DETAILED ACTION
This action is in response to the original filing of 12-21-2023. Claims 1-20 are pending and have been considered below:
Claim Rejections - 35 USC § 103
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 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) 1-2, 4-5, 8-9, 11-12, 15-16 and 18-19 is/are rejected under 35 U.S.C. 103 as being unpatentable over Zhao (20230094415 A1) in view of Goddijin et al.(“Goddijin” 20240152493 A1) and He et al. (“He” 11886457 B2).
Claim 1: Zhao discloses a computer-implemented method for model customization for domain-specific tasks, comprising: selecting, by at least one computer processor, a pre-trained embedding model, wherein the pre-trained embedding model comprises weights based on training the pre-trained embedding model with a first dataset (Paragraph 66 (trained model) and 31; embedding model with parameters-comprises weights);
Zhao further discloses modifying, the weights of the pre-trained embedding model; transforming, based on the modified weights of the pre-trained embedding model, the pre-trained embedding model to a target embedding model for the target domain
(Paragraphs 31 and 85; adapted model based on target domain).
Zhao may not explicitly disclosed determining, based on a target domain, a second dataset, wherein the second dataset comprises textual data representative of the target domain; transforming, based on target embeddings for data indicative of the target domain, the second dataset from a first format to a second format, wherein the second format is associated with the target domain. Goddijin is provided because it discloses a first dataset (Paragraphs 30; first dataset) and further discloses a the second dataset format transformation (Paragraphs 30 (first and second dataset) 55, 84 (edit mapping) and 91; transforms a dataset to a target domain dataset).
Therefore it would have been obvious to one having ordinary skill in the art before the effective filling date of the claimed invention to use a known technique to improve a similar device and provide the first and second dataset transformation process in Zhao. One would have been motivated to provide the functionality as an effective method of data management and standardization.
Zhao also may not explicitly disclose and generating, based on a task of the target domain performed by the target embedding model, an efficacy score for the target embedding model
He is provided because it discloses a transformation functionality and further determines a scoring for the targeted transformation (Column 3, Lines 38-48). Therefore it would have been obvious to one having ordinary skill in the art before the effective filling date of the claimed invention to use a known technique to improve a similar device and provide scoring for the dataset transformation process provided in Zhao. One would have been motivated to provide the functionality as an effective method to determine effectiveness and accuracy of transformation.
Claim 2: Zhao, Goddijin and He disclose a computer-implemented method of claim 1, wherein the transforming the second dataset from the first format to the second format is further based on at least one of: tokenization of data from the second dataset, stop word removal of data from the second dataset, stemming data from the second dataset, or lemmatization of data from the second dataset (Goddijin: Paragraph 24; standardized and normalizing data).
Claim 4: Zhao, Goddijin and He disclose a computer-implemented method of claim 1, wherein the task of the target domain comprises at least one of: a content item retrieval task for the target domain, a text classification task for the target domain, an entity recognition task for the target domain, or a sentiment analysis task for the target domain (Goddijin: Paragraphs 29 and 72; image processing/image scoring-entity recognition and Zhao: Paragraph 338; image /text recognition).
Claim 5: Zhao, Goddijin and He disclose a computer-implemented method of claim 1, further comprising implementing, based on the efficacy score for the target embedding model satisfying an efficacy score threshold for the target domain, the target embedding model within the target domain (He: Column 3, Lines 38-48).
Claims 8 and 15 are similar is scope to claim 1 and therefore rejected under the same rationale.
Memory/processor (Goddijin: Paragraph 26)
computer readable medium (Goddijin: Paragraph 26)
Claims 9 and 16 are similar is scope to claim 2 and therefore rejected under the same rationale.
Claims 11 and 18 are similar is scope to claim 4 and therefore rejected under the same rationale.
Claims 12 and 19 are similar is scope to claim 5 and therefore rejected under the same rationale.
Claim(s) 3, 10 and 17 is/are rejected under 35 U.S.C. 103 as being unpatentable over Zhao (20230094415 A1), Goddijin et al.(“Goddijin” 20240152493 A1) and He et al. (“He” 11886457 B2) in further view of Zhai et al. (“Zhai” 20230394317 A1).
Claim 3: Zhao, Goddijin and He disclose a computer-implemented method of claim 1, wherein the modifying the weights of the pre-trained embedding model further comprises: generating, based on at least one of skip-gram applied to the transformed second dataset or continuous bag of words (CBOW) applied to the transformed second dataset, a modified version of the transformed second dataset; and outputting, based on the pre-trained embedding model trained with the modified version of the transformed second dataset, the modified weights of the pre-trained embedding model (Zhao: Paragraphs 31 and 85; modified parameters of model).
Zhai is also provided because it discloses a transformation functionality, specifically CBOW (Paragraph 13). Therefore it would have been obvious to one having ordinary skill in the art before the effective filling date of the claimed invention to use a known technique to improve a similar device and provide the specific technique on the dataset provided in Zhao. One would have been motivated to provide the functionality as an effective method of normalizing datasets for optimal performance.
Claims 10 and 17 are similar is scope to claim 3 and therefore rejected under the same rationale.
Claim(s) 6, 13 and 20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Zhao (20230094415 A1), Goddijin et al.(“Goddijin” 20240152493 A1) and He et al. (“He” 11886457 B2) in further view of Brady et al. (“Brady” 20060075463 A1).
Claim 6: Zhao, Goddijin and He disclose a computer-implemented method of claim 1, however may not explicitly disclose wherein the second dataset indicates at least one of a content item that has been requested a threshold amount of times during a timeframe, or a content item that has at least one character in a title that has been requested another threshold amount of times.
Brady is provided because it discloses a transformation functionality and further discloses determining request for the data (abstract, Paragraphs 9 and 61-62; request of data based on conditions). Therefore it would have been obvious to one having ordinary skill in the art before the effective filling date of the claimed invention to use a known technique to improve a similar device to determine transformation of a dataset provided in Zhao. One would have been motivated to provide the functionality as an effective method of determining targeted data transformation.
Claims 13 and 20 are similar is scope to claim 6 and therefore rejected under the same rationale.
Claim(s) 7 and 14 is/are rejected under 35 U.S.C. 103 as being unpatentable over Zhao (20230094415 A1), Goddijin et al.(“Goddijin” 20240152493 A1) and He et al. (“He” 11886457 B2) in further view of Ando et al. (“Ando” 20240185057 A1)
Claim 7: Zhao, Goddijin and He disclose a computer-implemented method of claim 1, however may not explicitly disclose wherein each weight of the modified weights is associated with a respective content item of a plurality of content items for the target domain, the method further comprising adjusting a weight of the modified weights based on an event in the target domain associated with the respective content item.
Ando is provided because it discloses a transformation functionality and further discloses modifying weights for a target domain (Paragraph 21). Therefore it would have been obvious to one having ordinary skill in the art before the effective filling date of the claimed invention to use a known technique to improve a similar device and provide the weight adjustment for a target domain provided in Zhao. One would have been motivated to provide the functionality as an effective method of optimizing the model to ensure the inference addresses the specified task.
Claim 14 is similar is scope to claim 7 and therefore rejected under the same rationale.
Conclusion
The prior art made of record and not relied upon is considered pertinent to Applicant’s disclosure:
10650020 B1 ABSTRACT
Applicant is required under 37 C.F.R. § 1.111(c) to consider these references fully when responding to this action.
It is noted that any citation to specific pages, columns, lines, or figures in the prior art references and any interpretation of the references should not be considered to be limiting in any way. A reference is relevant for all it contains and may be relied upon for all that it would have reasonably suggested to one having ordinary skill in the art. In re Heck, 699 F.2d 1331, 1332-33, 216 U.S.P.Q. 1038, 1039 (Fed. Cir. 1983) (quoting In re Lemelson, 397 F.2d 1006, 1009, 158 U.S.P.Q. 275, 277 (C.C.P.A. 1968)).
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/SHERROD L KEATON/Primary Examiner, Art Unit 2148
5-23-2026
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