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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 5/22/2026 has been entered.
Status of claims
Claims 1, 11 and 20 are amended. Claims 1-20 are presented for examination.
Response to Arguments
Claim Rejection - 35 U.S.C. §101
In light of amendments and affidavit rejection under 35 U.S.C. §101 is withdrawn.
Claim Rejections - 35 USC § 103
Applicant’s arguments with respect to claims 1-20 have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
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.
And
KSR, 550 U.S. at 418, 82 USPQ2d at 1396. Exemplary rationales that may support a conclusion of obviousness include:
(A) Combining prior art elements according to known methods to yield predictable results;
(B) Simple substitution of one known element for another to obtain predictable results;
(C) Use of known technique to improve similar devices (methods, or products) in the same way;
(D) Applying a known technique to a known device (method, or product) ready for improvement to yield predictable results;
(E) "Obvious to try" – choosing from a finite number of identified, predictable solutions, with a reasonable expectation of success;
(F) Known work in one field of endeavor may prompt variations of it for use in either the same field or a different one based on design incentives or other market forces if the variations are predictable to one of ordinary skill in the art;
(G) Some teaching, suggestion, or motivation in the prior art that would have led one of ordinary skill to modify the prior art reference or to combine prior art reference teachings to arrive at the claimed invention.
See MPEP § 2143 for a discussion of the rationales listed above along with examples illustrating how the cited rationales may be used to support a finding of obviousness. See also MPEP § 2144 - § 2144.09 for additional guidance regarding support for obviousness determination.
Claims 1-8, 10-18 and 20 are rejected under 35 U.S.C. 103 as being unpatentable over He (CTRLSUM: Towards Generic Controllable Text Summarization - 2022.emnlp-main.396.pdf) and further in view of Wu ( US 20240143941) and further in view of He ( US 20220067284 ) hereinafter He’284
Regarding claim 1, He teach a system comprising: a processing unit configured to execute instructions to cause the system to: tokenize a body of text into numerical tokens ( tokenize the document, 2.2; also A.1 – tokenization) ; generate embedding vectors representing the tokens, the embedding vectors capturing semantic relationships between tokens (BERT based training at inference and/or utilize any sequence to sequence model for the extraction, Under 2.2-2.3; BERT is a well-known model to capture the sematic relationship between token) ; input the embedding vectors into a trained machine learning model to generate an original list of keywords summarizing the body of text based on the semantic relationships (keyword based on the guiding token and keyword +prompt for summarization, Under 3.2; utilize any sequence to sequence model for keyword extraction, Under 2.2; where CTRLSUM is our fine-tuned version of the pretrained BARTLARGE model (Lewis et al., 2020)., Under 4.1- BART intherently captures the sematic relationships ) generate a prompt to a large language model (LLM) for generating a subject line, the prompt including a list of keywords based on the original list of keywords ( we design a guiding token sequence for each task, which is used as both the keyword input and the decoder prompt., Under 3.2, Table 28-29; while prompt and keyword can be viewed as unified “prompt” which are both guiding tokens with different usage., Page 5883), wherein the prompt excludes the body of text such that a number of tokens provided to the LLM is reduced relative to the body of text ( gcontrol can be used to constrain the length of the output, Table 23-24; also the prompt is generated from keyword and prompt from guiding token which does not include full text from article) ; and obtain, from the LLM responsive to the prompt, at least one generated subject line corresponding to the body of text ( generate a summary from guiding tokens used as prompts and keywords, Table 28-29, Under4.5; here the prompt does not include the full body of text instead the way to generate summarization is either solely based on guiding tokens defining the keywords or keyword+prompt = prompt , Under 6. Conclusion)
While, He's body of text is an article and summary can be viewed as a subject line since the process is same if the article is a email body of text, one line summary with the length control factor can be taken as a subject line but He does not teach the concept of email and a prompt generating a subject line
However, Wu teaches the concept of email and the subject line and the prompt generating a subject line ( using a generative model to generate a subject line using keyword, Para 0068; just using keywords and NER, Para 0068; generative the subject line, Fig 3, Fig 7)
He already has a process of generating keywords based on the document/article/body of text and based on those keywords generate a summary of a particular length. Wu has an improved way of generating subject line using the keyword and it would have been obvious to POSITA having the teachings of He to further modify with the concept Wu to using He concept to generate a subject line in place of summary to provide improved efficiency in the subject generation system (Para 0034, Wu)
He modified by Wu does not explicitly teach a chosen list of keywords based on the original list of keywords
However He’284 teaches a chosen list of keywords based on the original list of keywords (the user 150 can also edit the customized keywords, which allows for more flexible customized summarization without the user manually editing the summary directly, user input of a control token sequence and/or one or more control parameters relating to a characteristic of the summary to be generated may be received to modify the set of keywords into a customized set of keywords, e.g., via the control center 232 in FIG. 2. During inference time, the user 150 may provide different configurations of control tokens 232 reflecting keyword control to entity and length of the summary. Para 0023, 0045-0046)
It would have been obvious to a POSITA having the teachings of He and Wu to further incorporate the concept of He’284, thereby allows for more flexible customized summarization without the user manually editing the summary directly ( Para 0023, He’284)
Regarding claim 2, He’284 as above in claim 1, teach , wherein the processing unit is further configured to provide the original list of keywords to a user device, wherein the user device is configured to present the original list of keywords as suggestions (control section present user with keyword user can edit the keyword or modify the keyword, Para 0045-0046, Fug 1b, He’284)
Regarding claim 3,He’284 as above in claim 3, wherein the processing unit is further configured to receive the chosen list of keywords from the user device ( user can edit or modify the keyword, Para 0023, 0045-0046, Fig 1)
Regarding claim 4, He’284 as above in claim 4, teach wherein the chosen list of keywords is the original list of keywords or includes at least one of the keywords from the original list of keywords ( the keywords provide a generic interface to control multiple aspects of summaries, which allows the user to optionally rely on automatically extracted keywords, user provided keywords, or a combination of both, Para 0026)
Regarding claim 5,He modified by Wu and He’284 as above in claim 1, teach :provide the at least one generated subject line to a user device for presentation; receive a revised list of keywords, based on the chosen list of keywords, from the user device; generate another prompt to the LLM for generating another subject line, the prompt including the revised list of keywords, wherein the prompt does not include the body of text; and obtain, from the LLM, another at least one generated subject line ( ( Fig 2, Subject Line– user can change the keyword) ; generate another prompt to the LLM for generating another subject line, the prompt including the revised list of keywords, wherein the prompt does not include the body of text; and obtain, from the LLM, another at least one generated subject line (user can change the keyword and the updated subject will be generated, Fig 2, Wu; user can edit the keyword, Para 0023, He’284)
Regarding claim 6, Wu as above in claim 5, teaches wherein the processing unit is further configured to :save the at least one generated subject line in memory; and provide the at least one generated subject line and the other at least one generated subject line to the user device for presentation ( Fig 9-12, Wu multiple subject lines)
Regarding claim 7, Wu as above in claim 5, wherein the processing unit is further configured to: provide the chosen list of keywords with the at least one generated subject line to the user device for presentation ( Fig 9, Wu; user can edit the keyword, Para 0023-0026, He’284)
Regarding claim 8, He modified by Wu and He’284 as above in claim 1, teach wherein the processing unit is further configured to: provide the at least one generated subject line to a user device for presentation; provide feedback options for the at least one generated subject line to the user device for presentation; and receive feedback from the user device ( user can modify the keywords and/or blacklist etc., and generate an updated subject, Fig 9; customize keyword to generate customized summary and keyword can be modified, Para 0023-0026, 0045-0046, He’284)
Regarding claim 10, He as above in claim 1, teach wherein the trained machine learning model is a text summarizer that uses natural language processing (NLP) techniques ( CTRLsum, Abstract)
Regarding claim 11, rejections analogous to claim 1, are applicable.
Regarding claim 12, rejections analogous to claim 2, are applicable.
Regarding claim 13, rejections analogous to claim 3, are applicable.
Regarding claim 14, rejections analogous to claim 4, are applicable.
Regarding claim 15, rejections analogous to claim 5, are applicable.
Regarding claim 16, rejections analogous to claim 6, are applicable.
Regarding claim 17, rejections analogous to claim 7, are applicable.
Regarding claim 18, rejections analogous to claim 8, are applicable.
Regarding claim 20, rejections analogous to claim 1, are applicable.
Claim 9 and 19 are rejected under 35 U.S.C. 103 as being unpatentable over and further in view of He (CTRLSUM: Towards Generic Controllable Text Summarization - 2022.emnlp-main.396.pdf) and further in view of Wu ( US 20240143941) and further inview of He ( US 20220067284 ) hereinafter He’284 and Agrawal ( US 20240312087)
Regarding claim 9, He modified by Wu and He’284 does not teach wherein the prompt further includes one or more of historical email data, business information, and user demographic information associated with a user.
In the same field of endeavor Agrawal teaches wherein the prompt further includes one or more of historical email data, business information, and user demographic information associated with a user (For instance, to generate a subject line for an email on a certain occasion, the following sentence prompt is provided to a generative model: “generate one short, exciting email subject line from {brand} on {occasion}.” Each generative model of the content generation apparatus 110 is fine-tuned with multiple (e.g., 100) examples, Para 0036)
It would have been obvious having the teachings of He and Wu and H’284 to further include the concept of Agrawal to make the subject line more consistent with brands/organizations ( Abstract, Agrawal)
Regarding claim 19, arguments analogous to claim 9, are applicable.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Mohammad (US 20250139382)
Mishra (US 20250094711) teaches obtain an original list of keywords based on a body of text using a trained machine learning model ( obtain a keyword/prompt word for text segments, Para 0147, 0154)
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/Richa Sonifrank/Primary Examiner, Art Unit 2654
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