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 .
Claim Rejections - 35 USC § 102
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 (i.e., changing from AIA to pre-AIA ) 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.
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)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claim(s) 1-14, 16, 19, and 20 with an earliest effective filing date of 6/4/24 is/are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Daftary et al. (U.S. Publication No. 2025/0200945 filed on 12/18/23).
With respect to claim 1, the Daftary reference teaches a system comprising one or more processors and at least one non-transitory memory having instructions that, when executed by the one or more processors, cause the one or more processors to:
receive a subject sequence data object (text data 106 is received [paragraph 45 and Figure 3]) and one or more subject contextual data objects associated therewith (various contextual data about the text data is determined [paragraphs 46-51]);
access a contextually augmented transformer neural network comprising an attention mechanism (the transformer layer of a neural network includes a self-attention mechanism [paragraphs 45 and 52]), wherein the attention mechanism comprises a queries matrix, a keys matrix, and a values matrix (the attention mechanism include query, key, and value vectors [paragraph 52]);
ingest the subject sequence data object and the one or more subject contextual data objects into the attention mechanism (the text data and contextual data are input to the transformer [paragraphs 48 and 51-53]);
embed the one or more subject contextual data objects in the attention mechanism (tokens are embedded [paragraphs 52-53]); and
generate, using the contextually augmented transformer neural network, an output associated with the subject sequence data object (the content is analyzed to generate various forms of output [paragraphs 104-110]).
With respect to claim 2, the Daftary reference teaches all the limitations of claim 1 as described above. In addition, the Daftary reference teaches that the instructions, that when executed by the one or more processors, further cause the one or more processors to: generate, based at least in part on the output, an electronic communication configured for display via a display device; and transmit the electronic communication to a computing device associated with a subject entity associated with the subject sequence data object (the system communicates with a user’s device which displays the generated content [paragraphs 104-110]).
With respect to claim 3, the Daftary reference teaches all the limitations of claim 1 as described above. Additionally, the Daftary reference teaches that embedding the one or more subject contextual data objects in the attention mechanism comprises: determining respective relevancies, based at least in part on the one or more subject contextual data objects, of one or more elements k in the keys matrix to each element q of the queries matrix (weights are determined using the query and key vectors [paragraph 52]).
With respect to claim 4, the Daftary reference teaches all the limitations of claim 3 as described above. In addition, the Daftary reference teaches that determining the respective relevancies comprises: generating weights of one or more elements of the attention mechanism based at least in part on the one or more subject contextual data objects; and applying the weights to the one or more elements of the attention mechanism (weights are determined using the query and key vectors [paragraph 52]).
With respect to claim 5, the Daftary reference teaches all the limitations of claim 1 as described above. Additionally, the Daftary reference teaches that the instructions, that when executed by the one or more processors, further cause the one or more processors to: receive a plurality of training sequence data objects (training data is received [paragraph 112]); receive a plurality of one or more training contextual data objects associated with a respective one or more of the plurality of the training sequence data objects (training data includes content data and contextual data [paragraph 112]); receive output labels for each of the plurality of the training sequence data objects and respective one or more training contextual data objects (a numerical score is calculated for the training data [paragraphs 112-114]); and train the contextually augmented transformer neural network with the plurality of the training sequence data objects, the one or more training contextual data objects, and the output labels (the model is trained based on the training data set [paragraphs 112-114]).
With respect to claim 6, the Daftary reference teaches all the limitations of claim 1 as described above. In addition, the Daftary reference teaches that the subject sequence data object comprises one or more tokens derived from sequential data, and positional encodings indicating the one or more tokens' respective positions within the sequential data (tokens are derived from data and their position is encoded in the sequenced data [paragraphs 47, 51, and 52]).
With respect to claim 7, the Daftary reference teaches all the limitations of claim 1 as described above. Additionally, the Daftary reference teaches that the subject sequence data object is derived from one or more transactional records (the content can include a user’s social media feed [paragraph 106]).
With respect to claim 8, the Daftary reference teaches all the limitations of claim 1 as described above. In addition, the Daftary reference teaches that the subject sequence data object is derived from one or more of natural language text, an image, or an audio file (the content includes image and text data [paragraph 31]).
With respect to claim 9, the Daftary reference teaches all the limitations of claim 1 as described above. Additionally, the Daftary reference teaches that the one or more subject contextual data objects comprise one or more subsequence contexts (contextual data includes sequential data [paragraphs 47, 51, and 52]).
With respect to claim 10, the Daftary reference teaches all the limitations of claim 9 as described above. In addition, the Daftary reference teaches that the one or more subsequence contexts comprise one or more demographic attributes of a subject entity associated with the subject sequence data object (the additional features include details about the person or entity who created or is associated with the content [paragraphs 79-80]).
With respect to claim 11, the Daftary reference teaches all the limitations of claim 1 as described above. Additionally, the Daftary reference teaches that the attention mechanism comprises a self-attention mechanism (the transformer encoding layer includes a multi-head self-attention mechanism [paragraph 52]).
With respect to claim 12, the Daftary reference teaches all the limitations of claim 1 as described above. In addition, the Daftary reference teaches that the one or more subject contextual data objects comprise one or more token-level contexts (the content is tokenized and contexts are determined [paragraphs 47, 51, and 52]).
With respect to claim 13, the Daftary reference teaches all the limitations of claim 12 as described above. Additionally, the Daftary reference teaches that the subject sequence data object is derived from a plurality of events, at least one of the token-level contexts applies to one or more of the plurality of events (temporal features of the content includes whether the content is related to a specific event [paragraph 82]).
With respect to claim 14, the Daftary reference teaches all the limitations of claim 1 as described above. In addition, the Daftary reference teaches that the one or more subject contextual data objects comprise one or more token-to-token contexts (the position of tokens to other tokens provides context [paragraphs 47 and 51]).
With respect to claim 16, the Daftary reference teaches all the limitations of claim 1 as described above. Additionally, the Daftary reference teaches that embedding the one or more subject contextual data objects in the attention mechanism comprises adding the one or more subject contextual data objects to the queries matrix (context relationships for each token are determined by deriving query, key, and value vectors from each token’s embedding [paragraph 52]).
With respect to claim 19, the limitations of claim 19 are merely the non-transitory computer readable medium embodiment of claim 1 and claim 19 recites no further significant limitations therein. Therefore, the limitations of claim 19 are rejected in the analysis of claim 1 and claim 19 is likewise rejected on the same basis.
With respect to claim 20, the limitations of claim 20 are merely the computer-implemented method embodiment of claim 1 and claim 20 recites no further significant limitations therein. Therefore, the limitations of claim 20 are rejected in the analysis of claim 1 and claim 20 is likewise rejected on the same basis.
Allowable Subject Matter
Claims 15, 17, and 18 are 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
Any inquiry concerning this communication or earlier communications from the examiner should be directed to KRIS E MACKES whose telephone number is (571)270-3554. The examiner can normally be reached Monday-Friday 9:00-4:00 EST.
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, Kavita Stanley can be reached at 571-272-8352. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/KRIS E MACKES/Primary Examiner, Art Unit 2153
Prosecution Insights