DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 06 February 2026 has been entered.
Status of Claims
This action is in reply to the claims filed on 07 January 2026. Claims 1-6 and 11 were amended. Claims 1-20 are currently pending and have been examined.
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 USC § 101
Step 1: Is the claim to a process, machine, manufacture, or composition of matter?
Claims 1-20 fall within one or more statutory categories. Claims 1-10 fall within the category of a process. Claims 11-20 fall within the category of a machine.
Step 2A Prong One: Does the claim recite an abstract idea, law of nature, or natural phenomenon?
Claims 1-20 recite an abstract idea. Representative claim 1 recites:
receiving, … an input including one or more parameters to define generation of digital therapeutic content, the one or more parameters identifying at least one domain of a plurality of domains with which to check the digital therapeutic content;
… identifying … a content item… using data associated with the one or more parameters;
… determining … the content item satisfies a compliance threshold of the at least one domain; and
causing … responsive to determining the content item satisfies the compliance threshold of the at least one domain, presentation of the content item….
Therefore, the claim as a whole is directed to “generating therapeutic content,” which is an abstract idea because it is a method of organizing human activity. “Generating therapeutic content” is considered to be a method of organizing human activity because it is an example of managing personal behavior or relationships or interactions between people (including social activities, teaching, and following rules or instructions). The broadest reasonable interpretation of the claims includes the interaction between a healthcare provider and patient. Further, the claims are directed to a mental process because they are an example of concepts that can be performed in the human mind (including an observation, evaluation, judgment, opinion) with the aid of pen and paper.
Step 2A Prong Two: Does the claim recite additional elements that integrate the judicial exception into a practical application?
This judicial exception is not integrated into a practical application. In particular, claim 1 recites the following additional element(s):
one or more processors;
a user interface;
a generative stage [of] a machine learning (ML) architecture;
a validation stage … using the ML architecture;
providing, by the one or more processors, feedback data generated using a response to the presentation of the content item via the user interface to update at least one or more ML models in the ML architecture;
wherein the ML architecture comprises at least one ML model configured with capabilities of at least one of a generative transformer model or a validation model.
The additional elements individually or in combination do not integrate the exception into a practical application. These additional elements, including the broadly recited machine learning models, merely recite the words ‘‘apply it’’ (or an equivalent) with the judicial exception, or merely include instructions to implement an abstract idea on a computer, or merely use a computer as a tool to perform an abstract idea (see MPEP 2106.05(f)). Accordingly, 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. Claim 1 is directed to an abstract idea.
Step 2B: Does the claim recite additional elements that amount to significantly more than the judicial exception?
Claim 1 does not include additional elements, considered individually or in combination, that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements, individually and in combination, merely recite the words ‘‘apply it’’ (or an equivalent) with the judicial exception, or merely include instructions to implement an abstract idea on a computer, or merely use a computer as a tool to perform an abstract idea (see MPEP 2106.05(f)). Accordingly, claim 1 is ineligible.
Dependent claim 2 recites the method of claim 1, wherein:
applying, by the one or more processors, the at least one ML model to at least one content item to determine at least one score for at least one content item with respect to the at least one domain; and
causing, by the one or more processors, via the user interface, presentation of an indication that the at least one content item is not compliant, responsive to the at least one score not satisfying the compliance threshold.
The additional elements present in this claim merely recites the words ‘‘apply it’’ (or an equivalent) with the judicial exception, or merely includes instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea (see MPEP 2106.05(f)). These types of additional elements are not enough to integrate the abstract idea into a practical application, nor do they amount to significantly more than the judicial exception. Accordingly, claim 2 is ineligible.
Dependent claim 3 recites the method of claim 1, wherein:
determining, by the one or more processors, that no content item corresponding to the one or more parameters was previously generated by the at least one ML model; and
providing, by the one or more processors, the data associated with the one or more parameters to the at least one ML model to generate the content item.
The additional elements present in this claim merely recites the words ‘‘apply it’’ (or an equivalent) with the judicial exception, or merely includes instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea (see MPEP 2106.05(f)). These types of additional elements are not enough to integrate the abstract idea into a practical application, nor do they amount to significantly more than the judicial exception. Accordingly, claim 3 is ineligible.
Dependent claim 4 recites the method of claim 1, wherein:
applying, by the one or more processors, the at least one ML model to the one or more parameters of the input to determine at least one score corresponding to the input with respect to the at least one domain;
wherein identifying the content item further comprises providing the data associated with the one or more parameters to the at least one ML model, responsive to the at least one score satisfying a threshold corresponding to the input.
The additional elements present in this claim merely recites the words ‘‘apply it’’ (or an equivalent) with the judicial exception, or merely includes instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea (see MPEP 2106.05(f)). These types of additional elements are not enough to integrate the abstract idea into a practical application, nor do they amount to significantly more than the judicial exception. Accordingly, claim 4 is ineligible.
Dependent claim 5 recites the method of claim 1, wherein:
receiving, by the one or more processors, a response identifying a portion of the content item to be modified; and
providing, by the one or more processors, feedback data generated using the response to update the at least one ML model.
The additional elements present in this claim merely recites the words ‘‘apply it’’ (or an equivalent) with the judicial exception, or merely includes instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea (see MPEP 2106.05(f)). These types of additional elements are not enough to integrate the abstract idea into a practical application, nor do they amount to significantly more than the judicial exception. Accordingly, claim 5 is ineligible.
Dependent claim 6 recites the method of claim 1, wherein:
identifying the content item further comprises identifying a plurality of content items using the data associated with the one or more parameters, each of the plurality of content items generated by at least one machine learning model;
wherein applying the at least one ML model further comprises applying the validation model to each respective content item of the plurality of content items to determine a respective score for the respective content item with respect to the at least one domain.
The additional elements present in this claim merely recites the words ‘‘apply it’’ (or an equivalent) with the judicial exception, or merely includes instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea (see MPEP 2106.05(f)). These types of additional elements are not enough to integrate the abstract idea into a practical application, nor do they amount to significantly more than the judicial exception. Accordingly, claim 6 is ineligible.
Dependent claim 7 recites the method of claim 1, wherein:
selecting the content item further comprises ranking a plurality of content items in accordance with one or more criteria, each content item of the plurality of content items generated by a respective machine learning model of a plurality of machine learning models using the data associated with the one or more parameters.
The additional elements present in this claim merely recites the words ‘‘apply it’’ (or an equivalent) with the judicial exception, or merely includes instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea (see MPEP 2106.05(f)). These types of additional elements are not enough to integrate the abstract idea into a practical application, nor do they amount to significantly more than the judicial exception. Accordingly, claim 7 is ineligible.
Dependent claim 8 recites the method of claim 1, wherein:
causing the presentation further comprises causing the presentation of information identifying a score for the content item with respect to the at least one domain.
This merely further limits the abstract idea of claim 1 discussed above and does not provide further additional elements. Therefore, claim 8 is considered to be ineligible.
Dependent claim 9 recites the method of claim 1, wherein:
receiving the input further comprises receiving the input including the one or more parameters comprising at least one of (i) a domain identifier corresponding to the at least one domain or (ii) an audience identifier corresponding to an audience for which the digital therapeutic content is to be generated.
This merely further limits the abstract idea of claim 1 discussed above and does not provide further additional elements. Therefore, claim 9 is considered to be ineligible.
Dependent claim 10 recites the method of claim 1, wherein:
each content item of a plurality of content items comprises at least one of textual content or visual content to be provided to a device for presentation in a session to address a condition of a user, wherein the user is administered with a medication to address the condition at least in a partial concurrence with the session;
wherein the plurality of domains comprises at least one of a science domain, a regulatory domain, an audience domain, or a product domain.
The additional elements present in this claim merely recites the words ‘‘apply it’’ (or an equivalent) with the judicial exception, or merely includes instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea (see MPEP 2106.05(f)). These types of additional elements are not enough to integrate the abstract idea into a practical application, nor do they amount to significantly more than the judicial exception. Accordingly, claim 10 is ineligible.
Claims 11-20 are parallel in nature to claims 1-10. Accordingly claims 11-20 are rejected as being directed towards ineligible subject matter based upon the same analysis above.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
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 Chin et al. (U.S. 2021/0057057), hereinafter “Chin,” in view of Tran (U.S. 2022/0237368), hereinafter “Tran.”
Regarding Claim 1, Chin discloses method, comprising:
receiving, by one or more processors, via a user interface (See Chin [0035] the system can include the use of processors and memory. [0050] system can employ the use of a user interface.), an input including one or more parameters to define generation of digital therapeutic content (See Chin [0050]-[0051] the system can collect information about the patient to ascertain how to help the patient. [0052] the system can use models to determine overall scores for questions and use the scores as criteria for selecting questions.), the one or more parameters identifying at least one domain of a plurality of domains with which to check the digital therapeutic content (See Chin [0046] multiple domains, programs, diseases, symptoms, subject matter expert knowledge models for the various components of the disclosure, which includes the dialog component. [0128] system can include models based on government regulations and treatment protocols. See also [0139].);
… identifying, by the one or more processors, a content item generated … using data associated with the one or more parameters (See Chin [0053] the system can identify a list of questions to be presented to the patient. This list can be modified or reduced. [0032] DTx can generate insights personalized to a specific patient. [0097] the DTx conversation can include AI generated questions.);
in a validation stage, determining, by the one or more processors, using the ML architecture the content item satisfies a compliance threshold of the at least one domain (See Chin [0046] the system can use multiple domains, programs, diseases, symptoms, subject matter expert knowledge models for the various components of the disclosure. [0052] the system can use models to determine overall scores for questions and use the scores as criteria for selecting questions for display to the user. [0070] the system can present the intensity mappings which include different colors such as red, green, and amber. These intensity colors are understood to be indications of how well the questions match the patient symptoms and relevant domains based on [0067] disclosing the intensity values are matched to thresholds and [0046] disclosing that the models can be based on domains. Therefore, this is a measure of compliance to those domains.);
causing, by the one or more processors, responsive to determining the content item satisfies the compliance threshold of the at least one domain (See Chin [0052] the system can use models to determine overall scores for questions and use the scores as criteria for selecting questions. [0070] the scores can be given unique intensity mappings. [0067] intensity values can be matched to thresholds. [0046] the system can use multiple domains, programs, diseases, symptoms, subject matter expert knowledge models for the various components of the disclosure, which includes the dialog component.), presentation of the content item via the user interface (See Chin [0053] the system can identify a list of questions to be presented to the patient. This list can be modified or reduced.); and
providing, by the one or more processors, feedback data generated using a response to the presentation of the content item via the user interface to update at least one or more ML models in the ML architecture (See Chin [0055] Developers can create and update DTx knowledge models used in the digital therapeutics or DTx. Developers can provide and update questions, rules and actions as to the DTx knowledge models. [0066] patient profiles can be updated based on outcomes and used for further artificial intelligence (AI) analytics and searching.).
Chin does not disclose:
[the content item is identified] in a generative stage, by a machine learning (ML) architecture;
wherein the ML architecture comprises at least one ML model configured with capabilities of at least one of a generative transformer model or a validation model.
Tran teaches:
the content item is generated by a generative transformer model (See Tran [0283] the system can use chatbots that provide assistance to patients by generating content. [0284] the chatbot can use generative, transformer based models (including GPT) and technology. These are a type of machine learning model. See also [0291].);
wherein the ML architecture comprises at least one ML model configured with capabilities of at least one of a generative transformer model or a validation model (See Tran [0283] the system can use chatbots that provide assistance to patients by generating content. [0284] the chatbot can use generative, transformer based models (including GPT) and technology. [0289] the chatbots (generative models) are periodically updated with new training data. See also [0291].) .
The system of Tran is applicable to the disclosure of Chin as they both share characteristics and capabilities, namely, they are directed to generating digital content for patients. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Chin to include generative models as taught by Tran. One of ordinary skill in the art before the effective filing date of the claimed invention would have been motivated to modify Chin in order to address patient needs realistically and with empathy through engaging use of personality, knowledge, and display of empathy (see Tran [0327]).
Regarding claim 2, Chin in view of Tran discloses the method of claim 1 as discussed above. Chin further discloses a method, comprising:
applying, by the one or more processors, the at least one ML model to at least one content item to determine at least one score for at least one content item with respect to the at least one domain (See Chin [0046] multiple domains, programs, diseases, symptoms, subject matter expert knowledge models for the various components of the disclosure. [0052] the system can use models to determine overall scores for questions and use the scores as criteria for selecting questions.); and
causing, by the one or more processors, via the user interface, presentation of an indication that the at least one content item is not compliant (See Chin [0070] the system can present the intensity mappings which include different colors such as red, green, and amber. These intensity colors are understood to be indications of how well the questions match the patient symptoms and relevant domains based on [0067] disclosing the intensity values are matched to thresholds and [0046] disclosing that the models can be based on domains. Therefore, this is a measure of compliance to those domains.), responsive to the at least one score not satisfying the compliance threshold (See Chin [0052] the system can use models to determine overall scores for questions and use the scores as criteria for selecting questions. [0070] the scores can be given unique intensity mappings. [0067] intensity values can be matched to thresholds.).
Regarding claim 3, Chin in view of Tran discloses the method of claim 1 as discussed above. Chin further discloses a method, comprising:
determining, by the one or more processors, that no content item corresponding to the one or more parameters was previously generated by the at least one ML model … (See Chin [0097] the DTx conversation can include AI generated questions. This means the system is generating content instead of pulling content from the database.).
Chin does not disclose:
providing, by the one or more processors, the data associated with the one or more parameters to the at least one ML model to generate the content item.
Tran teaches:
providing, by the one or more processors, the data associated with the one or more parameters to the at least one ML model to generate the content item (See Tran [0283] the system can use chatbots that provide assistance to patients by generating content. [0284] the chatbot can use generative, transformer based models (including GPT) and technology. These are a type of machine learning model. See also [0291].).
The system of Tran is applicable to the disclosure of Chin as they both share characteristics and capabilities, namely, they are directed to generating digital content for patients. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Chin to include generative models as taught by Tran. One of ordinary skill in the art before the effective filing date of the claimed invention would have been motivated to modify Chin in order to address patient needs realistically and with empathy through engaging use of personality, knowledge, and display of empathy (see Tran [0327]).
Regarding claim 4, Chin in view of Tran discloses the method of claim 1 as discussed above. Chin further discloses a method, comprising:
applying, by the one or more processors, the at least one ML model to the one or more parameters of the input to determine at least one score corresponding to the input with respect to the at least one domain (See Chin [0052] the system can determine symptom scores (i.e., “score corresponding to the input”) as well as scores for the questions (i.e., “score for the content item”). See also [0062].); and
wherein identifying the content item further comprises providing the data associated with the one or more parameters to the at least one ML model, responsive to the at least one score satisfying a threshold corresponding to the input (See Chin [0052] the system can use models to determine overall scores for questions and use the scores as criteria for selecting questions. [0070] the scores can be given unique intensity mappings. [0067] intensity values can be matched to thresholds.).
Regarding claim 5, Chin in view of Tran discloses the method of claim 1 as discussed above. Chin does not further disclose a method, comprising:
receiving, by the one or more processors, a response identifying a portion of the content item to be modified; and
providing, by the one or more processors, feedback data generated using the response to update the at least one machine learning mode.
Tran teaches:
receiving, by the one or more processors, a response identifying a portion of the content item to be modified (See Tran [0223] the system first produces a draft response (to avoid dull and repetitive responses) by appending data based on the dialogue history to the input of the chat generator. This is a modified input sequence that modifies the output.); and
providing, by the one or more processors, feedback data generated using the response (See Tran [0223] the system first produces a draft response (to avoid dull and repetitive responses) by appending data based on the dialogue history to the input of the chat generator. This is a modified input sequence that modifies the output.) to update the at least one ML model (See Tran [0283] the system can use chatbots that provide assistance to patients by generating content. [0284] the chatbot can use generative, transformer based models (including GPT) and technology. [0289] the chatbots (generative models) are periodically updated with new training data. See also [0291].).
The system of Tran is applicable to the disclosure of Chin as they both share characteristics and capabilities, namely, they are directed to generating digital content for patients. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Chin to include generative models as taught by Tran. One of ordinary skill in the art before the effective filing date of the claimed invention would have been motivated to modify Chin in order to address patient needs realistically and with empathy through engaging use of personality, knowledge, and display of empathy (see Tran [0327]).
Regarding claim 6, Chin in view of Tran discloses the method of claim 1 as discussed above. Chin further discloses a method, wherein:
identifying the content item further comprises identifying a plurality of content items using the data associated with the one or more parameters (See Chin [0046] multiple domains, programs, diseases, symptoms, subject matter expert knowledge models for the various components of the disclosure. [0052] the system can use models to determine overall scores for questions and use the scores as criteria for selecting questions.);
wherein applying the at least one ML model further comprises applying the validation model to each respective content item of the plurality of content items to determine a respective score for the respective content item with respect to the at least one domain (See Chin [0053] the system can identify a list of questions to be presented to the patient. This list can be modified or reduced. [0032] DTx can generate insights personalized to a specific patient. [0097] the DTx conversation can include AI generated questions.).
Chin does not disclose:
each of the plurality of content items generated by at least one machine learning model.
Tran teaches:
each of the plurality of content items generated by at least one machine learning model (See Tran [0283] the system can use chatbots that provide assistance to patients by generating content. [0284] the chatbot can use generative, transformer based models (including GPT) and technology. See also [0291].).
The system of Tran is applicable to the disclosure of Chin as they both share characteristics and capabilities, namely, they are directed to generating digital content for patients. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Chin to include generative models as taught by Tran. One of ordinary skill in the art before the effective filing date of the claimed invention would have been motivated to modify Chin in order to address patient needs realistically and with empathy through engaging use of personality, knowledge, and display of empathy (see Tran [0327]).
Regarding claim 7, Chin in view of Tran discloses the method of claim 1 as discussed above. Chin further discloses a method, wherein:
selecting the content item further comprises ranking a plurality of content items in accordance with one or more criteria (See Chin [0054] the questions can be sorted in priority order (i.e., ranked). This priority can be based on previous asked questions. [0098] patient profile can include question preferences.).
Chin does not disclose:
each content item of the plurality of content items generated by a respective machine learning model of a plurality of machine learning models using the data associated with the one or more parameters.
Tran teaches:
each content item of the plurality of content items generated by a respective machine learning model of a plurality of machine learning models using the data associated with the one or more parameters (See Tran [0283] the system can use chatbots that provide assistance to patients by generating content. [0284] the chatbot can use generative, transformer based models (including GPT) and technology. These are a type of machine learning model. See also [0291].).
The system of Tran is applicable to the disclosure of Chin as they both share characteristics and capabilities, namely, they are directed to generating digital content for patients. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Chin to include generative models as taught by Tran. One of ordinary skill in the art before the effective filing date of the claimed invention would have been motivated to modify Chin in order to address patient needs realistically and with empathy through engaging use of personality, knowledge, and display of empathy (see Tran [0327]).
Regarding claim 8, Chin in view of Tran discloses the method of claim 1 as discussed above. Chin further discloses a method, wherein:
causing the presentation further comprises causing the presentation of information identifying a score for the content item with respect to the at least one domain (See Chin [0070] the system can present the intensity mappings which include different colors such as red, green, and amber. The use of these intensity colors meets the broadest reasonable interpretation of “information identifying the score.” See also Fig. 3D and [0074]-[0075] the system can present the intensity values in a visual format.).
Regarding claim 9, Chin in view of Tran discloses the method of claim 1 as discussed above. Chin further discloses a method, wherein:
receiving the input further comprises receiving the input including the one or more parameters comprising at least one of (i) a domain identifier corresponding to the at least one domain or (ii) an audience identifier corresponding to an audience for which the digital therapeutic content is to be generated (See Chin [0050]-[0051] the system can collect information about the patient to ascertain how to help the patient. This meets the broadest reasonable interpretation of “an audience identifier.”).
Regarding claim 10, Chin in view of Tran discloses the method of claim 1 as discussed above. Chin further discloses a method, wherein:
each content item of a plurality of content items comprises at least one of textual content or visual content to be provided to a device for presentation in a session to address a condition of a user (See Chin [0053] the system can identify a list of questions to be presented to the patient (“i.e., “textual content”). This list can be modified or reduced. The system can be used to address primary symptoms and adverse events of the patient (i.e., “a condition of the user”).), wherein the user is administered with a medication to address the condition at least in a partial concurrence with the session (See Chin [0037] the digital therapeutics can be used together with medications.);
wherein the plurality of domains comprises at least one of a science domain, a regulatory domain, an audience domain, or a product domain (See Chin [0046] multiple domains, programs, diseases, symptoms, subject matter expert knowledge models for the various components of the disclosure. [0128] system can include models based on government regulations and treatment protocols. See also [0139].).
Regarding claim 11-20, Chin in view of Tran discloses the method of claims 1-10 as discussed above. Claims 11-20 recited a system that performs a method substantially similar to the method of claims 1-10. Accordingly, claims 11-20 are rejected based on the same analysis.
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 I.B.1. For a reply to a non-final Office action, see 37 CFR 1.111(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 actual 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.
Claim 1, 3-11, and 13-20 are rejected on the ground of nonstatutory double patenting as being unpatentable over claim 1, 3-11, and 13-20 of U.S. Patent No. 12,106,860. Although the claims at issue are not identical, they are not patentably distinct from each other. The present claims appear to be in a broader form than the claims in the ‘860 patent. However, they are still directed to the same subjected matter without any additional or different subject matter to distinguish them.
Claim 2 and 12 are rejected on the ground of nonstatutory double patenting as being unpatentable over claim 1 and 11 of U.S. Patent No. 12,106,860 in view of Chin et al. (U.S. 2021/0057057), hereinafter “Chin.”
Regarding claim 2, claim 1 of the ‘860 patent discloses the method of claim 1 as discussed above. However, claim 1 of the ‘860 patent does not further disclose a method, comprising:
applying, by the one or more processors, the validation model to at least one content item to determine at least one score for at least one content item with respect to the at least one domain; and
causing, by the one or more processors, via the user interface, presentation of an indication that the at least one content item is not compliant, responsive to the at least one score not satisfying the threshold.
Chin teaches:
applying, by the one or more processors, the at least one machine learning model to at least one content item to determine at least one score for at least one content item with respect to the at least one domain (See Chin [0046] multiple domains, programs, diseases, symptoms, subject matter expert knowledge models for the various components of the disclosure. [0052] the system can use models to determine overall scores for questions and use the scores as criteria for selecting questions.); and
causing, by the one or more processors, via the user interface, presentation of an indication that the at least one content item is not compliant (See Chin [0070] the system can present the intensity mappings which include different colors such as red, green, and amber. These intensity colors are understood to be indications of how well the questions match the patient symptoms and relevant domains based on [0067] disclosing the intensity values are matched to thresholds and [0046] disclosing that the models can be based on domains. Therefore, this is a measure of compliance to those domains.), responsive to the at least one score not satisfying the compliance threshold (See Chin [0052] the system can use models to determine overall scores for questions and use the scores as criteria for selecting questions. [0070] the scores can be given unique intensity mappings. [0067] intensity values can be matched to thresholds.).
The system of Chin is applicable to the disclosure of the ‘860 patent as they both share characteristics and capabilities, namely, they are directed to generating digital content for patients. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the ‘860 patent to include displays of the data as taught by Chin. One of ordinary skill in the art before the effective filing date of the claimed invention would have been motivated to modify the ‘860 patent in order to assure that better good is done than harm, and to accurately understand the therapy and effects of such therapy for each individual patient (see Chin [0005]).
Regarding claim 12, claim 11 of the ‘860 patent in view of Chin discloses the method of claim 2 as discussed above. Claim 12 recites a system that performs a method substantially similar to the method of claim 2. Accordingly, claim 12 is rejected based on the same analysis.
Response to Arguments
Applicant's arguments filed 07 January 2026, with respect to the 35 U.S.C. §101 rejection of the claims, have been fully considered but they are not persuasive. Applicant argues that the claims are similar to the claim in Example 39 of the 2019 PEG by addressing problems particular to the field of artificial intelligence, i.e. an improvement to technology (see Applicant Remarks page 8-10). This is not persuasive. The claims are not analogous. Example 39 concludes that the claims do not recite a judicial exception under Step 2A Prong One, not that the claims recite an improvement to technology under Step 2A Prong Two. As discussed above, under Step 2A Prong One, the claims are directed a method of organizing human activity. Further, when considered under step 2A Prong Two and Step 2B, the additional elements present in this claim, including the machine learning principles such as training from feedback, merely recites the words ‘‘apply it’’ (or an equivalent) with the judicial exception, or merely includes instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea (see MPEP 2106.05(f)). These types of additional elements are not enough to integrate the abstract idea into a practical application, nor do they amount to significantly more than the judicial exception.
Next, Applicant argues that the claims do not recite a mental process because they recite artificial intelligence (see Applicant Remarks pages 10-11). This is not persuasive. As already discussed above, the broadly recited machine learning elements are not considered to be part of the abstract idea, but instead are considered to be additional elements under step 2A Prong Two and Step 2B. as currently presented, the machine learning recited in the claims amount to merely reciting the words ‘‘apply it’’ (or an equivalent) with the judicial exception, or merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea (see MPEP 2106.05(f)). These types of additional elements are not enough to integrate the abstract idea into a practical application, nor do they amount to significantly more than the judicial exception. Accordingly, the claims remain rejected as being directed to ineligible subject matter.
Applicant's arguments filed 07 January 2026, with respect to the 35 U.S.C. §103 rejection of the claims, have been fully considered but they are not persuasive. Applicant argues that the present amendment includes “additional limitations that distinguish the claims over the disclosures of Chin and Tran” (see Applicant Remarks page 12). However, Applicant does not include any arguments to support this statement and therefore it is not persuasive. The cited references (Chin and Tran) do disclose the amended language. Accordingly, under the broadest reasonable interpretation of the claim language, Chin in view of Tran does teach each and every claim element as currently presented in the application.
Applicant's arguments filed 07 January 2026, with respect to the Nonstatutory Double Patenting rejection of the claims, have been fully considered but they are not persuasive. Applicant argues that the present amendment to the claims render the rejection moot (see Applicant Remarks page 12). Although the claims at issue are not identical, they are not patentably distinct from the claims in the ‘860 patent. The present claims appear to be in a broader form than the claims in the ‘860 patent. However, they are still directed to the same subjected matter without any additional or different subject matter to distinguish them.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Chaturvedi et al. (U.S. 2023/0367969) discloses a system for prioritizing content provided to users with the use of transformer and generative models.
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/B.L.H./Examiner, Art Unit 3684
/Shahid Merchant/Supervisory Patent Examiner, Art Unit 3684