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 and is in response to communications filed on 5/07/2025 in which claims 1-21 are presented for examination.
Priority
Acknowledgment is made of provisional application 63/657,851, filed 6/08/2024.
Drawings
Drawings have been acknowledged and are acceptable for examination purposes.
Specification
Specification has been acknowledged and is acceptable for examination purposes.
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, 7, 8, 14, 15, and 21 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Sharifi et al. US 20230186908 A1 (hereinafter referred to as “Sharifi”)
As per claim 15, Sharifi teaches:
A method for providing relevant results for queries, the method comprising, by a server computing device:
receiving a query from a client computing device (Sharifi, [0009] – The automated assistant can cause “Source A” to be searched by (a) submitting a query, that is based on the user request, to a search engine that is specific to “Source A” and/or by (b) formulating a query that is based on the user request and that includes a restriction to “Source A” (e.g., includes “site:SourceA” or other restriction(s)) and submitting the formulated query with restriction to a general search engine);
providing the query to a first machine learning (ML) model to produce a text answer to the query, wherein the text answer includes a plurality of text segments (Sharifi, [0056] – One or more of the machine learning models accessible to audio input/processing engine 122, rule engine 124, and/or one or more other components of the automated assistant 120 can be trained based on training examples that are generated using existing source parameter rules and configured to process various features of an audible request input in order to generate output that indicates a focus of the user request, such as a user intent and/or entity associated with the user request. [0061] – Search results can include a title or other synopsis of a responsive content item, a summary of the content item), and
each text segment of the plurality of text segments is associated with a respective image search query that corresponds to the query and the text segment (Sharifi, [0058] – The type of request may indicate which resources search engine 126 should search (e.g., search of local applications, search of a particular database, general web search, etc.) and/or which type of content the user is requesting (e.g., information, interfacing with another service or application, video or image content, etc.). [0061] – For a search result associated with an image, the search result may include a reduced size display of the image (e.g., “thumbnail” image), a title associated with the image, and/or a link to the image);
for each text segment of the plurality of text segments:
providing, to a second ML model, (i) the query, and (ii) the respective image search query, to obtain respective one or more digital assets that correspond to the query and the respective image search query (Sharifi, [0049] – Various different MLMs can be used that are accible to both client computing devices and/or remote systems which can be used to output generated recognized text or other results. [0061] – A video is interpreted as a digital asset);
generating results based on (i) the query, (ii) the text answer, (iii) the plurality of text segments, and (iv) the respective one or more digital assets (Sharifi, [0061] – The search results obtained by search engine 126 include search results corresponding to content that is responsive to the search(es) issued based on the search parameters. For example, each of the search results can include a title or other synopsis of a responsive content item, a summary of the content item, a link to the responsive content item, other information related to the responsive content item, and/or even the entirety of the content item); and
causing the results to be output by way of a user interface on the client computing device (Sharifi, [0068] – Output engine 130 then causes one or more of the user interface output device(s) 104 to audibly or visually present the response to the user request, as described herein).
As per claim 21, Sharifi teaches:
The method of claim 15, wherein, for a given text segment of the plurality of text segments, each digital asset of the respective one or more digital assets comprises a digital image, a digital video, a digital animation, a digital audio clip, a digital document, or some combination thereof (Sharifi, [0061] – A video is interpreted as a digital asset).
Claims 1 and 7 are directed to a method performing steps recited in claims 15 and 21 with substantially the same limitations. Therefore, the rejections made to claims 15 and 21 are applied to claims 1 and 7.
Claims 8 and 14 are directed to a non-transitory computer readable storage medium performing step recited in claims 15 and 21 with substantially the same limitations. Therefore, the rejections made to claims 15 and 21 are applied to claims 8 and 14.
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 of this title, 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.
Claims 2-6, 9-14, and 16-20 are rejected under 35 U.S.C. 103 as being unpatentable over Sharifi in view of Fernig et al. US 20220156268 A1 (hereinafter referred to as “Fernig”).
As per claim 16, Sharifi teaches:
The method of claim 15, further comprising, prior to providing the query and the respective image search queries to the second ML model:
generating a digital asset benefit metric based on the query and the text answer (Sharifi, [0062] – Source/result selection engine 128 calculates scores for the content of the search results identified by search engine 126 using various quality criteria, such as popularity of the content, degree of matching between the search parameters and the content, degree of matching between the rule parameters and the sources associated with the content, degree of matching between the transcript of the user request and the content, attributes of the user (e.g., a location of the user, a primary language of the user), historical user-assistant or user-device interaction data, and one or more other criteria determined based on comparing the content of the obtained results (e.g., determining which source is more up-to-date, authoritative, available in a more output-friendly format, etc.)); and
Sharifi doesn’t explicitly teach a score or metric that satisfies a threshold, however, Fernig teaches:
determining that the digital asset benefit metric satisfies a threshold (Fernig, [0049] – The threshold may be predefined or predetermined. Additionally or alternatively, the threshold may be manually adjusted, e.g., a value of 80 out of 100. In this case, for search queries 322 associated with an engagement score 321 higher than the threshold, the customization engine 320 may render and present the list of search results 311 from the search engine 310 without modification).
It would have been obvious for one of ordinary skill in the art prior to the effective filing date of the claimed invention to modify Sharifi’s invention in view of Fernig in order to include a score for each item which may satisfy a threshold; this is advantageous because it allows the system to modify search result order or ranking based on how helpful or relevant the results are to the user (Fernig, paragraph [0040]).
As per claim 17, Sharifi as modified with Fernig teaches:
The method of claim 16, wherein the digital asset benefit metric represents an overall helpfulness associated with accompanying the text answer with at least one digital asset (Fernig, [0046] – A higher relevancy score 316 indicates that the associated search result 312 is more likely to be helpful to the user).
As per claim 18, Sharifi as modified with Fernig teaches:
The method of claim 15, wherein, for a given text segment of the plurality of text segments, each digital asset of the respective one or more digital assets:
preexists and is obtained from a data store, or is generated based on the query, the respective image search query, or some combination thereof (Fernig, [0084] – An e-commerce platform contains products and services, wherein these are interpreted as digital assets, and which are preexisting in a data store); and
is assigned a respective digital asset relevance metric that represents a correlation strength between the digital asset, the query, and the respective image search query, wherein the digital asset relevance metric satisfies a threshold (Fernig, [0049] – The threshold may be predefined or predetermined. Additionally or alternatively, the threshold may be manually adjusted, e.g., a value of 80 out of 100. In this case, for search queries 322 associated with an engagement score 321 higher than the threshold, the customization engine 320 may render and present the list of search results 311 from the search engine 310 without modification).
As per claim 19, Sharifi as modified with Fernig teaches:
The method of claim 18, wherein generating the results further comprises, for each text segment of the plurality of text segments:
ordering the respective one or more digital assets based on their respective digital asset relevance metrics (Fernig, [0040] – The search engine 310 may use a ranking algorithm to assign each search result 312 a relevancy score 316 representing how helpful or relevant a search result is based on the search query).
As per claim 20, Sharifi as modified with Fernig teaches:
The method of claim 18, wherein, for a given digital asset obtained from the data store, the respective digital asset relevance metric is calculated based on at least one label, at least one tag, at least one annotation, at least one description, at least one feature vector, at least one embedding, metadata information, or some combination thereof, associated with the given digital asset (Fernig, [0105] – Different merchants will have different needs, and so may benefit from different applications 142. Applications 142 may be better discovered through the e-commerce platform 100 through development of an application taxonomy (categories) that enable applications to be tagged according to a type of function it performs for a merchant).
Claims 2-6 are directed to a method performing steps recited in claims 16-20 with substantially the same limitations. Therefore, the rejections made to claims 16-20 are applied to claims 2-6.
Claims 9-13 are directed to a non-transitory computer readable storage medium performing step recited in claims 16-20 with substantially the same limitations. Therefore, the rejections made to claims 16-20 are applied to claims 9-13.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
Matsuura et al. teaches a content acquisition unit which acquires a plurality of digital content belonging to the category of the received category identification number through a network, and an information transmission unit which transmits summary information of the plurality of acquired digital content and information for causing the software to display the summary information in a predetermined order to the display apparatus (Abstract).
Barnaby et al. 19 Jan 2024, “PhotoScount: Synthesis-Powered Multi-Modal Image Search”, https://arxiv.org/pdf/2401.10464, Pgs. 1-11.
Contact Information
Any inquiry concerning this communication or earlier communications from the examiner should be directed to the current examiner working on this case, name: Matthew Ellis, telephone number: (571)270-3443, email: matthew.ellis@uspto.gov, normal business hours Monday-Friday 8AM-5PM EST.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Neveen Abel-Jalil can be reached on (571)270-0474. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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April 3, 2026
/MATTHEW J ELLIS/Primary Examiner, Art Unit 2152
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