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 .
This action is in response to the communication filed on May 29, 2024.
Claims 1-23 are pending in this action.
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.
Claim(s) 1-7, 9-16, and 18 is/are rejected under 35 U.S.C. 103 as being unpatentable over Whitmore et al. (US 2024/0296275) in view of Botea et al. (US 11,561,964).
As per claim 1, Whitmore discloses, a computer-implemented method comprising:
receiving a text input from a computing device, wherein the text input comprises a plurality of words (Paragraphs 0034-0035, “text strings that convey a purpose of the message”).
Whitmore does not explicitly disclose, but Botea discloses, accessing, from a server computer, common knowledge associated with a data store (Abstract, col. 4, lines 6-23).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filling date of the claimed invention to modify the invention of Whitmore by including a server computer and common knowledge associate with data store as taught by Botea for the advantage of a response may be provided to one or more queries based upon information accessed from the knowledge domain (Summary of the invention).
generating, using a generative artificial intelligence model, contextual features in a query associated with the text input, wherein the generative artificial intelligence model has been trained to generate the contextual features in the query based on the common knowledge associated with the data store (Paragraphs 0049-0053, generative AI model, text query);
generating, using the generative artificial intelligence model, a text output using the contextual features in the query associated with the text input (Paragraphs 0049-0053);
sending, to the computing device, instructions for presenting a user interface comprising the text output (Paragraph 0056, “a notification is displayed requesting a confirmation from the user to send the message”).
As per claim 2, Whitmore discloses, further comprising incorporating the contextual features of the query on a plurality of terms used in the data store when composing the query (Paragraphs 0044, 0049-0051, and 0053).
As per claim 3, Whitmore discloses, further comprising: selecting a brand tone setting from a plurality of tone profiles in the data store; and adjusting a tone of the text output based on the brand tone setting that was selected (Paragraphs 0055 and 0060).
As per claim 4, Whitmore discloses, further comprising: accessing, from the server computer, a document template and a document type relevant to a goal; and generating any of sales emails, marketing announcements, or recruiting outreach emails based on the document template, the document type, and the text output associated with the text input from the computing device (Paragraphs 0036 and 0056).
As per claim 5, Whitmore discloses, wherein the generative artificial intelligence model comprises a large language model based on a multi-class neural network (Paragraph 0027, LLM).
As per claim 6, Whitmore discloses, further comprising modifying, using the multi-class neural network, the text output in a plurality of attributes (Paragraphs 0039-0041).
As per claim 7, Whitmore disclose, wherein the plurality of attributes comprises two or more of correctness, clarity, length, simplification, diversity, sensitivity, and tone Paragraphs 0039-0041 and 0055).
As per claim 9, Whitmore discloses, wherein the generative artificial intelligence model comprises a similarity function to map the input text to the contextual features in the query associated with the text input (Paragraph 0067).
As per claim 10, Whitmore discloses, a computer system, comprising:
one or more processors (; and
one or more non-transitory computer-readable storage media coupled to the one or more processors and storing instructions which, when executed by the one or more processors (Paragraphs 0125-0126), cause the system to execute:
receiving a text input from a computing device, wherein the text input comprises a plurality of words (Paragraphs 0034-0035, “text strings that convey a purpose of the message”).
Whitmore does not explicitly disclose, but Botea discloses, accessing, from a server computer, common knowledge associated with a data store (Abstract, col. 4, lines 6-23).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filling date of the claimed invention to modify the invention of Whitmore by including a server computer and common knowledge associate with data store as taught by Botea for the advantage of a response may be provided to one or more queries based upon information accessed from the knowledge domain (Summary of the invention).
generating, using a generative artificial intelligence model, contextual features in a query associated with the text input, wherein the generative artificial intelligence model has been trained to generate the contextual features in the query based on the common knowledge associated with the data store (Paragraphs 0049-0053, generative AI model, text query);
generating, using the generative artificial intelligence model, a text output using the contextual features in the query associated with the text input (Paragraphs 0049-0053);
sending, to the computing device, instructions for presenting a user interface comprising the text output (Paragraph 0056, “a notification is displayed requesting a confirmation from the user to send the message”).
As per claims 11-16 and 18, they are analyzed and thus rejected for the same reasons set forth in the rejection of claims 1-7 and 9-10, because the corresponding claims have similar limitations.
Claim(s) 8 and 17 is/are rejected under 35 U.S.C. 103 as being unpatentable over Whitmore et al. (US 2024/0296275) in view of Botea et al. (US 11,561,964) as applied to claims 6 and 15 above, and further in view of Saha et al. (US 11,227,119).
Whitmore in view of Botea do not disclose, but Saha discloses, further comprising using the multi-class neural network to generate one or more text suggestions comprising all of a grammatic error correction (GEC) to correct a grammatic error in the text input (col. 10, lines 34-47); modifying the text input by merging or splitting one or more words in the text input; modifying the text input by expanding or compressing one or more words in the text input; modifying the text input by simplifying or complexifying one or more words in the text input; modifying the text input by paraphrasing one or more words in the text input; modifying the text input by de-toxifying one or more words in the text input; and modifying the text input by using formal or informal terms for one or more words in the text input (col. 5, line 60-col. 6, line 47).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filling date of the claimed invention to modify the invention of Whitmore in view of Botea by including editing or modifying the text input as taught by Saha for the advantage of a word processing system, comprising a computerized input text, computerized attributes, and computer-implemented word processor, can perform operations of method (Abstract).
Allowable Subject Matter
Claims 19-23 are allowed over the prior art of record.
The following is an examiner’s statement of reasons for allowance:
The prior art of record fails to teach or fairly suggest the claimed combinations of features. The prior art of record particularly fails to teach or fairly suggest in combinations with other limitations, “receiving at the text processor, from a client computing device executing a browser and a text processing extension that extends functions of the browser, a text input from a computing device, wherein the text input comprises a plurality of words of a web page rendered by the browser; distribute sentences of the text input to a first check, a second check, and a third check that execute in parallel, the first check being configured to check grammar, the second check being configured to detect a tone, and the third check being configured to detect at least one of entities, acronyms, or keywords”.
Any comments considered necessary by applicant must be submitted no later than the payment of the issue fee and, to avoid processing delays, should preferably accompany the issue fee. Such submissions should be clearly labeled “Comments on Statement of Reasons for Allowance.”
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Shazeer et al. (US 11,960,848) discloses, machine-learned language models which generate intermediate textual analysis in service of contextual text generation.
Murphy et al. (US 2024/0291853) discloses, threat mitigation system and method.
Contact Information
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Abul K. Azad whose telephone number is (571) 272-7599. If attempts to reach the examiner by telephone are unsuccessful, the examiner's supervisor, Bhavesh Mehta, can be reached at (571) 272-7453.
Any response to this action should be mailed to:
Commissioner for Patents
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Alexandria, VA 22313-1450
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February 4, 2026
/ABUL K AZAD/Primary Examiner, Art Unit 2656