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 .
This application claims the benefit under 35 U.S.C. §119(e) of U.S. Provisional Patent Application Ser. No. 63/517,867, filed 08/04/2023.
This communication is responsive to the amendment filed on 11/07/2025.
Claims 1-20 are amended and presented for examination.
Information Disclosure Statement
The information disclosure statement (IDS) filed on 11/11/2024 complies with the provisions of M.P.E.P 609. It has been placed in the application file. The information referred to therein has been considered as to the merits.
Response to Arguments
Applicant's arguments with respect to the newly added limitations have been considered in view of the new ground(s) of rejection necessitated by amendment.
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 an abstract without significantly more.
Claims 1, 8 and 15Step 1: Statutory Category
The claims are directed to one of the four statutory categories of invention, i.e., process, machine, manufacture, or composition of matter.
Step 2A, Prong One:
The claims recite the limitations “generating …; generating …; creating…” are processes that, under its broadest reasonable interpretation, covers a mental process as a form of evaluation or judgement, but for the recitation of generic computer components. That is nothing in the claim element precludes the steps from practically being performed in a human mind.
If a claim limitation, under its broadest reasonable interpretation, covers
performance of the limitation in the mind but for the recitation of generic computer
components, then it falls within the "Mental Processes" grouping of abstract ideas.
Accordingly, the claim recites an abstract idea.
Step 2A, Prong Two: Integrated into a Practical Application
“receiving…; inputting…; randomly retrieving…”, amount to data-gathering steps which is considered to be insignificant extra-solution activity, (See MPEP 2106.05(g)).
“processor”, “memory” and “non-transitory computer-readable storage medium” are recited at a high level of generality such that they amount to on more than mere instructions to apply the exception using a generic component. (see MPEP 2106.05(f)). These limitations can also be viewed as nothing more than an attempt to generally link the use of the judicial exception to the technological environment of a computer (see MPEP 2106.05(h)).
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. The insignificant extra-solution activities identified above, which include the data-gathering, and presenting steps, are recognized by the courts as well-understood, routine, and conventional activities when they are claimed Ina merely generic manner (e.g., at a high level of generality) or as insignificant extra-solution activity.
Step 2B: Claim provides an Inventive Concept
“receiving…; inputting…; randomly retrieving… ”. These are identified as insignificant extra-solution activity above when re-evaluated this element is well-understood, routine, and conventional as evidenced by the court cases in MPEP 2106.05(d)(II), "i. Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information); … OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network); buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network);" and thus remains insignificant extra-solution activity that does not provide significantly more.
“processor”, “memory” and “non-transitory computer-readable storage medium”, amount to elements that have been recognized as well-understood, routine, and conventional activity in particular fields, as demonstrate by: relevant court decision: the followings are example of the court decisions demonstrating well-understood, routine and conventional activities, See e.g., MPEP 2106.05(d)(II) and MPEP 2106.05(f)(2): computer readable storage media comprising instructions to implement a method, e.g., see versata Dev. Group, Inc. v SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015).
The claims as a whole, does not amount to significantly more than the abstract
idea itself. This is because the claims do not affect an improvement to the functioning
of a computer itself; and the claims do not move beyond a general link of the use of an
abstract idea to a particular technological environment.
Accordingly, claims are directed to an abstract idea.
Claims 2-6, recites the additional limitations. These additional element do not integrate the integrate the judicial exception into a practical application and does not amount to significantly more..
Claim 7 recites the limitations, which are process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind, but for the recitation of generic computer components. They falls within the "Mental Processes" grouping of abstract ideas (concepts performed in the human mind including an observation, evaluation, judgement, and opinion).
Claims 8-14 are the system claims, which are similar to claims 2-7. Therefore, they are rejected under the same rational as claims 2-7 above.
Claims 16-20 are a non-transitory machine-readable storage medium claims, which are similar to claims 2-7. Therefore, they are rejected under the same rational as claims 2-7 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.
Claims 1-2, 4, 6-9, 11, 12-16, 18 and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Luzhnica et al., (US 11/516,158), hereinafter “Luzhnica”, in view of Srinivasan (US 11,157,693 B2).
As per claim 1, Luzhnica discloses a method comprising (see Fig.3A, col.10, lines 10-12 and col.116, lines 6-13, is a flow chart providing an overview of steps of an exemplary method of generating messages):
- receiving input data comprising at least one of attribute data or section data, wherein the input data is associated with at least one of a message sender or a prospective message recipient (col. 8 lines 52-67, allow users to input, store, and re-use different value propositions as well as to personalize a message using an article from the recipient's company web site, the recipient's LinkedIn posts; and col. 9 lines 9-35, allow for the input of different types of prompts as well as settings to generate messages that include greetings, closings, and multiple paragraphs. In one example presented on Flowrite.com, a message is generated using a prior message apparently as a prompt along with a few additional short segment inputs (yes and a date/time indicator);
- randomly retrieving a sentence from a database (col.71, line 40-col.74, line 18), wherein the sentence is associated with the at least one of the attribute data or the section data (col.107, lines 10-36);
- generating a prompt including the input data and the sentence randomly retrieved from the database (col.71, line 40-col.74, line 18, provides an artificial intelligence writing system that allows users to input and to either generate a sentence of a paragraph of text in response to such a prompt);
- creating a message based on a message content suggestion of the one or more message content suggestions generated by the first generative model, wherein the message is from the message sender to the prospective message recipient (col.18, lines 27-48, col.72, lines 53-col.73, line 3 and col.134, line 30-col.135, line 7, messages generated by systems/methods and important components of training set data include natural language messages, all of the messages generated by the system, messages included in training set data, or both, are linguistically complex, e.g., comprising at least 2 paragraphs, at least 1 multi-sentence paragraph, optionally 2 or 3 difference sentence types, and all messages generated by the system or used in training set data is characterized as semi-structured data (e.g., as emails including recipient address information, greeting, closing, etc.);.
However, Luzhnica does not disclose inputting the prompt to a first generative model; and generating, by the first generative model, one or more message content suggestions based on the prompt including the sentence randomly retrieved from the database and the at least one of the attribute data or the section data
On the other hand, Srinivasan discloses inputting the prompt to a first generative model (col. 5, lines 30-67, and col. 6, lines 1-5, describing providing the prompt as input to a generative AI model in order to train the model based on the sender’s writing style);
- generating, by the first generative model, one or more message content suggestions based on the prompt including the sentence randomly retrieved from the database and the at least one of the attribute data or the section data (Figure 8, col. 5, lines 30-67, and col. 6, lines 1-5, describing providing the prompt as input to a generative AI model in order to train the model based on the sender’s writing style; and col. 16 lines 53-63, generating stylized text by rewriting an input text in the writing style of a target author , wherein an input text is received and it’s representative of text comprising one or more sentences, and may represent text in any one or more possible formats such as an electronic message).
Therefore, it would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to have modified the system of Luzhnica to include the features as disclosed by Srinivasan in order to provide a copywriter's ability to understand which suggestion is appropriate to use for a target author and rewrite an input text with high fidelity in the stylistic rewriting system.
Claim 2, the combination of Luzhnica and Srinivasan discloses the invention as claimed. In addition, Luzhnica discloses the first generative model is trained to insert the sentence into suggested message content (col.71, line 40-col.74, line 18, provides an artificial intelligence writing system that allows users to input and to either generate a sentence of a paragraph of text in response to such a prompt), by the first generative model, including the sentence in the one or more message content suggestions data (col.71, line 40-col.74, line 18, provides an artificial intelligence writing system that allows users to input and to either generate a sentence of a paragraph of text in response to such a prompt);
Srinivasan also discloses by the first generative model, including the sentence in the one or more message content suggestions data (Figure 8, col. 5, lines 30-67, and col. 6, lines 1-5, describing providing the prompt as input to a generative AI model in order to train the model based on the sender’s writing style; and col. 16 lines 53-63, generating stylized text by rewriting an input text in the writing style of a target author , wherein an input text is received and it’s representative of text comprising one or more sentences, and may represent text in any one or more possible formats such as an electronic message).
Claim 4, the combination of Luzhnica and Srinivasan discloses the invention as claimed. In addition, Srinivasan discloses the section classifier is trained to classify a sentence of a historic message of the one or more historic messages or a generated message of the one or more synthetically-generated messages as belonging to a section (abstract, col. 1, lines 56-67, and col. 2, lines 1-4, querying a target author corpus for additional data relating to the author or sender of the message).
Claim 6, the combination of Luzhnica and Srinivasan discloses the invention as claimed. In addition, Luzhnica discloses generating the prompt comprises performing one or more string transformations on the at least one of the attribute data or the section data (col.107, lines 10-36).
As per claim 7, the combination of Luzhnica and Srinivasan discloses the invention as claimed. In addition, Luzhnica discloses generating, using a second generative model, a synthetically-generated message comprising a first section or a first attribute (col.107, lines 50-60); and
- determining a set of training message plans comprising a first training message plan based on at least one of the first section or the first attribute and a second training message plan based on a historic message, wherein the historic message comprises a second section or a second attribute, and the second section or the second attribute are different from the first section or the first attribute (col.35, line 20-35 and col.110, lines 15-20).
As per claims 8-9 and 11, are the system claims, which are similar to claims 1-2 and 4. Therefore, they are rejected under the same rational as claims 1-2 and 4 above.
As per claims 15-16 and 18, are a non-transitory machine-readable storage medium claims, which are similar to claims 1-2 and 4. Therefore, they are rejected under the same rational as claims 1-2 and 4 above.
Claims 3, 5, 10, 12, 17 and 19 are rejected under 35 U.S.C. 103 as being unpatentable over Luzhnica, Srinivasan, and further in view of Natoli et al., (US 11,909,699), hereinafter “Natoli”,
Claim 3, Natoli discloses the first generative model is fine- tuned to generate message content using a training message plan and a corresponding message (col. 15 lines 6-25, a fine-tuned enhancer model 132 can be applied (118) to enhance existing content for a selected one or more of the M short messages. That is, the remaining M short messages are enhanced by the fine-tuned enhancer model 132 by adding content. For an embodiment, applying (118) the fine-tuned enhancer model 132 to enhance existing content includes adding emojis to the selected one or more of the M short messages; col.1, lines 23-48 and col.11, line 58-col.12, line 14 and col.15, lines 10-25, a fine-tuned enhancer model is applied to enhance existing content for a selected one or more of the M short messages, wherein the remaining M short messages are enhanced by the fine-tuned enhancer model by adding content, applying the fine-tuned enhancer model to enhance existing content includes adding emojis to the selected one or more of the M short messages, wherein the inclusion of emojis, for instance, is determined by training an enhancement model on historical short message-emoji pairs, gathering an emoji selection by calling the enhancement model on a short message, and appending the emojis to the short message according to predetermined patterns and supplementing data used to train the generative text engine model with quality ratings of the at least the portion of the messages based on the M short messages),
the training message plan comprises an ordered sequence of training attribute data and training section data (col.6, lines 22-55, sequences of behaviors by the customer can be ranked for determining a score which is used for determining whether a customer action has occurred, wherein an artificial intelligence model trained to classify sequences of clicks and/or keystrokes as an acceptance or not an acceptance),
the training attribute data and training section data are extracted from one or more historic messages or one or more synthetically-generated message (col.4, lines 60-67, the trained historical model is equipped to assign a quality rating to new, previously unseen M short messages after the short messages have been generated, allowing selection of the predicted top-performing short messages to display to the merchant; col.6, lines 22-55, sequences of behaviors by the customer can be ranked for determining a score which is used for determining whether a customer action has occurred, wherein an artificial intelligence model trained to classify sequences of clicks and/or keystrokes as an acceptance or not an acceptance), and
the training section data extracted from the one or more historic messages or the one or more synthetically-generated messages is identified using a section classifier (col.4, lines 60-67, the trained historical model is equipped to assign a quality rating to new, previously unseen M short messages after the short messages have been generated, allowing selection of the predicted top-performing short messages to display to the merchant; col.6, lines 22-55, sequences of behaviors by the customer can be ranked for determining a score which is used for determining whether a customer action has occurred, wherein an artificial intelligence model trained to classify sequences of clicks and/or keystrokes as an acceptance or not an acceptance).
Therefore, it would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to have modified the system of Srinivasan to include the features as disclosed by Natoli in order to improve short messages generating performance of the electronic marketing message, thereby allowing merchant users with ability to automatically adjust tone of the short messages based on brand tone and adding effective practice keywords to improve success rate of the short messages.
Claim 5, the combination of Luzhnica and Srinivasan discloses the invention as claimed. In addition, Luzhnica discloses the training attribute data extracted from at least one or more historic messages or generated messages comprises key:value pairs (col. 121, line 55-col.122, line 19).
However, the combination of Luzhnica and Srinivasan do not disclose the first generative model is fine-tuned to generate message content using a training message plan and a corresponding message.
Meanwhile, Natoli discloses the first generative model is fine-tuned to generate message content using a training message plan and a corresponding message
(col. 15 lines 6-25, a fine-tuned enhancer model 132 can be applied (118) to enhance existing content for a selected one or more of the M short messages. That is, the remaining M short messages are enhanced by the fine-tuned enhancer model 132 by adding content. For an embodiment, applying (118) the fine-tuned enhancer model 132 to enhance existing content includes adding emojis to the selected one or more of the M short messages; col.1, lines 23-48 and col.11, line 58-col.12, line 14 and col.15, lines 10-25, a fine-tuned enhancer model is applied to enhance existing content for a selected one or more of the M short messages, wherein the remaining M short messages are enhanced by the fine-tuned enhancer model by adding content, applying the fine-tuned enhancer model to enhance existing content includes adding emojis to the selected one or more of the M short messages, wherein the inclusion of emojis, for instance, is determined by training an enhancement model on historical short message-emoji pairs, gathering an emoji selection by calling the enhancement model on a short message, and appending the emojis to the short message according to predetermined patterns and supplementing data used to train the generative text engine model with quality ratings of the at least the portion of the messages based on the M short messages),
the training message plan comprises an ordered sequence of training attribute data and training section data (col.6, lines 22-55, sequences of behaviors by the customer can be ranked for determining a score which is used for determining whether a customer action has occurred, wherein an artificial intelligence model trained to classify sequences of clicks and/or keystrokes as an acceptance or not an acceptance),
the training attribute data and training section data are extracted from at least one or more historic messages or one or more synthetically-generated messages (col.4, lines 60-67, the trained historical model is equipped to assign a quality rating to new, previously unseen M short messages after the short messages have been generated, allowing selection of the predicted top-performing short messages to display to the merchant).
Therefore, it would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to have modified the system of cited references to include the features as disclosed by Natoli in order to improve short messages generating performance of the electronic marketing message, thereby allowing merchant users with ability to automatically adjust tone of the short messages based on brand tone and adding effective practice keywords to improve success rate of the short messages.
As per claims 10 and 12, are the system claims, which are similar to claims 3 and 5. Therefore, they are rejected under the same rational as claims 3 and 5 above.
As per claims 17 and 19, are a non-transitory machine-readable storage medium claims, which are similar to claims 3 and 5. Therefore, they are rejected under the same rational as claims 3 and 5 above.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, 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 extension fee 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 date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Loan T. Nguyen whose telephone number is (571) 270-3103. The examiner can normally be reached on Monday from 10:00 am - 6:00 pm, Thursday-Friday from 10:00 am - 2:00 pm. If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Aleksandr Kerzhner can be reached on (571) 270-1760. The fax phone number for the organization where this application or proceeding is assigned is 571-270-4103. Information regarding the status of an application may be obtained from the Patent Application Information Retrieval (PAIR) system. Status information for published applications may be obtained from either Private PAIR or Public PAIR. Status information for unpublished applications is available through Private PAIR only. For more information about the PAIR system, see http://pair-direct.uspto.gov. Should you have questions on access to the Private PAIR system, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative or access to the automated information system, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/LOAN T NGUYEN/Examiner, Art Unit 2165
/ALEKSANDR KERZHNER/Supervisory Patent Examiner, Art Unit 2165
Prosecution Insights