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
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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 4/3/2026 has been entered.
Response to Amendment
Claims 1 and 17 are amended. Claims 1-20 are presented for examination.
Response to Arguments
Claim Rejections 35 U.S.C. §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.
Claim 1-3 and 17-18 are rejected under 35 U.S.C. 103 as being unpatentable over Srinivasan ( US 20230196014) and further in view of Mace ( US 20240070270) and further in view of Busch( US 20210224649 )
Regarding claim 1, Srinivasan teaches a computer-implemented method, comprising: providing input to a trained large language model, the input comprising a set of curated examples associated with one or more writing style choices, the set of curated examples having a first size, and a task-specific prompt that tunes a subset of parameters of the trained large language model (input the writing style of the author, Para 0005) ; generating, using the model , a rewriting corpus according to the one or more writing style choices, the rewriting corpus having a second size (two copies of the pretrained model ( noisy vs not noisy, Para 0005, 0042) , the one or more writing style choices including at least one of a style ( linguistic style of the author, Para 0046-0047) ; storing the rewriting corpus in memory; and training, by one or more processors using at least a subset of the stored rewriting corpus ( training the model with authors writing, Fig 6, Para 0070) , a text rewriting model that is configured to generate vivid textual information in response to a user input in the interactive domain, according to one or more specific ones of the writing style choices ( generate responses, Fig 7)
Srinivasan does not teach a task-specific prompt that tunes a subset of parameters of the trained large language model and generating, using the trained large language model, that is adapted by the task-specific prompt, a rewriting corpus according to the one or more writing style choices, the rewriting corpus having a second size two or more orders of magnitude larger than the first size; the one or more writing style choices including at least one of a tone, a conversion, an application context associated with an interactive domain, or a conversation type
However Mace teaches a task-specific prompt that tunes a subset of parameters of the trained large language model ( examples from the initial training set 111 are used as shots in a prompt 113 for generating the corresponding user query, Para 0063, 0066) prompt, and generating, using the trained large language model, that is adapted by the task-specific prompt, a rewriting corpus according to the one or more writing style choices ( by varying the shots included in the prompts 113, a plurality of different-styled user queries that can be generated that correspond to the same underlying KQL. This is on the basis that the LLM 114 will respond with different variations (e.g. different syntactic structure or writing style) of the user queries dependent on the shots included in the prompt. The result of this part of the process is a corpus comprising KQL queries, descriptions and corresponding user queries, Para 0063) , the one or more writing style choices including at least one of a tone, a conversion, an application context associated with an interactive domain, or a conversation type ( examples from the initial training set 111 are used as shots in a prompt 113 for generating the corresponding user query., Para 0063; wherein the initial training comprises metadata ( length of the sentence or type ) and/or context)
Srinivasan has a base concept of training a model with a particular writing style which is created by manipulation of the dataset and using the writing style to generate stylized writing. Srinivasan differed by the claimed invention based on the concept of using a LLM model to create the writing corpus. Mace teaches this concept and it would have been obvious to do so because LLM is able to augment the data to increase the smaller data into a huge dataset which can be used further to generate a particular writing style ( Para 0063, Mace)
Srinivasan modified by Mace does not explicitly teach the rewriting corpus having a second size two or more orders of magnitude larger than the first size
However, Busch teaches corpus having a second size two or more orders of magnitude larger than the first size ( the identified target sample data is processed with the augmentation tools (step 750). The processing by the augmentation tools generates a series of augmented target signal samples that can be collected as an extended target signal sample set. In many embodiments, the quantity of augmented target signal samples can be orders of magnitude larger than the original target signal samples, Para 0067, also from 0044)
It would have been obvious having the teachings of Srinivasan and Mace for Mace to generate the dataset set orders of magnitude larger since neural networks/generative model and/or llm model is known to do that to create a large dataset if the dataset is smaller for further training ( Para 0044, 0067, Busch)
Regarding claim 2, Srinivasan as above in claim 1, teaches wherein training the text rewriting model includes personalization according to one or more personalized inputs associated with at least one user profile ( target author, Fig 6)
Regarding claim 3, Srinivasan as above in claim 2, teaches wherein the training comprises updating a baseline version of the text rewriting model using the one or more personalized inputs as additional training data for the text rewriting model ( fig 2)
Regarding claim 17, arguments analogous to claim 1, are applicable. In addition, Srinivasan teaches A computing system, comprising: memory configured to store a rewriting corpus; and one or more processors operatively coupled to the memory, the one or more processors being configured to perform the method of claim 1 ( Fig 2, Fig 3)
Regarding claim 18, arguments analogous to claim 2, are applicable.
Claims 4-10and 19-20 are rejected under 35 U.S.C. 103 as being unpatentable over Srinivasan ( US 20230196014) and further in view of Mace ( US 20240070270) and further in view of Busch( US 20210224649 ) and further in view Luzhnica ( US 11516158)
Regarding claim 4, Srinivasan modified by Mace and Busch as above in claim 2, does not teach wherein the one or more personalized inputs includes conversational context information about a conversation a user has with another person
However Luzhnica teaches wherein the one or more personalized inputs includes conversational context information about a conversation a user has with another person (Short inputs #520, #522, #524, #526, and #528 allow the user to input recipient email address, name, title, associated-company/organization, and location information, which the system can use as instructional prompts to ensure that messages are personalized for the audience member and/or system-mandated instructions, e.g., with respect to the email address, Col 120, line 55-60)
It would have been obvious having the teachings of Srinivasan to further include the concept of styling choice as described in Luzhnica before effective filing date because by doing so the user gets more option of style their responses and make the system more flexible ( Col 9, line 50-60, Luzhnica)
Regarding claim 5, Srinivasan modified by Mace and Busch as above in claim 1, does not teach wherein the tone includes at least one of casual, formal, humorous, vivid or exaggerated (Note: The examiner did not rely on the tone as a styling choice in claim 1; therefore, under the Broadest Reasonable Interpretation (BRI), this styling did not occur)
However, Luzhnica teaches wherein the tone includes at least one of casual, formal, humorous, vivid or exaggerated (formal tone, Col 122, line 40-50)
It would have been obvious having the teachings of Srinivasan to further include the concept of styling
choice as described in Luzhnica before effective filing date because by doing so the user gets more option
of style their responses and make the system more flexible ( Col 9, line 50-60, Luzhnica)
Regarding claim 6, Srinivasan modified by Mace and Busch as above in claim 1, does not teach wherein the conversion includes one of expand an initial amount of text from the user input, abbreviate the initial amount of text, or emojify (Note: The examiner did not rely on the conversation as a styling choice in claim 1; therefore, under the Broadest Reasonable Interpretation (BRI), this styling did not occur)
However, Luzhnica teaches wherein the conversion includes one of expand an initial amount of text from the user input, abbreviate the initial amount of text, or emojify ( a text string from the user input an “emoji” setting, #612, which favors the use of emojis as semantic elements (using any suitable setting, such as selection of initial training set data for the neural network(s) or selection engine to draw from, as noted in FIG. 5),, Col 122, line 25-30; semantic emoji matching, Col 20, line 44-45; Col 27, line 27-30 , semantic element including emoji , Col 27, line 43, Col 122, line 25-30)
It would have been obvious having the teachings of Srinivasan to further include the concept of styling choice as described in Luzhnica before effective filing date because by doing so the user gets more option of style their responses and make the system more flexible ( Col 9, line 50-60, Luzhnica)
Regarding claim 7, Srinivasan modified by Mace and Busch as above in claim 1, does not teach where the application context is associated with a chat domain, a social media domain, an email domain, a word processing domain or a presentation domain (Note: The examiner did not rely on the application context as a styling choice in claim 1; therefore, under the Broadest Reasonable Interpretation (BRI), this styling did not occur)
However, Luzhnica teaches where the application context is associated with a chat domain, a social media domain, an email domain, a word processing domain or a presentation domain ( email domain, SMS, blog etc. , Col 107, line 25-35)
It would have been obvious having the teachings of Srinivasan to further include the concept of styling choice as described in Luzhnica before effective filing date because by doing so the user gets more option of style their responses and make the system more flexible ( Col 9, line 50-60, Luzhnica)
Regarding claim 8, Srinivasan modified by Mace and Busch as above in claim 1, does not teach teaches wherein the conversation type is one of a family conversation, a friend’s conversation, a dialogue, a colleague interaction, or a business communication
However, Luzhnica teaches wherein the conversation type is one of a family conversation, a friend’s conversation, a dialogue, a colleague interaction, or a business communication ( formal, informal, family member, Col 86, line 50-67; Col 122, line 40-50)
It would have been obvious having the teachings of Srinivasan to further include the concept of styling choice as described in Luzhnica before effective filing date because by doing so the user gets more option of style their responses and make the system more flexible ( Col 9, line 50-60, Luzhnica)
Regarding claim 9, Srinivasan modified by Mace and Busch as above in claim 1, does not teach wherein the text rewriting model is further trained to generate graphical indicia to emojify the vivid textual information
However, Luzhnica teaches wherein the text rewriting model is further trained to generate graphical indicia to emojify the vivid textual information ( use of emoji sematic element, setting initial training data for emoji, Col 122, line 25-30)
It would have been obvious having the teachings of Srinivasan to further include the concept of styling choice as described in Luzhnica before effective filing date because by doing so the user gets more option of style their responses and make the system more flexible ( Col 9, line 50-60, Luzhnica)
Regarding claim 10, Luzhnica as above in claim 9, teaches wherein the trained text rewriting model is configured to generate one or more patterns of the graphical indicia in response to a concept prediction model or rule-based keyword matching ( semantic emoji matching, Col 20, line 44-45; Col 27, line 27-30 , semantic element including emoji , Col 27, line 43, Col 122, line 25-30)
Regarding claim 19, arguments analogous to claim 3, are applicable.
Regarding claim 20, arguments analogous to claim 9, are applicable.
Claims 12-15 are rejected under 35 U.S.C. 103 as being unpatentable over Srinivasan ( US 20230196014) and further in view of Mace ( US 20240070270) and further in view of Busch( US 20210224649 ) and further in view Luzhnica ( US 11516158) and further in view of Farri( US 20180373700)
Regarding claim 12, Srinivasan modified by Mace and Busch and Luzhnica as above in claim 9, does not teach does not explicitly teach wherein the trained text rewriting model is configured to generate one or more patterns of the graphical indicia, the one or more patterns including a beat pattern or an append pattern
However, Farri teaches wherein the trained text rewriting model is configured to generate one or more patterns of the graphical indicia, the one or more patterns including a beat pattern or an append pattern ( fig 4, fig 3)
It would have been obvious having the teachings of Srinivasan modified by Mace and Busch and Luzhnica to further include the concept of Farri before effective filing date to make it easier for user to understand ( Para 0002, Farri)
Regarding claim 13, Luzhnica as above in claim 9, teaches generating emoji ( emoji is two dimensional pattern) ) however Srinivasan modified by Mace and Busch and Luzhnica does not explicitly teaches wherein the trained text rewriting model is configured to generate a two-dimensional visualization pattern including at least one emoji or other graphical indicia
In the same field of endeavor Farri teaches wherein the trained text rewriting model is configured to generate a two-dimensional visualization pattern including at least one emoji or other graphical indicia ( fig 3, Fig 4 – emoticon images )
It would have been obvious having the teachings of Srinivasan modified by Mace and Busch and Luzhnica to further include the concept of Farri before effective filing date to make it easier for user to understand ( Para 0002, Farri)
Regarding claim 14, Luzhnica as above in claim 9, teaches the concept of generating data using emoji however Srinivasan modified by Mace and Busch and Luzhnica does not explicitly teach , wherein the graphical indicia to emojify the vivid textual information is generated with semantic augmentation
In the same field of endeavor Farri teaches wherein the graphical indicia to emojify the vivid textual information is generated with semantic augmentation ( raining with the collaborative knowledge bases 144, English lexical databases 146 and/or an emoticon dictionary 148 orients nomenclature of the paraphrased sentence to that of a patient, Para 0019) Textual entailment includes creating vector space representations of the selected sentence/partially paraphrased sentence and recognizes if a sentence in a pair of sentences or conjunctive clauses has a textual entailment in either direction. The paraphrasing can include emoticons, Para 0025-0026)
It would have been obvious having the teachings of Srinivasan modified by Mace and Busch and Luzhnica to further include the concept of Farri before effective filing date to use the emoji be keep the semantic meaning intact
Regarding claim 15, Srinivasan modified by Mace and Busch and Luzhnica as above in claim 9, does not teach, does not explicitly teach , wherein training the text rewriting model to generate the graphical indicia includes generating a set of emojify annotations, and then training the text rewriting model based on the set of emojify annotations
However, Farri teaches wherein training the text rewriting model to generate the graphical indicia includes generating a set of emojify annotations, and then training the text rewriting model based on the set of emojify annotations (raining with the collaborative knowledge bases 144, English lexical databases 146 and/or an emoticon dictionary 148 orients nomenclature of the paraphrased sentence to that of a patient; feedback to retrain the model, Fig 2, Para 0025-0028)
It would have been obvious having the teachings of Srinivasan modified by Mace and Busch and Luzhnica to further include the concept of Farri before effective filing date to adapt the model ( Para 0027, Farri)
Allowable Subject Matter
Claims 11 and 16 is being objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
US 20240256792 discloses based on the textual description and user attribute generate a user specific textual description
US 20240273793 discloses Emojis: The software may use compressed language symbols (including selection of emojis and/or memes) to convey information compactly, quickly, efficiently. Text content may be processed into compressed form such as visual symbols, pictograms, emojis, or automatically generated images audio or video
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Richa Sonifrank whose telephone number is (571)272-5357. The examiner can normally be reached M-T 7AM - 5:30PM.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Phan Hai can be reached at (571)272-6338. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/Richa Sonifrank/Primary Examiner, Art Unit 2654
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