DETAILED ACTION
Notice of Pre-AIA or AIA Status
1. The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
2. Claims 1-19 are pending.
Information Disclosure Statement
3. The information disclosure statement (IDS) submitted on 3/30/2017 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Drawings
4. The drawings have been reviewed and are accepted as being in compliance with the provisions of 37 CFR 1.121.
Double Patenting
5. The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the "right to exclude" granted by a patent and to prevent possible harassment by multiple assignees. See In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); and, In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) may be used to overcome an actual or provisional rejection based on a nonstatutory double patenting ground provided the conflicting application or patent is shown to be commonly owned with this application. See 37 CFR 1.130(b).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on nonstatutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP § 2146 et seq. for applications not subject to examination under the first inventor to file provisions of the AIA . A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
The USPTO Internet website contains terminal disclaimer forms which may be used. Please visit www.uspto.gov/patent/patents-forms. The filing date of the application in which the form is filed determines what form (e.g., PTO/SB/25, PTO/SB/26, PTO/AIA /25, or PTO/AIA /26) should be used. A web-based eTerminal Disclaimer may be filled out completely online using web-screens. An eTerminal Disclaimer that meets all requirements is auto-processed and approved immediately upon submission. For more information about eTerminal Disclaimers, refer to www.uspto.gov/patents/process/file/efs/guidance/eTD-info-I.jsp.
Claim 1-19 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-20 of US 12,405,996. Although the claims at issue are not identical, they are not patentably distinct from each other because claims 1-19 of the instant application substantially recite the limitations of claims 1-20 of the cited US 12,405,996 for each of the plurality of digital images comprising one or more definition unigrams; training a deep learning model to map the one or more first unigrams to first vector representations for the text input and to map the one or more definition unigrams to second vector. The claim merely omits certain bolded limitations as shown in comparison table below, and replace them with .
Claim 1 (instant application)
Claim 1 (US 12,405,996)
1. A computer-implemented method comprising: using a server computer, obtaining from a client computer a text input comprising one or more first unigrams;
executing a deep learning model on the text input, to map the one or more first unigrams of the text input to first vector representations for the text input and
generating a first embedding for the first vector representations in a multi-dimensional embedding space based on a first combination of the first vector representations;
executing the deep learning model on a plurality of digital images each comprising one or more definition unigrams, the executing of the deep learning model on each digital image mapping the one or more definition unigrams to second vector representations for each digital image and
generating a second embedding of the second vector representations of each digital image in the multi-dimensional embedding space based on a second combination of the second vector representations of a corresponding image;
identifying one or more relevant images based on a respective similarity of the first embedding to the second embedding;
determining one or more information terms for each of the one or more relevant images, an image informativeness value for each of the one or more relevant images based on the one or more information terms;
and transmitting, to the client computer in response to obtaining the text input, instructions for presenting a user interface comprising the one or more relevant images and the image informativeness value for each of the one or more relevant images.
1. A computer-implemented method comprising: using a server computer, obtaining from a client computer a text input comprising one or more first unigrams in a query from a user;
accessing in digital data storage coupled to the server computer a plurality of digital images, each of the plurality of digital images comprising one or more definition unigrams;
training a deep learning model to map the one or more first unigrams to first vector representations for the text input and to map the one or more definition unigrams to second vector representations for the plurality of digital images, the deep learning model being a dual encoder model comprising a text encoder and an image encoder based on a ranking loss function;
determining, using the deep learning model, the first vector representations of the text input by mapping the one or more first unigrams of the text input to the first vector representations for the text input;
determining, using the deep learning model, a first embedding of the first vector representations of the text input in a multi-dimensional embedding space based on a combination of the first vector representations of the text input;
determining, using the deep learning model, the second vector representations of each of the plurality of digital images by mapping the one or more definition unigrams of each of the plurality of digital images to the second vector representations for the plurality of digital images;
determining, using the deep learning model, a second embedding of the second vector representations of each of the plurality of digital images in the multi-dimensional embedding space based on a combination of the second vector representations of a corresponding image;
identifying one or more relevant images based on a respective similarity of the first embedding to the second embedding;
determining one or more information terms for each of the one or more relevant images, an image informativeness value for each of the one or more relevant images based on the one or more information terms, and a confidence score for each of the one or more information terms;
and transmitting, to the client computer in response to obtaining the text input, instructions for presenting a user interface comprising the one or more relevant images and the confidence score for each of the one or more information terms for each of the one or more relevant images.
Table 1
Therefore, it would have been obvious to one of ordinary skill in the art of data processing at the time the invention was made to modify the invention as claimed in the instance application by substituting accessing in digital data storage coupled to the server computer a plurality of digital images, each of the plurality of digital images comprising one or more definition unigrams; training a deep learning model to map the one or more first unigrams to first vector representations for the text input and to map the one or more definition unigrams to second vector representations for the plurality of digital images, the deep learning model being a dual encoder model comprising a text encoder and an image encoder based on a ranking loss function; with executing the mentioned limitations; since an omission and addition of a cited limitation would have not changed the process according to which the method and system as claimed.
Therefore, the use of generating a second embedding of the second vector representations of each digital image in the multi-dimensional embedding space based on a second combination of the second vector representations of a corresponding image would be an obvious variation in the art for the purpose of achieving the same end results determining, using the deep learning model of the steps previously claimed and would perform the same function.
The independent and dependent claims 2-19 are rejected for fully incorporating the errors of their respective base claims by dependency.
Allowable Subject Matter
6. Claims 1-19 would be allowable if rewritten or amended to overcome the double patenting rejection set forth in this Office action.
The closest prior art Zhang et al (US 2023/0418861), GENERATING EMBEDDINGS FOR TEXT AND IMAGE QUERIES WITHIN A COMMON EMBEDDING SPACE FOR VISUAL-TEXT IMAGE SEARCHES. Specifically, system utilizes one or more search engines to perform textual-visual searches and/or sketch searches via common embedding spaces; utilizes powerful image-editing techniques—such as color transfer, tone transfer, or texture transfer—to modify the input. Forsyth et al (US 2020/0311798), Relates to SEARCH ENGINE USE OF NEURAL NETWORK REGRESSOR FOR MULTI-MODAL ITEM RECOMMENDATIONS BASED ON VISUAL SEMANTIC EMBEDDINGS. Specifically, to employing machine learning by way of a neural network (NN) regressor to provide multi-modal search results to users. In one embodiment, a search engine server may perform visual semantic embedding of images in order to learn both item-based similarity (for example, when two tops are interchangeable) and compatibility (items of possibly different type that can go together in an outfit). COHEN et al. (WO 2023/154385) is directed to MACHINE LEARNING MODELS AS A DIFFERENTIABLE SEARCH INDEX FOR DIRECTLY PREDICTING RESOURCE RETRIEVAL RESULTS. Specifically, the query generation model can be provided with the resource and tasked with predicting the corresponding query included in the tuple. A loss function can compare the predicted query with the real query and used to update the query generation model. Price et al. (US 2022/0198671), relates to
UTILIZING A SEGMENTATION NEURAL NETWORK TO PROCESS INITIAL OBJECT SEGMENTATIONS AND OBJECT USER INDICATORS WITHIN A DIGITAL IMAGE TO GENERATE IMPROVED OBJECT SEGMENTATIONS. Specifically, the user interface manager 1012 receives user inputs from a user, such as a click/tap to provide an object user indicator with respect to a portion of a digital image. Additionally, the user interface manager 1012 in one or more embodiments presents a variety of types of information, including text, digital media items, object segmentations, or other information for presentation in a user interface. However, Zhang, Forsyth, COHEN, and Price either singularly or in combination, fail to anticipate or render obvious the recited features “…using a server computer, obtaining from a client computer a text input comprising one or more first unigrams; executing a deep learning model on the text input, to map the one or more first unigrams of the text input to first vector representations for the text input and generating a first embedding for the first vector representations in a multi-dimensional embedding space based on a first combination of the first vector representations; executing the deep learning model on a plurality of digital images each comprising one or more definition unigrams, the executing of the deep learning model on each digital image mapping the one or more definition unigrams to second vector representations for each digital image and generating a second embedding of the second vector representations of each digital image in the multi-dimensional embedding space based on a second combination of the second vector representations of a corresponding image; identifying one or more relevant images based on a respective similarity of the first embedding to the second embedding; determining one or more information terms for each of the one or more relevant images, an image informativeness value for each of the one or more relevant images based on the one or more information terms; and transmitting, to the client computer in response to obtaining the text input, instructions for presenting a user interface comprising the one or more relevant images and the image informativeness value for each of the one or more relevant images.”
Conclusion
7. Any inquiry concerning this communication or earlier communications from the examiner should be directed to ANGELICA RUIZ whose telephone number is (571)270-3158. The examiner can normally be reached M-F 10:00 am to 6:00 pm.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Boris Gorney can be reached at (571) 270-5626. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/ANGELICA RUIZ/Primary Examiner, Art Unit 2154 June 27, 2026
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