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 .
Claims 1-20 are pending in this application.
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.
Claim(s) 1, 6-8, 13-15, 19-20 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Huyghe et al., US 2019/0340255 (hereinafter Huyghe).
For claims 1, 8, 15, Huyghe teaches a method, comprising:
receiving, by a server computing device, a digital asset to be made available for distribution by way of a digital asset manager (see Huyghe, Fig. 15, [0039], [0061], [0145], “digital assets” received in order to made available for “searching digital assets in a computing device” by “digital asset management module” on “server”);
identifying, by the server computing device, a plurality of attributes associated with the digital asset (see Huyghe, [0041], [0061] - [0062], “metadata describes one or more characteristics or attributes associated with one or more digital assets”);
generating, by the server computing device, based on the plurality of attributes, a plurality of natural language tags that correspond to the digital asset (see Huyghe, [0005], [0042], “generating zero keyword or contextual keyword search of the digital assets” that associate “keyword tags that describe characteristics associated with the digital assets,”);
associating by the server computing device, the plurality of natural language tags with the digital asset (see Huyghe, [0005], [0042], [0084], storing generated “keyword tags associated with the digital assets”);
generating, by the server computing device, a product page for the digital asset, wherein the product page includes at least one natural language tag of the plurality of natural language tags (see Huyghe, [0087], “digital asset management page” for digital asset that includes display of “associated keyword tags”); and
publishing by the server computing device, the digital asset for distribution to at least one client computing device by way of the digital asset manager (see Huyghe, [0087], “digital asset management page” is published/distributed to allow user to “search” for digital assets), wherein:
the plurality of natural language tags is exposed to query functions implemented by the digital asset manager (see Huyghe, Fig. 10, [0087], “search field 202 is configured to receive one or more characters of text that is used to search the digital assets in the digital asset collection,” [0108], along with search text method “returns suggested search categories 1014 with associated collections icons 1016 and keyword tags 1018 to help in suggesting a further search category”), and
the at least one client computing device displays the product page on a display device that is communicatively coupled to the at least one client computing device (see Huyghe, Fig. 10, [0108], displays search and results on “user interface” of user device).
For claims 6, 13, 19, Huyghe teaches the method of claim 1, wherein the at least one client computing device accesses the product page by submitting a query that includes at least one natural language tag that corresponds to at least one natural language tag of the plurality of natural language tags (see Huyghe, Fig. 10, [0087], [0108], search text and at least one of selected “keyword tags” to return search results).
For claims 7, 14, 20, Huyghe teaches the method of claim 1, wherein the at least one natural language tag is displayed on the product page (see Huyghe, Fig. 10, [0087], [0108], along with search text method “returns suggested search categories 1014 with associated collections icons 1016 and keyword tags 1018 to help in suggesting a further search category”).
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) 2-4, 9-11, 16-18 is/are rejected under 35 U.S.C. 103 as being unpatentable over Huyghe et al., US 2019/0340255 (hereinafter Huyghe) in view of Hemani et al., US 2019/0005043 (hereinafter Hemani).
For claims 2, 9, 16, Hemani teaches the method of claim 1, wherein identifying the plurality of attributes includes providing, by the server computing device, the digital asset, information associated with the digital asset, or some combination thereof, to a large-language model to cause the large-language model to provide the plurality of attributes (see Hemani, [0004], “automated tagging of digital assets (e.g., digital images) through use of models trained using machine learning in accordance with a generic vocabulary set,” [0017] – [0019], “training” “machine learning” model (representing LLM) to “generate custom tags”). It would have been obvious to one skilled in the art at the time of the invention to modify the teachings of Huyghe with the teachings of Hemani to implement a machine learning model to efficiently generated relevant searchable tags for digital assets likely conserving computational resources (see Hemani, [0018]).
For claims 3, 10, 17, the combination teaches the method of claim 2, wherein the large-language model is trained on a corpus of digital assets and attributes thereof (see Hemani, [0019] – [0022], ML model is trained “using training digital assets”).
For claims 4, 11, 18, the combination teaches the method of claim 2, wherein generating the plurality of natural language tags includes providing, by the server computing device to the large-language model, the plurality of attributes to cause the large-language model to provide the plurality of natural language tags (see Hemani, [0018] – [0022], ML analyzes “characteristics” of digital assets).
Claim(s) 5, 12 is/are rejected under 35 U.S.C. 103 as being unpatentable over Huyghe et al., US 2019/0340255 (hereinafter Huyghe) in view of Hemani et al., US 2019/0005043 (hereinafter Hemani) and further in view of Roozen et al., US 2013/0185175 (hereinafter Roozen).
For claims 5, 12, Roozen teaches wherein the information associated with the digital asset includes a plurality of online reviews received for the digital asset (see Roozen, [0028] “analyzes the product data 116 to associate tags with products”, [0030], “identify feedback items,” [0042], [0051], analyze “feedback” and/or user “review” associated with “product” to determine “tag” to associate with product). It would have been obvious to one skilled in the art at the time of the invention to modify the teachings of Huyghe and Hemani with the teachings of Roozen to associate review sentiment tags with products to efficiently search for relevant products that match user’s query (see Roozen, [0030], [0070]).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Ganesan US 2022/0253495. [0024] - [0025], [0063].
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/JENSEN HU/Primary Examiner, Art Unit 2169