to extra solution activity. Accordingly, even in combination, these additional elements do not integrate the abstract idea into a practical application.
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 element of a user-interface was considered extra-solution activity. This has been re-evaluated under “significantly more” analysis and determined to be well-understood, routine and conventional activity in the field. MPEP 2016.05(d)(II) indicates that receiving and/or transmitting data over a network has been held by the courts to be well-understood, routine and conventional activity (citing Symantec, TLI Communications, OIP Techs., and buySAFE). Well-understood, routine and conventional activity cannot provide an inventive concept (“significantly more”). Therefore when considering the additional elements alone, and in combination, there is no inventive concept in the claim, and thus 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 2-12 and 15-20 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, inputting and calculating data. Dependent claims 4 and 17 further define the persona clustering model. Dependent claims 5 and 18 further define receiving interactions and adjusting the targeted content. Dependent claim 6 further defines the targeted content. Dependent claims 7 and 19 further define step after providing the targeted content. 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 data. Dependent claims 10 and 19 further define 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. Dependent claims 12 and 20 further define EMR data.
Double Patenting
The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on nonstatutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP § 2146 et seq. for applications not subject to examination under the first inventor to file provisions of the AIA . A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
The filing of a terminal disclaimer by itself is not a complete reply to a nonstatutory double patenting (NSDP) rejection. A complete reply requires that the terminal disclaimer be accompanied by a reply requesting reconsideration of the prior Office action. Even where the NSDP rejection is provisional the reply must be complete. See MPEP § 804, subsection 1.B.1. Fora reply toa non-final Office action, see 37 CFR1.111(a). For a reply to final Office action, see 37 CFR 1.113(c). A request for reconsideration while not provided for in 37 CFR 1.113(c) may be filed after final for consideration. See MPEP §§ 706.07(e) and 714.13.
The USPTO Internet website contains terminal disclaimer forms which may be used. Please visit www.uspto.gov/patent/patents-forms. The filing date of the application in which the form is filed determines what form (e.g., PTO/SB/25, PTO/SB/26, PTO/AIA /25, or PTO/AIA /26) should be used. A web-based eTerminal Disclaimer may be filled out completely online using web-screens. An eTerminal Disclaimer that meets all requirements is auto-processed and approved immediately upon submission. For more information about eTerminal Disclaimers, refer to www.uspto.gov/patents/apply/applying- online/eterminal-disclaimer.
Claims 1-20 are provisionally rejected on the ground of obviousness-type nonstatutory double patenting as being unpatentable over claims 1-20 of copending Application No. 17/907,396 (reference application). The claims of the present application contains several if not all features present in the claims of the ‘396 application. Any features not taught by the ‘396 application would have been obvious in view of the cited prior art of record (see citations, infra). And a person having skill in the art would be motivated to combine the prior art of record (see, infra) with the claimed features of the ‘396 to arrive at the presently claimed invention with the motivation of determining a patient-specific dosing regimen for a patient, using a computerized dosing regimen recommendation system.
This is a provisional nonstatutory double patenting rejection.
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 1-20 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, 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);
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 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, however Dias discloses:
calculating one or more key motivators to engage in advance care planning according to the persona (Dias at [0143] teaches using a clustering algorithm (interpreted by examiner as the persona clustering machine learning model of Mandaviya and Abedini). [0131] teaches The lifestyle information sources 424, 425 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, presented either physically and then entered through a data entry process or presented electronically and gathered automatically, directed to the patient's lifestyle, preferences, and the like, [0127] teaches personalized patient care plan for a patient entry is generated from a patient cohort database storing information for various patient having similar characteristics (interpreted by examiner as the persona) [0142]-[0413] teach creation/update engine applied to historical personalized care plan information for other similar patients, engine may be any known mechanism, including a clustering algorithm [0124] teaches content users input questions to cognitive system which implements the QA pipeline. The QA pipeline then answers the input questions using the content in the corpus of data by evaluating documents, sections of documents, portions of data in the corpus, or the like (interpreted by examiner as calculating one or more key motivators to engage in advance care planning according to the persona)), determining targeted content for the user based the persona and the one or more key motivators (Dias at [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)),
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 system and methods of Mandaviya and Abedini to incorporate calculating one or more key motivators and determining targeted content 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, however Malik discloses:
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 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 system and methods of Mandaviya, Abedini and Dias to incorporate the key motivators comprise financial security of the user and care preferences relating to advance care directives 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, 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)).
REGARDING CLAIM 3
Mandaviya, Abedini, Dias and Malik disclose the limitation of claim 1.
Mandaviya, Abedini and Malik do not explicitly disclose, 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)).
REGARDING CLAIM 4
Mandaviya, Abedini, Dias and Malik disclose the limitation of claim 1.
Mandaviya, Dias and Malik do not explicitly disclose, 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)).
REGARDING CLAIM 5
Mandaviya, Abedini, Dias and Malik disclose the limitation of claim 1.
Mandaviya, Dias and Malik do not explicitly disclose, however Abedini discloses:
The method of claim 1, further comprising: receiving, via the user-interface, interactions of the targeted content at a targeted content platform; and adjusting the targeted content via the targeted content platform in real time based on the interactions of the targeted content received (Abedini at [0058] teaches interaction and feedback from patients updates the profiles of the individual (interpreted by examiner as adjusting the targeted content via the targeted content platform in real time based on the interactions of the targeted content)).
REGARDING CLAIM 6
Mandaviya, Abedini, Dias and Malik disclose the limitation of claim 1.
Mandaviya, Abedini and Dias do not explicitly disclose, 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).
REGARDING CLAIM 7
Mandaviya, Abedini, Dias and Malik disclose the limitation of claim 6.
Mandaviya, Abedini and Dias do not explicitly disclose, 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)).
REGARDING CLAIM 8
Mandaviya, Abedini, Dias and Malik disclose the limitation of claim 7.
Mandaviya, Abedini and Dias do not explicitly disclose, 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)).
REGARDING CLAIM 9
Mandaviya, Abedini, Dias and Malik disclose the limitation of claim 8.
Mandaviya, Abedini and Dias do not explicitly disclose, 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)).
REGARDING CLAIM 10
Mandaviya, Abedini, Dias and Malik disclose the limitation of claim 8.
Mandaviya, Abedini and Dias do not explicitly disclose, 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)).
REGARDING CLAIM 11
Mandaviya, Abedini, Dias and Malik disclose the limitation of claim 1.
Mandaviya, Dias and Malik do not explicitly disclose, 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)).
REGARDING CLAIM 12
Mandaviya, Abedini, Dias and Malik disclose the limitation of claim 1.
Mandaviya, Dias and Malik do not explicitly disclose, 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)).
REGARDING CLAIMS 13 and 14
Claims 13 and 14 are analogous to Claim 1 thus Claims 13 and 14 are similarly analyzed and rejected in a manner consistent with the rejection of Claim 1.
REGARDING CLAIM 15, 16, 17 and 20
Claims 15, 16, 17 and 20 are analogous to Claims 2-4 and 11-12 thus Claims 15, 16, 17 and 20 are similarly analyzed and rejected in a manner consistent with the rejection of Claims 2-4 and 11-12.
REGARDING CLAIMS 18 and 19
Claims 18 and 19 are analogous to Claims 5-10 thus Claims 18 and 19 are similarly analyzed and rejected in a manner consistent with the rejection of Claims 5-10.
Response to Arguments
Rejection under 35 U.S.C. § 101
Regarding the rejection of claims 1-20, the Examiner has considered the Applicant’s arguments, but does not find them persuasive. Applicant argues:
MPEP 2106.04(a)(2) identifies three sub-groupings of "methods of organizing human activity": (1) fundamental economic principles or practices (including hedging, insurance, mitigating risk); (2) commercial or legal interactions (including agreements in the form of contracts; legal obligations; advertising, marketing or sales activities or behaviors; business relations); and (3) managing personal behavior or relationships or interactions between people (including social activities, teaching, following rules or instructions). Applicant respectfully submits that claim 1 does not fall within any of these sub-groupings. Claim 1 is not directed to fundamental economic principles, commercial or legal interactions, or managing personal behavior or relationships. Instead, claim 1 is directed to a computer- implemented method for generating a specific training dataset and training a persona clustering machine learning model with the specific training dataset.
Regarding 1, the Examiner respectfully disagrees. The claims receive, determine, calculate and provide information. Stripped of all additional elements, the claims encompass a user manually acquiring user-generated data, applying a model to determine a persona of the user, applying mathematical formulas and determining targeted content based on the persona. These limitations, as drafted, given the broadest reasonable interpretation, but for the recitation of generic computer components, encompass managing interactions between people, which is a subgrouping of Certain Methods of Organizing Human Activity.
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 would, arguendo, are merely based on a mathematical concept rather than expressly reciting a mathematical concept.
Regarding 2, the Examiner respectfully disagrees. 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 (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.
In Example 39, it was found that claim 1 does not recite any mathematical relationships, formulas, or calculations. In particular, while some of the limitations may be based on mathematical concepts, the mathematical concepts are not recited in the claims. Additionally, claim 1 was not found to recite any method of organizing human activity. Claim 1 has been amended to recite "training a persona clustering machine learning model to correlate at least readiness values to a persona ..." Like claim 1 of Example 39, current claim 1 recites a specific training limitation to train a persona clustering machine learning model with specific training data, which provides the same type of specific training methodology found patentable in Example 39.
Regarding 3, the Examiner respectfully disagrees. Applicant’s specification recites the training of a persona clustering machine learning model to output a persona of a user to include mathematical concepts, see above para. [055]. The mathematical relationships do not need to be recite in the claim. Moreover, Applicants claims are unlike that of Example 39, as Applicants claims merely apply the use of a trained machine learning model to the abstract idea. The training is not done in a way that is non-conventional.
Applicant respectfully submits that claim 1 as amended integrates any alleged judicial exception into a practical application by providing specific technological improvements to machine learning models themselves. As noted in paragraph [0088] of the published specification, "In some embodiments, machine learning algorithms are employed to build a model to determine quantifiable measures of dyadic ties between the individuals." Claim 1 recites specific technical limitations that provide for an improved training method allowing for enhanced machine learning functionality using custom-made similarity based datasets of user-generated data. For instance, the limitation of "training a persona clustering machine learning model to correlate at least readiness values to a persona ..." provides a specific training step allowing for a custom built machine learning model to provide enhanced clustering capabilities.
Regarding 4, the Examiner respectfully disagrees. The claims do not integrated the abstract idea into a practical application and do not provide technological improvements to machine learning models. The claims apply the use of a trained machine learning model to the abstract idea. There is no support in the specification for technical improvement to the way machine learning is trained.
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) "obtaining an initial dataset of a plurality of user-generated data"; (2) "generating a training dataset comprising sets of similar user-generated data through modifying the initial dataset based on a similarity of users within the initial dataset"; (3) "training a persona clustering machine learning model with the training dataset to input user-generated data and output one or more personas based on the user-generated data"; (4) "determining a persona of a user based on received user-generated data through the persona clustering machine learning model, wherein the persona clustering machine learning model is trained by correlating user-generated data comprising readiness to engage in advance care planning to one or more personas"; (5) "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"; and (6) "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."
Regarding 5, the Examiner respectfully disagrees. The additional element in the claim 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”). Also, the additional element of using the trained persona clustering machine learning model 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.
Moreover, 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.
Rejection under 35 U.S.C. § 103
Regarding the rejection of claims 1-20, the Examiner has considered the Applicant’s arguments, but does not find them persuasive. Applicant argues:
As noted above, during the Examiner Interview of August 27, 2025, Examiner Kanaan agreed that Dias does not disclose, teach, or suggest "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' as recited in claim 1. Applicant respectfully submits that this limitation is absent from all of Abedini, Dias, Mandaviya, and Malik, alone or in combination... Claim 1 has been amended to recite "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." None of Abedini, Dias, Mandaviya, nor Malik disclose, teach, or suggest this limitation.
Regarding 1, the Examiner respectfully disagrees and has updated the rejection. Please refer to the new rejection under 35 U.S.C. § 103. Given the broadest reasonable interpretation, the cited references in combination teach the claimed feature.
Conclusion
Applicant’s amendment necessitated the new grounds of rejection presented in this Office action. THIS ACTION IS MADE FINAL. See MPEP §706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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:
Tritch (US 2003/0040939) teaches method of storing and retrieving advanced medical directives.
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/LIZA TONY KANAAN/Examiner, Art Unit 3683
/ROBERT W MORGAN/Supervisory Patent Examiner, Art Unit 3683