DETAILED ACTION
This Non-Final Office Action is in response to the application filed on 05/02/2024, the Amendment & Remark filed on 07/21/2026 and the Request for Continued Examination filed on 07/21/2026.
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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 07/21/2026 has been entered.
Status of Clams
Claims 5 and 14 are canceled.
Claims 1, 4, 6, 7, 9, 10, 13, 15, 16 and 18 are amended.
Claims 1-4, 6-13 and 15-18 are pending.
Claim Rejections - 35 USC § 112
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention.
Claims 1-4, 6-13 and 15-18 are rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention.
An original claim may lack written description support when (1) the claim defines the invention in functional language specifying a desired result but the disclosure fails to sufficiently identify how the function is performed or the result is achieved or (2) a broad genus claim is presented but the disclosure only describes a narrow species with no evidence that the genus is contemplated. See Ariad Pharms., Inc. v. Eli Lilly & Co., 598 F.3d 1336, 1349-50 (Fed. Cir. 2010) (en banc).
While the Applicant specifies in claims 1 and 10 that “generating, by the processor, personalized content information by using the user profile information and processed news articles as input to a transformer-based generative Artificial Intelligence (Al) model trained via back-propagation, the transformer-based generative Al model comprising an input layer configured to receive the user profile information and processed news articles, one or more hidden layers configured to process the user profile information and processed news articles to generate the personalized content information, and an output layer configured to output the personalized content information”, there is no written content as to how or what specific Generative AI model is used (i.e. formulas, algorithms, sequence of mathematical steps, process of determination, for example) in order to generate personalized content information using the user profile information and processed news articles as input. The examiner noted that Specification paragraph 0020 mentioned an non-limiting list of AI models that the Generative AI module “may utilize” and in paragraph 31 that “the user data as input to an input layer of the Generative AI model 130. The received information/data is then processed at a hidden layer of the Generative AI model 130 to generate personalized forecast/recommendation based on observed trend and the user's profile and preferences. The personalized content/news segment is then output from an output layer of the Generative AI model 130 for user’s review and selection”. However, these AI models are only nominally mentioned without discussion on the process of training a particular model or a particular combination of models to perform the desired function, in a result-only nominal fashion. As such, the disclosure does not objectively demonstrate that the applicant actually invented—was in possession of—the claimed subject matter.
While the Applicant specifies in claims 4 and 13 that “wherein the processor is configured to generate the personalized content information by: filtering and categorizing, by a news aggregator module, the news articles based on the financial portfolio data and user interests; and generating, by the transformer-based generative Al model, the personalized content information based on the user profile information and the filtered and categorized news articles received at the input layer of the transformer-based generative Al model”, there is no written content as to how or what specific Generative AI model is used (i.e. formulas, algorithms, sequence of mathematical steps, process of determination, for example) in order to generate personalized content information using the user profile information and filtered and categorized news articles as input. As such, the disclosure does not objectively demonstrate that the applicant actually invented—was in possession of—the claimed subject matter.
While the Applicant specifies in claims 6 and 15 that “wherein a tone and a reporting style of the personalized content information are adjusted according to the user preference information, and wherein the tone comprises one of a speech style of a real person and a speech style reminiscent of a fictional character”, there is no written content as to how or what specific process of determination are performed (i.e. formulas, algorithms, sequence of mathematical steps, process of determination, for example) in order to adjust the tone and reporting style according the user preference information. As such, the disclosure does not objectively demonstrate that the applicant actually invented—was in possession of—the claimed subject matter.
While the Applicant specifies in claims 7 and 16 that “wherein the processor is configured to generate the personalized content information by: for the news articles being comprised of a plurality of categories, adjusting a tone and a reporting style for each of the plurality of categories based on the user preference information; and generating, using the transformer-based generative Al model, a plurality of news segments from the news articles and the user profile information, each segment of the plurality of news segments being associated with a tone and a reporting style based on the user preference information, wherein the personalized content information comprises the plurality of news segments and associated tones and reporting styles”, there is no written content as to how or what specific Generative AI model is used (i.e. formulas, algorithms, sequence of mathematical steps, process of determination, for example) in order to adjust tone and reporting style and generate the plurality of news segments. As such, the disclosure does not objectively demonstrate that the applicant actually invented—was in possession of—the claimed subject matter.
While the Applicant specifies in claims 9 and 18 that “translating, by the transformer-based generative Al model, the news articles from a first language to a second language native to the user based on the user preference, wherein the processor is configured to generate the personalized content information from the user preference information and translated news articles using the transformer-based generative Al model”, there is no written content as to how or what specific Generative AI model is used (i.e. formulas, algorithms, sequence of mathematical steps, process of determination, for example) in order to translate the news article from a first language to second language based on the user preference. As such, the disclosure does not objectively demonstrate that the applicant actually invented—was in possession of—the claimed subject matter.
The written description requirement can be satisfied if the particular steps, i.e., algorithm, necessary to perform the claimed function were “described in the specification.” In re Hayes Microcomputer Prods, Inc. Patent Litigation, 982 F.2d 1527, 1533-34, 25 USPQ2d 1241, (Fed. Cir. 1992).
As such, claims 1-4, 6-13 and 15-18 are rejected as failing the written description requirement.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-4, 6-13 and 15-18 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.
As an initial matter, the claims as a whole are to a method and a system, which falls within one or more statutory categories. (Step 1: YES) The recitation of the claimed invention is then further analyzed as follow, in which the abstract elements are boldfaced.
Claim 1 recites:
A method for generating personalized newsletter, the method comprising:
collecting and processing, by a processor, news articles from various sources;
retrieving, by the processor, user profile information associated with a user stored in a database;
generate personalized content information by using the user profile information and processed news articles as input to a transformer-based generative Artificial Intelligence (AI) model trained via back-propagation, the transformer-based generative Al model comprising an input layer configured to receive the user profile information and processed news articles, one or more hidden layers configured to process the user profile information and processed news articles to generate the personalized content information, and an output layer configured to output the personalized content information; and
formatting, by the processor, the personalized content information into a newsletter tailored to the user for review;
generating, by the processor, a forecast based on the personalized content information and the user profile information using the transformer based generative Al model;
transmitting, by the processor, the forecast to a user device associated with the user as a push notification;
receiving, by the processor, a single user action from the user device in response to the push notification; and
automatically executing, by the processor, a financial order based on predefined user settings stored in the database in response to the single user action, wherein the financial order is executed without further user input after the single user action.
Claim 10 recites:
A personalized newsletter generation system, the system comprising:
a database;
and a processor in communication with the database, wherein the processor is configured to:
collect and processing news articles from various sources,
retrieve user profile information associated with a user stored in the database,
generate personalized content information by using the user profile information and processed news articles as input to a transformer-based generative Artificial Intelligence (AI) model trained via back-propagation, the transformer-based generative Al model comprising an input layer configured to receive the user profile information and processed news articles, one or more hidden layers configured to process the user profile information and processed news articles to generate the personalized content information, and an output layer configured to output the personalized content information, and
format the personalized content information into a newsletter tailored to the user for review;
generate a forecast based on the personalized content information and the user profile information using the transformer based generative Al model;
transmit the forecast to a user device associated with the user as a push notification;
receive a single user action from the user device in response to the push notification; and
automatically executing a financial order based on predefined user settings stored in the database in response to the single user action, wherein the financial order is executed without further user input after the single user action.
Claims 2 and 11 recites:
wherein the processor is configured to process the news articles by filtering and categorizing the news articles based on predefined topics and sectors.
Claims 3 and 12 recites:
wherein the user profile information comprises user demographic information, financial portfolio data, user interests, user preference information, and financial education level of the user.
Claims 4 and 13 recites:
wherein the processor is configured to generate the personalized content information by: filtering and categorizing, by a news aggregator module, the news articles based on the financial portfolio data and user interests; and generating, by the transformer-based generative Al model, the personalized content information based on the user profile information and the filtered and categorized news articles received at the input layer of the transformer-based generative Al model.
Claims 6 and 15 recites:
wherein a tone and a reporting style of the personalized content information are adjusted according to the user preference information and wherein the tone comprises one of a speech style of a real person and a speech style reminiscent of a fictional character.
Claims 7 and 16 recites:
wherein the processor is configured to generate the personalized content information by: for the news articles being comprised of a plurality of categories, adjusting a tone and a reporting style for each of the plurality of categories based on the user preference information; and generating, using the transformer-based generative Al model, a plurality of news segments from the news articles and the user profile information, each segment of the plurality of news segments being associated with a tone and a reporting style based on the user preference information, wherein the personalized content information comprises the plurality of news segments and associated tones and reporting styles.
Claims 8 and 17 recites:
wherein tones associated with the plurality of categories are voice tones, and each of the voice tones is associated a voice of a person or a fictional character.
Claims 9 and 18 recites:
translating, by the transformer-based generative Al model processor, the news articles from a first language to a second language native to the user based on the user preference information, wherein the processor is configured to generate the personalized content information from the user preference information and translated news articles using the transformer-based generative Al model.
Based on the limitations above, the claims describe a process that covers providing a personalized content service. Providing personalized content, forecast and execution of financial order is considered to be a commercial interaction at least between a content provider and a client, which falls within the “Certain Methods of Organizing Human Activity” grouping of abstract ideas. As such, the claim(s) recite(s) a Judicial Exception. (Step 2A prong one: Yes)
This analysis then evaluates whether the claims as a whole integrates the recited Judicial Exception into a practical application of the exception. In particular, the claims recite the additional element(s) of “processor” as a mere tool to perform the steps of the Judicial Exception, which encompasses no more than Mere Instruction to Apply.
For example, the limitation “collect and processing news articles from various sources” encompasses no more than generically invoking a processor to apply the Judicial Exception step of collecting and processing news articles;
the limitation “retrieve user profile information associated with a user stored in the database” encompasses no more than generically invoking a processor to apply the Judicial Exception step of retrieving user profile information;
the limitation “generate personalized content information by using the user profile information and processed news articles as input to a transformer-based generative Artificial Intelligence (AI) model trained via back-propagation, the transformer-based generative Al model comprising an input layer configured to receive the user profile information and processed news articles, one or more hidden layers configured to process the user profile information and processed news articles to generate the personalized content information, and an output layer configured to output the personalized content information” encompasses no more than generically invoking a processor to apply the Judicial Exception step of generating personalized content information by receiving user profile information and processed news articles, processing the profile information and news articles to generate personalized content information and outputting the personalized content information;
the limitation “format the personalized content information into a newsletter tailored to the user for review” encompasses no more than generically invoking a processor to apply the Judicial Exception step of formatting the personalized content information into a newsletter tailor to the user for review;
the limitation “generate a forecast based on the personalized content information and the user profile information using the transformer based generative Al model” encompasses no more than generically invoking a processor to apply the Judicial Exception step of generating the forecast based on personalized content information and user profile information;
the limitation “transmit the forecast to a user device associated with the user as a push notification” encompasses no more than generically invoking a processor to apply the Judicial Exception step of sending the forecast to the user;
the limitation “receive a single user action from the user device in response to the push notification” encompasses no more than generically invoking a processor to apply the Judicial Exception step of receiving the user’s single action response;
the limitation “automatically executing a financial order based on predefined user settings stored in the database in response to the single user action, wherein the financial order is executed without further user input after the single user action” encompasses no more than generically invoking a processor to apply the Judicial Exception step of executing the financial order based on predefined user settings in response to the single action response;
the limitation “wherein the processor is configured to process the news articles by filtering and categorizing the news articles based on predefined topics and sectors” encompasses no more than generically invoking a processor to apply the Judicial Exception step of filtering and categorizing the news articles;
the limitation “wherein the processor is configured to generate the personalized content information by: filtering and categorizing, by a news aggregator module, the news articles based on the financial portfolio data and user interests; and generating, by the transformer-based generative Al model, the personalized content information based on the user profile information and the filtered and categorized news articles received at the input layer of the transformer-based generative Al model” encompasses no more than generically invoking a processor to apply the Judicial Exception step of filtering and categorizing the news articles, and generating the personalized content information;
the limitation “wherein the processor is configured to generate the personalized content information by: for the news articles being comprised of a plurality of categories, adjusting a tone and a reporting style for each of the plurality of categories based on the user preference information; and generating, using the transformer-based generative Al model, a plurality of news segments from the news articles and the user profile information, each segment of the plurality of news segments being associated with a tone and a reporting style based on the user preference information, wherein the personalized content information comprises the plurality of news segments and associated tones and reporting styles” encompasses no more than generically invoking a processor to apply the Judicial Exception step of adjusting the tone and reporting style for each categories based on user preference and generating a plurality of news segments with the respective tones and reporting styles;
the limitation “translating, by the transformer-based generative Al model processor, the news articles from a first language to a second language native to the user based on the user preference information, wherein the processor is configured to generate the personalized content information from the user preference information and translated news articles using the transformer-based generative Al model” encompasses no more than generically invoking a processor to apply the Judicial Exception step of translating the news articles from a first language to a second language.
Other than being generally linked to the steps of the Judicial Exception, the additional elements in the above step(s) is/are recited at a high-level of generality, without technological detail of how the particular steps are performed technologically.
The additional element(s) of “database” are generically recited to store data and/or instructions of the Judicial Exception.
The additional element(s) of “as a push notification” are generically recited to perform transmitting steps described only by a result-oriented solution with insufficient detail for how the step was accomplished.
The additional element(s) of “the transformer based generative Al model”, “trained via back-propagation”, “input layer configured to receive”, “hidden layers configured to process” and “output layer configured to output” are generically recited to perform steps of generating personalized content information described only by a result-oriented solution with insufficient detail for how the Generative AI accomplish it. The examiner found that to be mere instructions to implement the Judicial Exception idea on a computer by nominally reciting “by the transformer based generative Al model”.
Indeed, the instant claims (1) attempted to cover a solution to an identified problem with no restriction on how the result is accomplished and no description of the mechanism for accomplishing the result; (2) used of a computer or other machinery in its ordinary capacity for economic or other tasks or simply added a general purpose computer or computer components after the fact to the Judicial Exception and (3) generally applied the Judicial Exception to a generic computing environment without limitation indicative of practical application (See MPEP 2106.04(d)I). Thus, the claims are no more than Mere Instruction to Apply the Judicial Exception (See MPEP 2106.05(f)) or adding insignificant extra-solution activity to the judicial exception (See MPEP 2106.05(g)), which do not integrate the cited Judicial Exception into practical application (Step 2A prong two: No) The claims are directed to a Judicial Exception.
The claim does 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 using a processor to provide personalized content, forecast and financial order execution amounts to no more than mere instructions to apply the exception using generic computer components. The recited ordered combination of additional elements includes invoking generic processor and AI to apply the Judicial Exception. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. Dependent claim 3, 6, 8, 12, 15 and 17 merely limit the abstract idea but do not recite any additional element beyond the cited abstract idea, thus, do not amount to significantly more. No additional element currently recited in the claims amount the claims to be significantly more than the cited abstract idea. (Step 2B: No)
Therefore, claims 1-4, 6-13 and 15-18 are rejected under 35 U.S.C. 101 as being directed to non-statutory subject matter.
Response to Arguments
Applicant's arguments filed on 07/21/2026 have been fully considered but they are not persuasive.
Regarding the applicant’s argument that the features rejected in 35 USC 112(a) are adequately supported by the Specification, the examiner respectfully disagrees. The applicant contended that the claim amendment address the rejection by reciting “a transformer-based generative Al model trained via back- propagation, the transformer-based generative Al model comprising an input layer configured to receive the user profile information and processed news articles, one or more hidden layers configured to process the user profile information and processed news articles to generate the personalized content information, and an output layer configured to output the personalized content information”. However, it should be noted that the written description support for the training the layers of the “transformer based generative AI” found in the Specification merely includes result-only nominal description that does not explain the technological knowhow. For example, Specification paragraph 0020 recites “The news data is received at an input layer of the Generative AI model, and processed at a hidden layer of the Generative AI model to generate personalized content/news segment based on the user's profile and preferences. The personalized content/news segment is then output from an output layer of the Generative AI model for additional processing” and paragraph 0031 recites “the forecast/recommendation may be derived using the personalized newsletter and the user data as input to an input layer of the Generative AI model 130. The received information/data is then processed at a hidden layer of the Generative AI model 130 to generate personalized forecast/recommendation based on observed trend and the user's profile and preferences. The personalized content/news segment is then output from an output layer of the Generative AI model 130 for user’s review and selection”. None of the paragraphs discuss how the “hidden layer” of the generative AI turn user data into personalized forecast or recommendation. As to the training, the only disclosure found regarding training is an sentence nominal disclosure “Training of the Generative AI model is performed using back-propagation” without any technological detail of how a generic technique of back-prorogation would train a generically recited “transformer based generic AI model” to perform the specific function claimed. There is no support objectively demonstrating the possession of such generative AI As such, the argument is not persuasive.
Regarding the applicant’s argument does not recite the commercial interaction, the examiner respectfully disagrees. The claims recite limitations encompassing the generating and providing of newsletters to user. The recitation of the additional elements such as automation, AI model and push notification does not negate the recitation of the limitations. As such the argument is not persuasive.
Regarding the applicant’s argument that the claims integrate the Judicial Exception into practical application, the examiner respectfully disagrees. The applicant contended that the claims provide “type of solution "necessarily rooted in computer technology in order to overcome a problem specifically arising in the realm of computer networks" that the Federal Circuit recognized as patent-eligible in DDR Holdings, LLC v. Hotels.com”. However, it should be noted the nature of providing newsletter is not rooted to computer nor AI technology. The mere usage of computer or AI in practicing the commercial interaction does not root such interaction to the technology as the risk mitigation in Alice Corp. is nonetheless applied to the computing environment. Moreover, the applicant did not provide how the mere usage of the AI model would “meaningfully limits beyond a general link to a technological environment” as the claims only contains mere usage. As such, the argument is not persuasive.
Regarding the applicant’s argument that the claims include arrangement that is not well-understood, routine, and conventional activity, the examiner respectfully disagrees. It should be noted that neither the generative AI model nor the push notification was claimed to have been invented by the applicant. Limitations that the courts have found not to be enough to qualify as “significantly more” when recited in a claim with a judicial exception explicitly include: Generally linking the use of the judicial exception to a particular technological environment or field of use, e.g., a claim describing how the abstract idea of hedging could be used in the commodities and energy markets, as discussed in Bilski v. Kappos, 561 U.S. 593, 595, 95 USPQ2d 1001, 1010 (2010) or a claim limiting the use of a mathematical formula to the petrochemical and oil-refining fields, as discussed in Parker v. Flook, 437 U.S. 584, 588-90, 198 USPQ 193, 197-98 (1978) (MPEP § 2106.05(h)). The claims merely invoke the generative AI model to perform the generating of personalized newsletter without technological details of the AI model. Similarly, the push notification is only invoked to perform the transmitting of the newsletters without technological details of the push notification technology. As such, the argument is not persuasive.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to CHO KWONG whose telephone number is (571)270-7955. The examiner can normally be reached 9am - 5pm EST M-F.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, MICHAEL W ANDERSON can be reached at 571-270-0508. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/CHO YIU KWONG/Primary Examiner, Art Unit 3693
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