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 .
This Office Action is sent in response to Applicant's Communication received on February 28, 2025 for application number 19/067,743. This Office hereby acknowledges receipt of the following and placed of record in file: Specification, Drawings, Abstract, Oath/Declaration, and Claims.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 1, 10, and 15 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
The phrase "stored verified values” in claims 1, 10, and 15 are not previously mentioned in claim language prior which renders the claim indefinite. The phrase "stored verified values" is unclear as to what verified values are being referred to, it is unclear if stored verified values are referring to the verified attribute values are a different stored verified values, and one of ordinary skill in the art would not be reasonably apprised of the scope of the invention.
Claims 2-9, 11-14 and 16-20 are rejected by virtue of their dependency on independent claims 1, 10, and 15.
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.
The factual inquiries set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 1, 5-6, 8-10, 14-15, and 19-20 are rejected under 35 U.S.C. 103 as being unpatentable over Howard (US 2021/0232632)(hereinafter Howard) in view of Sathi et al. (US 2024/0242527)(hereinafter Sathi).
Regarding claim 1, Howard teaches a method comprising: accessing a catalogue database of an online system including one or more items (see Fig. 1, para [0030], para [0032], discloses accessing target identity content repositories (catalogue database) of a virtual experience service that includes content elements and information feeds (items)); for each item in the one or more items: extracting one or more attribute values from information from two or more data sources for the item (see Fig. 1, para [0095], para [0115], discloses extracting entities (attribute values) of text, image, and video content such as, key concepts from text and extraction of person or place from images from information of element sources), wherein the two or more data sources have different item data modalities (see Fig. 1, para [0081], discloses different element sources, e.g., JSON, XML, HTML, images, video files, document files, and other proprietary formats), the two or more data sources comprising at least two or more of: a text description of the item, image of the item, information for the item from a third-party database, or user engagement data for the item (see para [0081], para [0115], discloses key concepts from text and person or place from images), wherein extracting the one or more attribute values further comprises applying at least one machine-learning model to the information from the two or more data sources (see Fig. 4B, para [0119, 0121], para [0164],using machine learning-based image interpretation services extracting image subject matter).
Howard does not explicitly teach verifying the one or more attribute values for the item; and responsive to verifying the one or more attribute values for the item, updating the catalogue database to store verified values in association with the item in the catalogue database.
Sathi teaches verifying the one or more attribute values for the item (see Fig. 4, Fig. 5B, para [0063], para [0071], discloses user validation (verifying attribute values)); and responsive to verifying the one or more attribute values for the item, updating the catalogue database to store verified values in association with the item in the catalogue database (see Fig. 5B, para [0071-0072], discloses in response to user validation response, updating feedback file for document cluster).
Howard/Sathi are analogous arts as they are each from the same field of endeavor of database systems.
Before the effective filing date of the invention it would have been obvious to a person of ordinary skill in the art to modify the system of Howard to verify attribute values from disclosure of Sathi. The motivation to combine these arts is disclosed by Sathi as “improved techniques can provide automated feedback-based modification of data extracted from an image of a document using previously used validation guidance” (para [0003]) and verifying attribute values are well known to persons of ordinary skill in the art, and therefore one of ordinary skill would have good reason to pursue the known options within his or her technical grasp that would lead to anticipated success.
Regarding claim 10, Howard teaches a non-transitory computer-readable storage medium storing computer instructions, the computer instructions (see Fig. 10, para [0244, 0246], discloses processor and memory), when executed by one or more processors, cause the one or more processors to perform operations further comprising: accessing a catalogue database of an online system including one or more items (see Fig. 1, para [0030], para [0032], discloses accessing target identity content repositories (catalogue database) of a virtual experience service that includes content elements and information feeds (items)); for each item in the one or more items: extracting one or more attribute values from information from two or more data sources for the item (see Fig. 1, para [0095], para [0115], discloses extracting entities (attribute values) of text, image, and video content such as, key concepts from text and extraction of person or place from images from information of element sources), wherein the two or more data sources have different item data modalities (see Fig. 1, para [0081], discloses different element sources, e.g., JSON, XML, HTML, images, video files, document files, and other proprietary formats), the two or more data sources comprising at least two or more of: a text description of the item, image of the item, information for the item from a third-party database, or user engagement data for the item(see para [0081], para [0115], discloses key concepts from text and person or place from images), wherein extracting the one or more attribute values further comprises applying at least one machine-learning model to the information from the two or more data sources (see Fig. 4B, para [0119, 0121], para [0164],using machine learning-based image interpretation services extracting image subject matter).
Howard does not explicitly teach verifying the one or more attribute values for the item; and responsive to verifying the one or more attribute values for the item, updating the catalogue database to store verified values in association with the item in the catalogue database.
Sathi teaches verifying the one or more attribute values for the item (see Fig. 4, Fig. 5B, para [0063], para [0071], discloses user validation (verifying attribute values)); and responsive to verifying the one or more attribute values for the item, updating the catalogue database to store verified values in association with the item in the catalogue database (see Fig. 5B, para [0071-0072], discloses in response to user validation response, updating feedback file for document cluster).
Howard/Sathi are analogous arts as they are each from the same field of endeavor of database systems.
Before the effective filing date of the invention it would have been obvious to a person of ordinary skill in the art to modify the system of Howard to verify attribute values from disclosure of Sathi. The motivation to combine these arts is disclosed by Sathi as “improved techniques can provide automated feedback-based modification of data extracted from an image of a document using previously used validation guidance” (para [0003]) and verifying attribute values are well known to persons of ordinary skill in the art, and therefore one of ordinary skill would have good reason to pursue the known options within his or her technical grasp that would lead to anticipated success.
Regarding claim 15, Howard teaches a computer system comprising: a processor; and a non-transitory computer readable storage medium storing instructions (see Fig. 10, para [0244, 0246], discloses processor and memory) that, when executed by the processor, cause the processor to perform actions comprising: accessing a catalogue database of an online system including one or more items (see Fig. 1, para [0030], para [0032], discloses accessing target identity content repositories (catalogue database) of a virtual experience service that includes content elements and information feeds (items)); for each item in the one or more items: extracting one or more attribute values from information from two or more data sources for the item (see Fig. 1, para [0095], para [0115], discloses extracting entities (attribute values) of text, image, and video content such as, key concepts from text and extraction of person or place from images from information of element sources), wherein the two or more data sources have different item data modalities (see Fig. 1, para [0081], discloses different element sources, e.g., JSON, XML, HTML, images, video files, document files, and other proprietary formats), the two or more data sources comprising at least two or more of: a text description of the item, image of the item, information for the item from a third-party database, or user engagement data for the item (see para [0081], para [0115], discloses key concepts from text and person or place from images), wherein extracting the one or more attribute values further comprises applying at least one machine-learning model to the information from the two or more data sources (see Fig. 4B, para [0119, 0121], para [0164],using machine learning-based image interpretation services extracting image subject matter).
Howard does not explicitly teach verifying the one or more attribute values for the item; and responsive to verifying the one or more attribute values for the item, updating the catalogue database to store verified values in association with the item in the catalogue database.
Sathi teaches verifying the one or more attribute values for the item (see Fig. 4, Fig. 5B, para [0063], para [0071], discloses user validation (verifying attribute values)); and responsive to verifying the one or more attribute values for the item, updating the catalogue database to store verified values in association with the item in the catalogue database (see Fig. 5B, para [0071-0072], discloses in response to user validation response, updating feedback file for document cluster).
Howard/Sathi are analogous arts as they are each from the same field of endeavor of database systems.
Before the effective filing date of the invention it would have been obvious to a person of ordinary skill in the art to modify the system of Howard to verify attribute values from disclosure of Sathi. The motivation to combine these arts is disclosed by Sathi as “improved techniques can provide automated feedback-based modification of data extracted from an image of a document using previously used validation guidance” (para [0003]) and verifying attribute values are well known to persons of ordinary skill in the art, and therefore one of ordinary skill would have good reason to pursue the known options within his or her technical grasp that would lead to anticipated success.
Regarding claims 5, 14, and 19, Howard/Sathi teach a method of claim 1, medium of claim 10, and system of claim 15.
Howard further teaches transmitting instructions to cause display of an interface on a client device (see Fig. 1, Fig. 5, para [0197], discloses display dashboard for beholder’s virtual experience to show factors of interest about target identity in relation to displayed content segment); and receiving, via the interface, a set of inputs from the client device, wherein the set of inputs includes at least one desired attribute for extraction and instructions for obtaining the information from the two or more data sources for each item in the one or more items (see Fig. 1, para [0056-0057], discloses target identity designators, subject matter prompt and parameters (set of inputs) in Beholder request in interpretation services identifying key concepts in text and people and places in images).
Regarding claim 6, Howard/Sathi teach a method of claim 1.
Howard further teaches generating a prompt from the set of inputs received from the interface, the prompt requesting extraction of attribute values for the desired attribute for the one or more items (see Fig. 1, para [0033], para [0057], discloses subject matter prompt in Beholder request for identifying entities in text and images) ;providing the prompt for execution to a multi-modal machine-learning model (see Fig. 1, Fig. 4B, para [0164], para [0168], discloses providing subject matter prompt to machine learning model); and extracting the one or more attribute values for each item from a response from the multi-modal machine-learning model (see Fig. 4B, para [0081], para [0115], discloses extraction of attributes or facets).
Regarding claim 8, Howard/Sathi teach a method of claim 1 and system of claim 15.
Howard further teaches iteratively performing an attribute extraction process for the at least one desired attribute using different sets of inputs (see Figs. 2A-2B, para [0064], discloses searching capabilities of a query formulation iteratively performed); receiving, from a user, a selection of a desired set of inputs based on one or more evaluation results of the attribute extraction process (see Fig. 1, para [0048], para [0067], discloses selecting facet type and target identity); and storing the desired set of inputs (see para [0047], para [0057], discloses storing target identity in target identity’s content repositories).
Regarding claims 9 and 20, Howard/Sathi teach a method of claim 1 and system of claim 15.
Howard further teaches transmitting instructions to a second client device to cause display of a set of attribute values (see Fig. 1, Fig. 4A, Fig. 6, para [0215], para [0222], discloses presenting virtual experience container data to other devices); receiving a selection of an attribute value from a user of the second client device (see Fig. 1, para [0214], discloses receiving Beholder request properties to modify selected content elements); and presenting a filtered set of items associated with the selected attribute value (see Fig. 1, Fig. 6, para [0214], para [0222], discloses presenting filtered data in accordance with beholder properties).
Claims 2-3, 7, 11-12, and 16-17 are rejected under 35 U.S.C. 103 as being unpatentable over Howard (US 2021/0232632)(hereinafter Howard) in view of Sathi et al. (US 2024/0242527)(hereinafter Sathi) as applied to claims 1, 10, and 15 and in further view of Narlikar et al. (US 2023/0334328)(hereinafter Narlikar).
Regarding claims 2, 11, and 16, Howard/Sathi teach a method of claim 1, medium of claim 10, and system of claim 15.
Howard/Sathi do not explicitly teach cross-checking the extracted attribute values for the item to identify whether two or more attribute values extracted from the two or more data sources contradict each other; and responsive to identifying that there is a contradiction within the two or more attribute values, providing the two or more contradicting values to an audit system for verification.
Narlikar teaches cross-checking the extracted attribute values for the item to identify whether two or more attribute values extracted from the two or more data sources contradict each other (see Fig. 2, para [0037-0038], discloses comparing extracted features from data elements that include extracted text features and extracted image features); and responsive to identifying that there is a contradiction within the two or more attribute values, providing the two or more contradicting values to an audit system for verification (see Fig. 3, para [0030-0031], para [0043-0044], discloses identifying image based content items that do not appear in text based content items and providing confidence values for merged content items generated from common text and image features to classify content items based on policy classification).
Howard/Sathi/Narlikar are analogous arts as they are each from the same field of endeavor of database systems.
Before the effective filing date of the invention it would have been obvious to a person of ordinary skill in the art to modify the system of Howard/Sathi to cross-check attribute values from disclosure of Narlikar. The motivation to combine these arts is disclosed by Narlikar as “train a joint model that classifies content items including multiple content types” (para [0016]) and cross-checking attribute values is well known to persons of ordinary skill in the art, and therefore one of ordinary skill would have good reason to pursue the known options within his or her technical grasp that would lead to anticipated success.
Regarding claims 3, 12, and 17, Howard/Sathi teach a method of claim 1, medium of claim 10, and system of claim 15.
Howard/Sathi do not explicitly teach wherein extracting the one or more attribute values further comprises extracting the two or more attribute values, comprising: applying a text-based machine-learning model to the text description of the item; or applying an image-based machine-learning model to the image of the item.
Narlikar teaches wherein extracting the one or more attribute values further comprises extracting the two or more attribute values, comprising: applying a text-based machine-learning model to the text description of the item (see para [0016-0017], discloses applying machine learning models to text data); or applying an image-based machine-learning model to the image of the item (see para [0016-0017], discloses applying machine learning models to image data).
Howard/Sathi/Narlikar are analogous arts as they are each from the same field of endeavor of database systems.
Before the effective filing date of the invention it would have been obvious to a person of ordinary skill in the art to modify the system of Howard/Sathi to cross-check attribute values from disclosure of Narlikar. The motivation to combine these arts is disclosed by Narlikar as “train a joint model that classifies content items including multiple content types” (para [0016]) and cross-checking attribute values is well known to persons of ordinary skill in the art, and therefore one of ordinary skill would have good reason to pursue the known options within his or her technical grasp that would lead to anticipated success.
Regarding claim 7, Howard/Sathi teach a method of claim 1.
Howard/Sathi do not explicitly teach wherein the set of inputs further includes a type of model for execution, and wherein the multi-modal machine-learning model is the type of model specified in the set of inputs.
Narlikar teaches wherein the set of inputs further includes a type of model for execution, and wherein the multi-modal machine-learning model is the type of model specified in the set of inputs (see Figs. 3-4, para [0016], para [0027], discloses configuring specific modalities to be merged).
Howard/Sathi/Narlikar are analogous arts as they are each from the same field of endeavor of database systems.
Before the effective filing date of the invention it would have been obvious to a person of ordinary skill in the art to modify the system of Howard/Sathi to cross-check attribute values from disclosure of Narlikar. The motivation to combine these arts is disclosed by Narlikar as “train a joint model that classifies content items including multiple content types” (para [0016]) and cross-checking attribute values is well known to persons of ordinary skill in the art, and therefore one of ordinary skill would have good reason to pursue the known options within his or her technical grasp that would lead to anticipated success.
Claims 4, 13, and 18 are rejected under 35 U.S.C. 103 as being unpatentable over Howard (US 2021/0232632)(hereinafter Howard) in view of Sathi et al. (US 2024/0242527)(hereinafter Sathi) as applied to claims 1, 10, and 15 and in further view of Narlikar et al. (US 2023/0334328)(hereinafter Narlikar) and O’Neill (US 2024/0412542)(hereinafter O’Neill).
Regarding claims 4, 13, and 18, Howard/Sathi teach a method of claim 1, medium of claim 10, and system of claim 15.
Howard/Sathi/Narlikar do not explicitly teach ranking the two or more data sources for the item, and wherein cross-checking the extracted attribute values for the item further comprises identifying that there is the contradiction between the extracted attribute values responsive to identifying the two or more data sources are above a threshold ranking.
O’Neill teaches ranking the two or more data sources for the item (see para [0201], discloses ranking brands and content creators respective content on search engines), and wherein cross-checking the extracted attribute values for the item further comprises identifying that there is the contradiction between the extracted attribute values responsive to identifying the two or more data sources are above a threshold ranking (see Fig. 14B, Fig. 15B, para [0201], para [0216], para [0487], discloses unable to identify topic-of-Interest, ToI using external ToI identifier and retrieve content related to ToI and select content that exceeds a media impact score threshold).
Howard/Sathi/Narlikar/O’Neill are analogous arts as they are each from the same field of endeavor of database systems.
Before the effective filing date of the invention it would have been obvious to a person of ordinary skill in the art to modify the system of Howard/Sathi/Narlikar to utilize a threshold ranking from disclosure of O’Neill. The motivation to combine these arts is disclosed by O’Neill as “improve the relevance and specificity of information contained” (para [0075]) and utilizing a threshold ranking is well known to persons of ordinary skill in the art, and therefore one of ordinary skill would have good reason to pursue the known options within his or her technical grasp that would lead to anticipated success.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. See Mikhailuk et al. US Publication No. 2025/0150414.
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/Courtney Harmon/Primary Examiner, Art Unit 2159
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