Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
DETAILED ACTION
Claims 1-20 are present in this application. Claims 1-20 are pending in this office action.
This office action is NON-FINAL.
Drawings
The Drawings filed on 08/30/24 are acceptable for examination purposes.
Specification
The Specification filed on 08/30/24 is acceptable for examination purposes.
Information Disclosure Statement
The information disclosure statements (IDS) filed on 02/26/26 has been
considered by the Examiner and made of record in the application file.
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 idea without significantly more.
Claim 1 recites “receiving, from a publisher, one or more inputs describing a payload series to be designed, the payload series including one or more content payloads to be sent to a payload recipient; generating a prompt for input to a large language model (LLM), the prompt including a request to design the payload series based on the one or more inputs; providing the prompt to a model serving system for execution by the LLM; receiving, from the model serving system, the payload series generated by executing the LLM on the prompt; and transmitting one or more content payloads of the generated payload series to the payload recipient”.
The limitation of “receiving, from a publisher, one or more inputs describing a payload series to be designed, the payload series including one or more content payloads to be sent to a payload recipient; generating a prompt for input to a large language model (LLM), the prompt including a request to design the payload series based on the one or more inputs; providing the prompt to a model serving system for execution by the LLM; receiving, from the model serving system, the payload series generated by executing the LLM on the prompt; and transmitting one or more content payloads of the generated payload series to the payload recipient”. Nothing in the claim element precludes the step from practically being performed in the mind. Accordingly, the claim recites an abstract idea. This judicial exception is not integrated into a practical application. In particular, the claim does not recite additional elements to perform receiving, generating, providing, receiving and transmitting and steps.
Accordingly, the claim is directed to an abstract idea. The claim does not include
additional elements that are sufficient to amount to significantly more than the judicial
exception. Mere instructions to apply an exception using a generic computer
component cannot provide an inventive concept. The claim is not patent eligible.
Claim 2 is dependent on claim 1 and includes all the limitations of claim 1. Claim
2 recites receiving, from one or more user computing devices, a message from a conversation sent from the payload recipient to the publisher; generating a prompt for input to a second LLM, the prompt specifying at least the message and a request to infer whether an automated action can be performed for the message; providing the prompt to a model serving system for execution by the second LLM; receiving, from the model serving system, a response generated by executing the second LLM on the prompt; parsing the response from the model serving system to extract an automated action to perform based on the message; in claim 2. But receiving…a message from a conversation sent from the payload recipient to the publisher; generating a prompt for input to a second LLM…; providing the prompt to a model serving system for execution by the second LLM; receiving, from the model serving system, a response generated by executing the second LLM on the prompt; parsing the response from the model serving system to extract an automated action to perform based on the message a does not go beyond the abstract idea itself. There are no additional components in the claim that would make it significantly more than the abstract idea.
Claim 3 is dependent on claim 1 and includes all the limitations of claim 1. Claim
3 recites wherein the one or more inputs describing the payload series to be designed describe a tone of the one or more content payloads in claim 3. But the one or more inputs describing the payload series to be designed describe a tone of the one or more content payloads does not go beyond the abstract idea itself. There are no additional components in the claim that would make it significantly more than the abstract idea.
Claim 4 is dependent on claim 1 and includes all the limitations of claim 1. Claim
4 recites wherein the one or more inputs describing the payload series to be designed describe keywords to include and exclude in the one or more content payloads in claim 4. But the one or more inputs describing the payload series to be designed describe keywords to include and exclude in the one or more content payloads does not go beyond the abstract idea itself. There are no additional components in the claim that would make it significantly more than the abstract idea.
Claim 5 is dependent on claim 1 and includes all the limitations of claim 1. Claim
5 recites wherein the one or more inputs describing the payload series to be designed describe start and end conditions, channels to use for each message, trigger conditions, and number of content payloads in the payload series in claim 5. But the one or more inputs describing the payload series to be designed describe start and end conditions, channels to use for each message, trigger conditions, and number of content payloads in the payload series does not go beyond the abstract idea itself. There are no additional components in the claim that would make it significantly more than the abstract idea.
Claim 6 is dependent on claim 1 and includes all the limitations of claim 1. Claim
6 recites wherein the LLM is trained based on training examples including past payload series associated with the publisher in claim 6. But the LLM is trained based on training examples including past payload series associated with the publisher does not go beyond the abstract idea itself. There are no additional components in the claim that would make it significantly more than the abstract idea.
Claim 7 is dependent on claim 1 and includes all the limitations of claim 1. Claim
7 recites wherein the LLM is trained based on training examples including past payload series associated with an industry in claim 7. But the LLM is trained based on training examples including past payload series associated with the publisher does not go beyond the abstract idea itself. There are no additional components in the claim that would make it significantly more than the abstract idea.
Claim 8 is dependent on claim 1 and includes all the limitations of claim 1. Claim
8 recites wherein the LLM is trained based on training examples including past payload series associated with a channel of communication in claim 8. But the LLM is trained based on training examples including past payload series associated with a channel of communication does not go beyond the abstract idea itself. There are no additional components in the claim that would make it significantly more than the abstract idea.
Claim 9 is dependent on claim 1 and includes all the limitations of claim 1. Claim
9 recites wherein the LLM is trained based on training examples including past payload series labeled by feedback received from payload recipients of the past payload series in claim 9. But the LLM is trained based on training examples including past payload series labeled by feedback received from payload recipients of the past payload series does not go beyond the abstract idea itself. There are no additional components in the claim that would make it significantly more than the abstract idea.
Claim 10 is dependent on claim 9 and includes all the limitations of claim 1. Claim 10 recites wherein the LLM is trained based on training examples including past payload series labeled by feedback received from payload recipients of the past payload series in claim 10. But wherein the LLM is trained based on training examples including past payload series labeled by feedback received from payload recipients of the past payload series does not go beyond the abstract idea itself. There are no additional components in the claim that would make it significantly more than the abstract idea.
Claim 11 recites the same limitations as claim 1 above. Therefore, claim 11 is rejected based on the same reasoning.
Claim 12 recites the same limitations as claim 2 above. Therefore, claim 12 is
rejected based on the same reasoning.
Claim 13 recites the same limitations as claim 3 above. Therefore, claim 13 is
rejected based on the same reasoning.
Claim 14 recites the same limitations as claim 4 above. Therefore, claim 14 is
rejected based on the same reasoning.
Claim 15 recites the same limitations as claim 5 above. Therefore, claim 15 is
rejected based on the same reasoning.
Claim 16 recites the same limitations as claim 6 above. Therefore, claim 16 is
rejected based on the same reasoning.
Claim 17 recites the same limitations as claim 7 above. Therefore, claim 17 is
rejected based on the same reasoning.
Claim 18 recites the same limitations as claim 8 above. Therefore, claim 18 is
rejected based on the same reasoning.
Claim 19 recites the same limitations as claim 9 above. Therefore, claim 19 is
rejected based on the same reasoning.
Claim 20 recites the same limitations as claim 1 above. Therefore, claim 20 is rejected based on the same reasoning.
Claim Rejections 35 U.S.C. §103
6. In the event the determination of the status of the application as subject to AIA 35
U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any
correction of the statutory basis for the rejection will not be considered a new ground of
rejection if the prior art relied upon, and the rationale supporting the rejection, would be
the same under either status.
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 following is a quotation of 35 U.S.C. 103 which forms the basis for all
obviousness rejections set forth in this Office action:
Claims 1-20 are rejected under 35 U.S.C. § 103 as being un patentable over SAXENA (US 2024/0330579 A1) in view of HATTANGADY et al. (US 2025/0005295 A1).
Regarding claim 1, SAXENA teaches a computer-implemented method, comprising:
receiving, from a publisher, (See , SAXENA paragraph [0021], The website development system 120 receives data associated with the website 105 and/or user input 11), one or more inputs describing a payload series to be designed, the payload series including one or more content payloads to be sent to a payload recipient, (See , SAXENA paragraph [0021], The website development system 120 receives data associated with the website 105 and/or user input 110 and, using the website data or user input);
generating a prompt for input to a large language model (LLM), the prompt including a request to design the payload series based on the one or more inputs, (See, SAXENA paragraph [0021], using the website data or user input, generates a prompt 125 to a large language model (LLM) 130. The LLM 130 can include any commercially available or custom models, or a set or ensemble of two or more models);
providing the prompt to a model serving system for execution by the LLM, (See SAXENA paragraph [0075], A prompt can include one or more examples of the desired output, which provides the LLM with additional information to enable the LLM to better generate output according to the desired output);
receiving, from the model serving system, the payload series generated by executing the LLM on the prompt, (See SAXENA paragraph [0021], receives data associated with the website 105 and/or user input 110 and, using the website data or user input, generates a prompt 125 to a large language model (LLM) 130); and
SAXENA does not explicitly disclose transmitting one or more content payloads of the generated payload series to the payload recipient.
However, HATTANGADY teaches transmitting one or more content payloads of the generated payload series to the payload recipient, (See HATTANGADY paragraph [0105], the user is a first user, the AI prompt is a first AI prompt, the output payload is a first output payload…wherein a sender of the draft message is a second user and a recipient of the draft message is the first user).
It would have been obvious to one with ordinary skill in the art before the
effective filing date of the claimed invention was made to modify transmitting one or more content payloads of the generated payload series to the payload recipient of HATTANGADY in order for generating advanced feedback for draft messages using a language model.
Claims 11 and 20 recite the same limitations as claim 1 above. Therefore, claims 11 and 20 are rejected based on the same reasoning.
Regarding claim 2, SAXENA taught the computer-implemented method according to claim 1 as described above. SAXENA further teaches, further comprising:
receiving, from one or more user computing devices, (See SAXENA paragraph [0021], The website development system 120 receives data associated with the website 105 and/or user input 110), a message from a conversation sent from the payload recipient to the publisher, (See SAXENA paragraph [0095], provide for a communications facility 729 and associated merchant interface for providing electronic communications and marketing, such as utilizing an electronic messaging facility for collecting and analyzing communication interactions between merchants, customers, merchant devices 702, customer devices 750);
generating a prompt for input to a second LLM, the prompt specifying at least the message, (See SAXENA paragraph [0080], The prompt generated by the computing system is provided to the language model or LLM…the prompt could be sent to a remote LLM via a network such as, for example, as or in message (e.g., in a payload of a message), and a request to infer whether an automated action can be performed for the message, (See SAXENA paragraph [0024], generates user interfaces and receives inputs from users to create or modify websites with automatically generated text);
providing the prompt to a model serving system for execution by the second LLM, (See SAXENA paragraph [0075], A prompt can include one or more examples of the desired output, which provides the LLM with additional information to enable the LLM to better generate output according to the desired output);
receiving, from the model serving system, a response generated by executing the second LLM on the prompt, (See SAXENA paragraph [0021], receives data associated with the website 105 and/or user input 110 and, using the website data or user input, generates a prompt 125 to a large language model (LLM) 130);
parsing the response from the model serving system, (See SAXENA paragraph [0068], Input to a language model (whether transformer-based or otherwise) typically is in the form of natural language as may be parsed into tokens), to extract an automated action to perform based on the message, (See SAXENA paragraph [0018], a website development system according to implementations herein enables LLM-based text generation for a website by automating the generation of a prompt using data retrieved or derived from the website);
comparing the automated action extracted from the response to a set of rule actions to identify whether a rule action that corresponds to the automated action is present in the set of rule actions, (See SAXENA paragraph [0056], The training data may be a subset of a larger data set…a training set, a validation (or cross-validation) set, and a testing set. The three subsets of data may be used sequentially during ML model training…The validation (or cross-validation) set may then be used as input data into the trained ML models to, e.g., measure the performance of the trained ML models and/or compare performance between them); and
responsive to identifying that a corresponding rule action is present, performing the automated action and sending an automated response to the payload recipient, (See SAXENA paragraph [0048], The text generated by the LLM can be output to the user for approval or to enable the user to request regeneration of the text…the computer system sends the user information about one or more automatically determined parameter values, enabling the user to confirm or modify the values).
Claim 12 recites the same limitations as claim 2 above. Therefore, claim 12 is
rejected based on the same reasoning.
Regarding claim 3, SAXENA taught the computer-implemented method according to claim 1 as described above.
SAXENA does not explicitly disclose wherein the one or more inputs describing the payload series to be designed describe a tone of the one or more content payloads. However, HATTANGADY teaches wherein the one or more inputs describing the payload series to be designed describe a tone of the one or more content payloads, (the output payload may include primary topics of the messages, styles of the messages, mood or tone of the messages, and/or the user's response to the messages).
It would have been obvious to one with ordinary skill in the art before the
effective filing date of the claimed invention was made to modify wherein the one or more inputs describing the payload series to be designed describe a tone of the one or more content payloads of HATTANGADY in order for generating advanced feedback for draft messages using a language model.
Claim 13 recites the same limitations as claim 3 above. Therefore, claim 13 is
rejected based on the same reasoning.
Regarding claim 4, SAXENA taught the computer-implemented method according to claim 1 as described above. SAXENA further teaches wherein the one or more inputs describing the payload series, (See SAXENA paragraph [0080], the prompt may be provided directly to the language model or LLM without requiring an API call. For example, the prompt could be sent to a remote LLM via a network such as, for example, as or in message (e.g., in a payload of a message), to be designed describe keywords, event data specifying keywords associated with the electronic document (See SAXENA paragraph [0096], product search information (search keywords, click-through events), product reviews, abandoned carts, and/or other transactional information associated with business through the e-commerce platform 700), to include and exclude in the one or more content payloads, (See SAXENA paragraph [0109], (receive customer information to complete the order such as the customer's contact information…If the customer inputs their contact information but does not proceed to payment, the e-commerce platform 700 may (e.g., via an abandoned checkout component) to transmit a message to the customer device 750 to encourage the customer to complete the checkout).
Claim 14 recites the same limitations as claim 4 above. Therefore, claim 14 is
rejected based on the same reasoning.
Regarding claim 5, SAXENA taught the computer-implemented method according to claim 1 as described above.
SAXENA does not explicitly disclose wherein the one or more inputs describing the payload series to be designed describe start and end conditions, channels to use for each message, trigger conditions, and number of content payloads in the payload series.
However, HATTANGADY teaches wherein the one or more inputs describing the payload series to be designed describe start and end conditions, channels to use for each message, trigger conditions, and number of content payloads in the payload series, (See HATTANGADY paragraph [0047], the advanced feedback features of the technology discussed herein are automatically triggered or triggered in response to the selection of a UI element or other trigger).
It would have been obvious to one with ordinary skill in the art before the
effective filing date of the claimed invention was made to modify wherein the one or more inputs describing the payload series to be designed describe start and end conditions, channels to use for each message, trigger conditions, and number of content payloads in the payload series of HATTANGADY in order for generating advanced feedback for draft messages using a language model.
Claim 15 recites the same limitations as claim 5 above. Therefore, claim 15 is
rejected based on the same reasoning.
Regarding claim 6, SAXENA taught the computer-implemented method according to claim 1 as described above. SAXENA further teaches wherein the LLM is trained based on training, (See SAXENA paragraph [0067], LLMs may be trained on a large unlabelled corpus. Some LLMs may be trained on a large multi-language), examples including past payload series associated with the publisher, (See SAXENA paragraph [0075], the prompt may optionally be processed or pre-processed into a token sequence prior to being provided as input to the LLM via its API).
Claim 16 recites the same limitations as claim 6 above. Therefore, claim 16 is
rejected based on the same reasoning.
Regarding claim 7, SAXENA taught the computer-implemented method according to claim 1 as described above. SAXENA further teaches wherein the LLM is trained based on training, (See SAXENA paragraph [0067], LLMs may be trained on a large unlabelled corpus. Some LLMs may be trained on a large multi-language), examples including past payload series associated with an industry, (See SAXENA paragraph [0028], a type of the entity that operates the webpage or an industry in which the entity operates, a product type of a product described or sold through the website, or tone for the generated text).
Claim 17 recites the same limitations as claim 7 above. Therefore, claim 17 is
rejected based on the same reasoning.
Regarding claim 8, SAXENA taught the computer-implemented method according to claim 1 as described above. SAXENA further teaches wherein the LLM is trained based on training, (See SAXENA paragraph [0067], LLMs may be trained on a large unlabelled corpus. Some LLMs may be trained on a large multi-language), examples including past payload series associated with a channel of communication, (See SAXENA paragraph [0080], The prompt generated by the computing system is provided to the language model or LLM and the output (e.g., token sequence) generated by the language model or LLM is communicated back to the computing system).
Claim 18 recites the same limitations as claim 8 above. Therefore, claim 18 is
rejected based on the same reasoning.
Regarding claim 9, SAXENA taught the computer-implemented method according to claim 1 as described above.
SAXENA does not explicitly disclose wherein the LLM is trained based on training, examples including past payload series labeled by feedback received from payload recipients of the past payload series.
However, HATTANGADY teaches wherein the LLM is trained based on training, (See HATTANGADY paragraph [0027], The size of a language model 108 may be measured by the number of parameters it has. For instance, as one example of an LLM…a large number of parameters allows the model to capture complex patterns in the training data), examples including past payload series labeled by feedback received from payload recipients of the past payload series, (See HATTANGADY [0056], The language model 108 processes the AI prompt and generates an output payload in response to the AI prompt. The output payload is received by the feedback generator 260).
It would have been obvious to one with ordinary skill in the art before the
effective filing date of the claimed invention was made to modify wherein the LLM is trained based on training, examples including past payload series labeled by feedback received from payload recipients of the past payload series. of HATTANGADY in order for generating advanced feedback for draft messages using a language model.
Claim 19 recites the same limitations as claim 9 above. Therefore, claim 19 is
rejected based on the same reasoning.
Regarding claim 10, SAXENA taught the computer-implemented method according to claim 9 as described above.
SAXENA does not explicitly disclose wherein the feedback describes whether the payload recipients of the past payload series performed conversion events.
However, HATTANGADY teaches wherein the feedback describes whether the payload recipients of the past payload series performed conversion events, (See HATTANGADY paragraph [0024], generated for the recipient(s) of the message, into an AI prompt for the language model 108. The language model 108 then generates an output payload based on the prompt. The output payload is parsed and otherwise processed to generated and display the advanced feedback).
It would have been obvious to one with ordinary skill in the art before the
effective filing date of the claimed invention was made to modify wherein the feedback describes whether the payload recipients of the past payload series performed conversion events of HATTANGADY in order for generating advanced feedback for draft messages using a language model.
Conclusions/Points of Contacts
The prior art made of record and not relied upon is considered pertinent to
applicant’s disclosure. See form PTO-892.
Gergov et al. (US 2026/0187441 A1), processing, by the trained language model, the set of words relating to a target entity comprises processing, by the trained language model, a set of keywords used in the selection process for one or more digital components relating to the target entity, to predict a set of keyword recommendations of keywords.
BEAUCHAMP (US 2024/0256762 A1) the present disclosure describes a technical solution that may be provided by a platform (e.g., SaaS platform). The platform may serve as an interface layer between a user device and the LLM, to improve accessibility to the LLM.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to MULUEMEBET GURMU whose telephone number is (571)270-7095. The examiner can normally be reached M-F 9am - 5pm.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Tony Mahmoudi can be reached at 5712724078. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/MULUEMEBET GURMU/Primary Examiner, Art Unit 2163
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