DETAILED ACTION
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
This office action is in responsive to communication(s): original application filed on 08/12/2024, said application claims a priority filing date of 08/11/2023.
Claims 1-20 are pending. Claims 1, 13 and 17 are independent.
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale or otherwise available to the public before the effective filing date of the claimed invention.
Claims 1-20 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Shevchenko et al. (US Patent 10,764,534; hereinafter “Shev”).
In regard to independent claims 1, 13 and 17, Shev teaches A method of electronic communication assistance comprising: receiving a partial electronic communication at an artificial intelligence assistant computing facility from a first electronic identifier associated with a first user, the partial electronic communication comprising a communication content associated with the first electronic identifier associated with the first user and a second electronic identifier associated with a second user; extracting a communication context from the partial electronic communication; encoding the partial electronic communication for processing, creating an encoded partial electronic communication; retrieving from a communication profile database a first communication profile for the first user using the first electronic identifier associated with the first user, wherein the first communication profile comprises a first user communication attribute; retrieving from the communication profile database a second communication profile for the second user using the second electronic identifier associated with the second user, wherein the second communication profile comprises a second user communication attribute; processing the encoded partial electronic communication with a processor to generate a compositional change for the communication content of the partial electronic communication using at least one of the communication context, the first user communication attribute, or the second user communication attribute to generate the compositional change; and generating a changed electronic communication from the partial electronic communication and the compositional change (column 1, lines 34-62; column 3, lines 9-14).
In regard to dependent claim 2, Shev teaches transmitting the changed electronic communication to the first electronic identifier associated with the first user, and/or transmitting the changed electronic communication to the second electronic identifier associated with the second user (column 4, lines 7-11).
In regard to dependent claims 3 and 14, Shev teaches the compositional change is derived from representations of previous content and context from a plurality of user profiles stored in the communication profile database, which are like at least one of the first communication profile or the second communication profile (claim 2).
In regard to dependent claim 4, Shev teaches the processor is trained on large-scale data mixed with prior communication and effective communications from a plurality of user profiles (claim 3).
In regard to dependent claim 5, Shev teaches the processor performs operations based on instructions corresponding to a machine learning model for creating the compositional change (column 2, lines 28-31).
In regard to dependent claim 6, Shev teaches the compositional change is an auto-generated textual completion; the auto-generated textual completion being a phrasal completion, and wherein the processor is configured to generate the compositional change by optimizing generated language as determined by the processor from the second user communication attribute (column 2, lines 31-36).
In regard to dependent claim 7, Shev teaches the processor is configured to generate the compositional change by replicating a communication style of the first user as determined by the processor from the first user communication attribute (claim 6).
In regard to dependent claims 8 and 15, Shev teaches the partial electronic communication includes a communication goal, and the processor is configured to generate the compositional change by optimizing for impact and effectiveness of generated language with respect to the communication goal (column 2, lines 39-43).
In regard to dependent claims 9, 16 and 18, Shev teaches the processor is configured to generate the compositional change further using a communication template selected from a plurality of communication templates comprising at least one of prepared text or placeholder locations for defining structural elements for user completion (column 2, lines 43-48).
In regard to dependent claim 10, Shev teaches the processor is configured to select the communication template using a machine learning model to find a most effective communication template based at least in part on the communication content (column 2, lines 48-52).
In regard to dependent claims 11 and 19, Shev teaches the plurality of communication templates includes at least one automatically generated template generated by the processor (column 2, lines 52-54).
In regard to dependent claims 12 and 20, Shev teaches the processor is configured to select the communication template by using the machine learning model to score the plurality of communication templates based at least in part on the communication content, first user communication attribute, second user communication attribute, or communication context (column 2, lines 54-62).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Bender et al. U.S. Publication 2021/0134299 - Teaches recommending compositional change based on 1st user and 2nd user’s communication context and their profile.
Pandit et al. U.S. Publication 2022/0012434 - Teaches a writing assistant that edit draft based on user’s profile.
Shevchenko et al. (US Patent 10,594,757) – Teaches artificial intelligence communication assistance for augmenting communication.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to REZA NABI whose telephone number is (571)270-7592. The examiner can normally be reached Monday - Friday 8:00 am - 5:00 pm EST.
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/Reza Nabi/
Primary Examiner, Art Unit 2174
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