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 .
DETAILED ACTION
Continued Examination Under 37 CFR 1.114
1. A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.
Applicant's submission filed on 10-23-2025 has been entered.
2. Claims 1 - 22 are pending. Claims 1, 21, 22 have been amended. Claims 1, 21, 22 are independent. This application was filed on 1-15-2025.
Response to Arguments
3. Applicant’s arguments, see Arguments/Remarks Made in an Amendment, filed 10-23-2025, with respect to the rejection(s) under Agarwal in view of Luo have been fully considered and are persuasive. Therefore, the rejection has been withdrawn. However, upon further consideration, a new ground(s) of rejection is made in view of Agarwal in view of Luo and further in view of Sarma.
A. Applicant argues on page 7 of Remarks: ... calculating a score for the digital content based on the determined attribute-value pairs and a distance between sets of attribute-values of the digital content and that of other content ... .
The Examiner respectfully disagrees. Agarwal discloses calculating a score parameter associated with digital content based upon attribute value parameters. (see Agarwal paragraph [0198]: calculating relevance scores for each query and generating an attribute map (attribute-value pairs) according to the queries (images, digital content) and their relevance scores. Calculating relevance scores for a query may be a function of popularity, i.e. the relevance score for a query may be a function of a number of times the query has occurred among the queries collected)
And, Sarma discloses calculating a distance parameter representing a distance between attribute values associated with digital content. (see Sarma paragraph [0047]: input data is further processed to obtain an attribute distance parameter between values of each attribute within the item metadata, ... . In one embodiment, the processing engine 211 processes the user data stored within the user tables 310, the item data stored within the item tables 330, and the item metadata stored within the attribute tables 340 to calculate an attribute distance parameter between attribute values pertaining to each attribute of art item.; paragraph [0048]: an item distance parameter between any pair of items is further computed, as described in detail below in connection with FIG. 7. In one embodiment, the processing engine 211 uses the attribute distance parameters to calculate the item distance parameter between a pair of items in order to determine if the respective items are similar or not and the degree of similarity between items.)
B. Applicant argues on pages 7-8 of Remarks: ... there does not appear to be any disclosure in Agarwal of analyzing digital content to determine an attribute-value pair characterizing the digital content, where the attribute-value pairs adhere to a predefined multi- dimensional attribute namespace, ... .
The Examiner respectfully disagrees. Agarwal discloses a determination of a plurality of attribute values pairs describing associated digital content. Agarwal discloses the attribute value pairs are associated with a namespace. (see Agarwal paragraph [0033]: Textual data may include one or more attribute/value pairs, e.g. (color, blue) describing features or attributes for one or more items of clothing depicted in the image. Where multiple items of clothing are shown (image, digital content), each item of clothing may be identified, and its corresponding attribute/value pairs associated with its identifier in the text; (namespace, attribute space used to generate attribute-value pairs)) And, Agarwal discloses the usage of associated digital content as stated above.
Luo in an obviousness rejection discloses a multi-dimensional namespace. (see Luo paragraph [0010]: provide for generation of a multi-dimensional tag metric in a cloud resource management environment. More specifically, a tagging namespace is provided for managing a resource in the cloud resource management environment. This namespace comprises at least two dimensions and a plurality of positions. A set of tags associated with the resource are received into the tagging namespace. A match of each tag of the set of tags to a position within the namespace into which that tag was received is verified ... Alternatively, in the case verification is successful, the tag-containing namespace is validated as a multi-dimensional tag metric.; paragraph [0078]: Each position 132N in tagging namespace 130 can be associated with a particular attribute indicating a type, category, or other feature of a tag that can be received into that position 132N.)
C. Applicant argues on page 8 of Remarks: ... calculating a score for digital content as claimed since Agarwal fails to disclose a multi-dimensional attribute namespace, ... .
The Examiner respectfully disagrees. Agarwal discloses calculating a score parameter associated with digital content based upon attribute value parameters. (see Agarwal paragraph [0198]: calculating relevance scores for each query and generating an attribute map (attribute-value pairs) according to the queries (images, digital content) and their relevance scores. Calculating relevance scores for a query may be a function of popularity, i.e. the relevance score for a query may be a function of a number of times the query has occurred among the queries collected)
Luo in an obviousness rejection discloses a multi-dimensional namespace. (see Luo paragraph [0010]: provide for generation of a multi-dimensional tag metric in a cloud resource management environment. More specifically, a tagging namespace is provided for managing a resource in the cloud resource management environment. This namespace comprises at least two dimensions and a plurality of positions. A set of tags associated with the resource are received into the tagging namespace. A match of each tag of the set of tags to a position within the namespace into which that tag was received is verified ... Alternatively, in the case verification is successful, the tag-containing namespace is validated as a multi-dimensional tag metric.; paragraph [0078]: Each position 132N in tagging namespace 130 can be associated with a particular attribute indicating a type, category, or other feature of a tag that can be received into that position 132N.)
D. Applicant argues on page 8 of Remarks: ... Applicant respectfully submits that Luo fails to disclose calculating a score for digital content based on the determined attribute-value pairs and a distance between sets of attribute-values of the digital content and that of other content, ... .
The Examiner respectfully disagrees. Agarwal discloses calculating a score parameter associated with digital content based upon attribute value parameters. (see Agarwal paragraph [0198]: calculating relevance scores for each query and generating an attribute map (attribute-value pairs) according to the queries (images, digital content) and their relevance scores. Calculating relevance scores for a query may be a function of popularity, i.e. the relevance score for a query may be a function of a number of times the query has occurred among the queries collected)
And, Sarma discloses calculating a distance parameter representing a distance between attribute values associated with digital content. (see Sarma paragraph [0047]: input data is further processed to obtain an attribute distance parameter between values of each attribute within the item metadata, ... . In one embodiment, the processing engine 211 processes the user data stored within the user tables 310, the item data stored within the item tables 330, and the item metadata stored within the attribute tables 340 to calculate an attribute distance parameter between attribute values pertaining to each attribute of art item.; paragraph [0048]: an item distance parameter between any pair of items is further computed, as described in detail below in connection with FIG. 7. In one embodiment, the processing engine 211 uses the attribute distance parameters to calculate the item distance parameter between a pair of items in order to determine if the respective items are similar or not and the degree of similarity between items.)
E. Applicant argues on page 8 of Remarks: ... The remaining cited references also do not appear to disclose the concept of calculating a score for the digital content based on the determined attribute-value pairs and a distance between sets of attribute-values of the digital content and that of other content, ... .
The Examiner respectfully disagrees. Agarwal discloses calculating a score parameter associated with digital content based upon attribute value parameters. (see Agarwal paragraph [0198]: calculating relevance scores for each query and generating an attribute map (attribute-value pairs) according to the queries (images, digital content) and their relevance scores. Calculating relevance scores for a query may be a function of popularity, i.e. the relevance score for a query may be a function of a number of times the query has occurred among the queries collected)
And, Sarma discloses calculating a distance parameter representing a distance between attribute values associated with digital content. (see Sarma paragraph [0047]: input data is further processed to obtain an attribute distance parameter between values of each attribute within the item metadata, ... . In one embodiment, the processing engine 211 processes the user data stored within the user tables 310, the item data stored within the item tables 330, and the item metadata stored within the attribute tables 340 to calculate an attribute distance parameter between attribute values pertaining to each attribute of art item.; paragraph [0048]: an item distance parameter between any pair of items is further computed, as described in detail below in connection with FIG. 7. In one embodiment, the processing engine 211 uses the attribute distance parameters to calculate the item distance parameter between a pair of items in order to determine if the respective items are similar or not and the degree of similarity between items.)
F. Applicant argues on page 8 of Remarks: ... none of the other cited reference disclose attribute-value pairs adhering to a predefined multi-dimensional attribute name space ... .
The Examiner respectfully disagrees. Luo in an obviousness rejection discloses a multi-dimensional namespace. (see Luo paragraph [0010]: provide for generation of a multi-dimensional tag metric in a cloud resource management environment. More specifically, a tagging namespace is provided for managing a resource in the cloud resource management environment. This namespace comprises at least two dimensions and a plurality of positions. A set of tags associated with the resource are received into the tagging namespace. A match of each tag of the set of tags to a position within the namespace into which that tag was received is verified ... Alternatively, in the case verification is successful, the tag-containing namespace is validated as a multi-dimensional tag metric.; paragraph [0078]: Each position 132N in tagging namespace 130 can be associated with a particular attribute indicating a type, category, or other feature of a tag that can be received into that position 132N.)
G. Applicant argues on page 8 of Remarks: ... analyzing digital content to determine an attribute-value pair, or calculating a score for digital content based on a distance between sets of attribute-values of the digital content and that of other content, ... .
The Examiner respectfully disagrees. Agarwal discloses calculating a score parameter associated with digital content based upon attribute value parameters. (see Agarwal paragraph [0198]: calculating relevance scores for each query and generating an attribute map (attribute-value pairs) according to the queries (images, digital content) and their relevance scores. Calculating relevance scores for a query may be a function of popularity, i.e. the relevance score for a query may be a function of a number of times the query has occurred among the queries collected)
And, Sarma discloses calculating a distance parameter representing a distance between attribute values associated with digital content. (see Sarma paragraph [0047]: input data is further processed to obtain an attribute distance parameter between values of each attribute within the item metadata, ... . In one embodiment, the processing engine 211 processes the user data stored within the user tables 310, the item data stored within the item tables 330, and the item metadata stored within the attribute tables 340 to calculate an attribute distance parameter between attribute values pertaining to each attribute of art item.; paragraph [0048]: an item distance parameter between any pair of items is further computed, as described in detail below in connection with FIG. 7. In one embodiment, the processing engine 211 uses the attribute distance parameters to calculate the item distance parameter between a pair of items in order to determine if the respective items are similar or not and the degree of similarity between items.)
Claim Rejections - 35 USC § 103
4. 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.
5. Claims 1 - 7, 11, 14 - 16, 19 - 22 are rejected under 35 U.S.C. 103 as being unpatentable over Agarwal et al. (US PGPUB No. 20210182287) in view of Luo et al. (US PGPUB No. 20200322442) and further in view of Sarma et al. (US PGPUB No. 20090077081)
Regarding Claims 1, 21, 22, Agarwal discloses a computer-implemented method for analyzing digital content and a system, comprising: a processor; and a non-transitory computer-readable medium coupled with the processor, wherein the non-transitory computer-readable medium comprises instructions that, when executed by the processor, enable the processor to perform operations and a non-transitory computer-readable medium comprising processor-executable instructions that enable a processor to perform operations, comprising:
a) receiving, by a computing device, digital content associated with an original digital work; (see Agarwal paragraph [0034]: An image (original digital content) and its text may be evaluated to generate an image data hierarchy. The data hierarchy represents descriptive data for the image in order from more subjective to less subjective (more objective), attributes such as color, material type (knit/weave), article type (shirt, pants, etc.) may be known with specificity.; paragraph [0052]: receiving a set of tagged images, at least a portion of which are tagged with the subject value at the subject level and at least a portion of which are not tagged with the subject value at the subject level.)
b) analyzing, by the computing device, the digital content to determine a plurality of attribute-value pairs characterizing the digital content, wherein the attribute-value pairs adhere to a predefined namespace; (see Agarwal paragraph [0033]: Textual data may include one or more attribute/value pairs, e.g. (color, blue) describing features or attributes for one or more items of clothing depicted in the image. Where multiple items of clothing are shown (image, digital content), each item of clothing may be identified, and its corresponding attribute/value pairs associated with its identifier in the text; (namespace, attribute space used to generate attribute-value pairs))
d) generating, by the computing device, a measure for the digital content based on the calculated score associated with a relevance of the digital content; (see Agarwal paragraph [0179]: products as identified at step 2106 may be ranked, assigned scores indicating their estimated relevance to a user, or otherwise be in an order indicating the expected relevance of one product relative to another product, i.e. a list of product identifiers ordered from most to least relevant.; (measure: relevance of information to original digital content)) and
e) causing, by the computing device, the measure to be displayed in association with the digital content when the digital content is presented to a user. (see Agarwal paragraph [0181]: The method may then include ordering the set of products according to the scores Gi, e.g., lowest to highest where a low score indicates higher relevance or highest to lowest where a high score indicates higher relevance. only the top N products with the highest relevance are selected whereas all products remain in the set and are presented (displayed) in order of relevance in response to scrolling or clicking through multiple pages of results; (items ordered based upon relevance (measure)))
Agarwal does not specifically disclose for b) a multi-dimensional attribute namespace.
However, Luo discloses wherein for b) a multi-dimensional attribute namespace. (see Luo paragraph [0010]: provide for generation of a multi-dimensional tag metric in a cloud resource management environment. More specifically, a tagging namespace is provided for managing a resource in the cloud resource management environment. This namespace comprises at least two dimensions and a plurality of positions. A set of tags associated with the resource are received into the tagging namespace. A match of each tag of the set of tags to a position within the namespace into which that tag was received is verified ... Alternatively, in the case verification is successful, the tag-containing namespace is validated as a multi-dimensional tag metric.; paragraph [0078]: Each position 132N in tagging namespace 130 can be associated with a particular attribute indicating a type, category, or other feature of a tag that can be received into that position 132N.)
It would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to modify Agarwal for b) a multi-dimensional attribute namespace as taught by Luo. One of ordinary skill in the art would have been motivated to employ the teachings of Luo for the usage of multiple data processing techniques such as the utilization of a multi-dimensional namespace processing attribute value data pairs. (see Luo paragraph [0010])
Furthermore, Agarwal discloses for c) calculating, by the computing device, a score for the digital content based on the determined attribute-value pairs. (see Agarwal paragraph [0198]: calculating relevance scores for each query and generating an attribute map (attribute-value pairs) according to the queries (images, digital content) and their relevance scores. Calculating relevance scores for a query may be a function of popularity, i.e. the relevance score for a query may be a function of a number of times the query has occurred among the queries collected)
Agarwal-Luo does not specifically disclose for c) calculating a distance between sets of attribute-value of the digital content and that of other content.
However, Sarma discloses wherein for c) calculating a distance between sets of attribute-value of the digital content and that of other content. (see Sarma paragraph [0047]: input data is further processed to obtain an attribute distance parameter between values of each attribute within the item metadata, ... . In one embodiment, the processing engine 211 processes the user data stored within the user tables 310, the item data stored within the item tables 330, and the item metadata stored within the attribute tables 340 to calculate an attribute distance parameter between attribute values pertaining to each attribute of art item.; paragraph [0048]: an item distance parameter between any pair of items is further computed, as described in detail below in connection with FIG. 7. In one embodiment, the processing engine 211 uses the attribute distance parameters to calculate the item distance parameter between a pair of items in order to determine if the respective items are similar or not and the degree of similarity between items.)
It would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to modify Agarwal and Luo for c) calculating a distance between sets of attribute-value of the digital content and that of other content as taught by Sarma. One of ordinary skill in the art would have been motivated to employ the teachings of Sarma for the flexibility of a system that enables the calculation of multiple associated parameters for attribute information such as a distance between attribute parameters. (see Sarma paragraph [0047]; paragraph [0048])
Furthermore, for Claim 21, Agarwal discloses wherein a processor; and a non-transitory computer-readable medium coupled with the processor, wherein the computer-readable medium comprises instructions that, when executed by the processor, enable the processor to perform operations. (see Agarwal paragraph [0244]: Processor(s) include one or more processors or controllers that execute instructions stored in memory device(s) and/or mass storage device(s).; paragraph [0256]: Computer-executable instructions comprise, for example, instructions and data which, when executed at a processor, cause a general purpose computer, special purpose computer, or special purpose processing device to perform a certain function or group of functions.)
Furthermore, for Claim 22, Agarwal discloses wherein processor-executable instructions that enable a processor to perform operations. (see Agarwal paragraph [0244]: Processor(s) include one or more processors or controllers that execute instructions stored in memory device(s) and/or mass storage device(s).; paragraph [0256]: Computer-executable instructions comprise, for example, instructions and data which, when executed at a processor, cause a general purpose computer, special purpose computer, or special purpose processing device to perform a certain function or group of functions.)
Regarding Claim 2, Agarwal-Luo-Sarma discloses the method of claim 1, wherein the score relates to bias in the digital content. (see Agarwal paragraph [0060]: Images may be tagged according to the method whereby trained models generate the image data hierarchy for the images. The images may be further tagged with either a positive or negative feedback. images posted by the user or referenced by the user's social media activity may be assumed to be positive unless associated with a negative sentiment (e.g., “dislike”, one-star rating, etc.) on the social media platform from which the image was obtained.; (digital content associated with a bias (digital content: preference or negative))
Regarding Claim 3 Agarwal-Luo-Sarma discloses the method of claim 1, wherein the attribute-value pairs include at least one of: a credibility of a source of the digital content, a reputation of the source of the digital content, a number of supporting citations, or a presence of circular citations. (see Agarwal paragraph [0074]: The top Q products with the highest scores (best source for digital content) may be selected as search results in this example and a listing of those products may be presented to the user. In other embodiments, products with scores below a threshold may be filtered out and the remainder presented as search results. The listing of search results may be ordered according to the scores with the highest score first.; (selected: a credibility of a source of the digital content))
Regarding Claim 4, Agarwal-Luo-Sarma discloses the method of claim 1, further comprising updating the score over time based on newly available information associated with the digital content. (see Agarwal paragraph [0066]: The user preference hierarchy as compiled at step may then be used to make product recommendations to a user. As additional feedback on images and/or social media images are received, the method may be repeated in order to update the user preference hierarchy with the new information.; (associated score for digital content updated))
Regarding Claim 5, Agarwal-Luo-Sarma discloses the method of claim 1, wherein the predefined namespace is part of a well-defined attribute space for an edition containing the digital content. (see Agarwal paragraph [0033]: Textual data may include one or more attribute/value pairs, e.g. (color, blue) describing features or attributes for one or more items of clothing depicted in the image. Where multiple items of clothing (images; digital content) are shown, each item of clothing may be identified, and its corresponding attribute/value pairs associated with its identifier in the text; (namespace, attribute space used to generate attribute-value pairs))
Regarding Claim 6, Agarwal-Luo-Sarma discloses the method of claim 1, further comprising:
a) determining a user context; and b) calculating the score based on the determined user context. (see Agarwal paragraph [0126]: some or all of the inputs used to derive S.sub.s and S.sub.t as described above may be input to a machine learning model that that scores a product. The machine learning model may have been trained to output a score based on these inputs, the score indicating a likelihood of user interest in a product (user context). Note that inputs as input to the machine learning model or in the calculation of S.sub.s and S.sub.t may further be weighted by regency such that more recently received data is given greater weight than earlier received data.; paragraph [0179]: products as identified at step 2106 may be ranked, assigned scores indicating their estimated relevance to a user, or otherwise be in an order indicating the expected relevance of one product relative to another product, i.e. a list of product identifiers ordered from most to least relevant.; (measure: relevance of information to original digital content))
Regarding Claim 7, Agarwal-Luo-Sarma discloses the method of claim 1, further comprising:
a) identifying a cluster of content consumed by the user; (see Agarwal paragraph [0140]: the product vectors may be processed by a clustering algorithm that groups the products vectors into a plurality of clusters based on similarity. Any clustering algorithm known in the art may be used and any number of clusters may be generated.) and
b) determining if the cluster indicates a potential filter bubble. (see Agarwal paragraph [0146]: The result of the algorithm is a set of product clusters. Each of these product clusters may be further processed by an image clustering algorithm. As will be discussed below, groups of images for product records 1202 in the same cluster may be presented to a user in order to determine the user's affinity (positive or negative) to that cluster 1402.; paragraph [0060]: Images may be tagged according to the method whereby trained models generate the image data hierarchy for the images. The images may be further tagged with either a positive or negative feedback. images posted by the user or referenced by the user's social media activity may be assumed to be positive unless associated with a negative sentiment (e.g., “dislike”, one-star rating, etc.) on the social media platform from which the image was obtained.; (digital content associated with a bias (preference or negative); (filter bubble; bias))
Regarding Claim 11, Agarwal-Luo-Sarma discloses the method of claim 1, further comprising generating a machine learning training dataset based on the attribute-value pairs and user interactions with the digital content. (see Agarwal paragraph [0126]: some or all of the inputs used to derive S.sub.s and S.sub.t as described above may be input to a machine learning model that that scores a product. The machine learning model may have been trained to output a score based on these inputs, the score indicating a likelihood of user interest in a product (user context). Note that inputs as input to the machine learning model or in the calculation of S.sub.s and S.sub.t may further be weighted by regency such that more recently received data is given greater weight than earlier received data.)
Regarding Claim 14, Agarwal-Luo-Sarma discloses the method of claim 1, further comprising:
a) identifying changes in the digital content over time; (see Agarwal paragraph [0096]: product recommendations for a user may be provided based on session data. In particular, user activity at different points in time and in different browsing sessions separated by hours or even days or weeks may be associated to the same session and recommendations may be provided primarily based on a session profile; paragraph [0066]: The user preference hierarchy as compiled at step may then be used to make product recommendations to a user. As additional feedback on images and/or social media images are received, the method may be repeated (changes over time) in order to update the user preference hierarchy with the new information.; (associated score updated)) and
b) updating the score based on the identified changes. (see Agarwal paragraph [0066]: The user preference hierarchy as compiled at step may then be used to make product recommendations to a user. As additional feedback on images and/or social media images are received, the method may be repeated in order to update the user preference hierarchy with the new information.)
Regarding Claim 15, Agarwal-Luo-Sarma discloses the method of claim 1, further comprising generating a timeline of changes to the score over time. (see Agarwal paragraph [0096]: product recommendations for a user may be provided based on session data. In particular, user activity at different points in time and in different browsing sessions separated by hours or even days or weeks may be associated to the same session and recommendations may be provided primarily based on a session profile; paragraph [0066]: The user preference hierarchy as compiled at each step may then be used to make product recommendations to a user. As additional feedback on images and/or social media images are received, the method may be repeated in order to update the user preference hierarchy 114 with the new information.; (associated score updated over time))
Regarding Claim 16, Agarwal-Luo-Sarma discloses the method of claim 1, wherein the measure includes a visual representation of the score. (see Agarwal paragraph [0074]: The top Q products with the highest scores may be selected as search results in this example and a listing of those products may be presented to the user; products with scores below a threshold may be filtered out and the remainder presented (visual representation) as search results. The listing of search results may be ordered according to the scores with the highest score first.)
Regarding Claim 19, Agarwal-Luo-Sarma discloses the method of claim 1, wherein the digital content is part of a social media post, and the measure is displayed as part of a social media timeline. (see Agarwal paragraph [0066]: The user preference hierarchy as compiled at step may then be used to make product recommendations to a user. As additional feedback on images and/or social media images are received, the method may be repeated in order to update the user preference hierarchy with the new information.)
Regarding Claim 20, Agarwal-Luo-Sarma discloses the method of claim 1, further comprising identifying change features of interest to the user. (see Agarwal paragraph [0066]: The user preference (interest to user) hierarchy as compiled at step may then be used to make product recommendations to a user. As additional feedback on images and/or social media images are received, the method may be repeated in order to update the user preference hierarchy with the new information.)
6. Claims 8, 9 are rejected under 35 U.S.C. 103 as being unpatentable over Agarwal in view of Luo and further in view of Sarma and Dykstra et al. (US Patent No. 8,554,640).
Regarding Claim 8, Agarwal-Luo-Sarma discloses the method of claim 7, further comprising recommendations. (see Agarwal paragraph [0066]: The user preference hierarchy as compiled at step may then be used to make product recommendations to a user. As additional feedback on images and/or social media images are received, the method may be repeated in order to update the user preference hierarchy with the new information.; paragraph [0033]: Textual data may include one or more attribute/value pairs, e.g. (color, blue) describing features or attributes for one or more items of clothing depicted in the image. Where multiple items of clothing are shown, each item of clothing may be identified, and its corresponding attribute/value pairs associated with its identifier in the text; (namespace, attribute space used to generate attribute-value pairs))
Agarwal-Luo-Sarma does not specifically disclose recommending content with an opposing viewpoint.
However, Dykstra discloses wherein recommending content with an opposing viewpoint if a filter bubble is detected. (see Dykstra col 13, lines 20-26: select users who enjoyed content items of a similar type or having similar characteristics, such as being of a particular length or reading level, and recommend other content items having those same characteristics. Alternatively, works having opposing or alternative viewpoints or positions might be recommended in some situations.; (recommend content of an opposing viewpoint))
It would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to modify Agarwal-Luo-Sarma for recommending content with an opposing viewpoint. as taught by Dykstra. One of ordinary skill in the art would have been motivated to employ the teachings of Dykstra for the benefits achieved from the flexibility of a system that enables the presentation of multiple viewpoints associated with processed digital content. (see Dykstra col 13, lines 20-26)
Regarding Claim 9, Agarwal-Luo-Sarma discloses the method of claim 1, including attribute-value pairs. (see Agarwal paragraph [0033]: Textual data may include one or more attribute/value pairs, e.g. (color, blue) describing features or attributes for one or more items of clothing depicted in the image. Where multiple items of clothing are shown, each item of clothing may be identified, and its corresponding attribute/value pairs associated with its identifier in the text; (namespace, attribute space used to generate attribute-value pairs)
Agarwal-Luo-Sarma does not specifically disclose an opposing attribute-value pair representing an opposing viewpoint.
However, Dykstra discloses wherein include an opposing attribute-value pair representing an opposing viewpoint. (see Dykstra col 13, lines 20-26: select users who enjoyed content items of a similar type or having similar characteristics, such as being of a particular length or reading level, and recommend other content items having those same characteristics. Alternatively, works having opposing or alternative viewpoints or positions might be recommended in some situations.; (recommend content of an opposing viewpoint))
It would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to modify Agarwal-Luo-Sarma for an opposing attribute-value pair representing an opposing viewpoint as taught by Dykstra. One of ordinary skill in the art would have been motivated to employ the teachings of Dykstra for the benefits achieved from the flexibility of a system that enables the presentation of multiple viewpoints associated with processed digital content. (see Dykstra col 13, lines 20-26)
7. Claim 10 is rejected under 35 U.S.C. 103 as being unpatentable over Agarwal in view of Luo and further in view of Sarma and Gudupally et al. (US Patent No. 11,741,521).
Regarding Claim 10, Agarwal-Luo-Sarma discloses the method of claim 1, wherein content based on the attribute-value pairs and the score. (see Agarwal paragraph [0033]: Textual data may include one or more attribute/value pairs, e.g. (color, blue) describing features or attributes for one or more items of clothing depicted in the image. Where multiple items of clothing are shown, each item of clothing may be identified, and its corresponding attribute/value pairs associated with its identifier in the text; (namespace, attribute space used to generate attribute-value pairs); paragraph [0198]: calculating relevance scores for each query and generating an attribute map (attribute-value pairs) according to the queries (images, digital content) and their relevance scores.)
Agarwal-Luo-Sarma does not specifically disclose a validity measure for an assertion.
However, Gudupally discloses wherein further comprising determining a validity measure for an assertion made in the digital content. (see Gudupally col 10, lines 10-41: the confidence score can be a measure of confidence in the accuracy of the value information; ... review the information generated by the machine learning algorithm to check the information for validity, which can involve the category specialist raising or lowering the confidence score when the machine-generated content is accurate or not accurate, respectively. By using confidence scores, category specialists can enter information or which they are not sure, and vendors can be able to update and override the information. In many embodiments, each record in the content catalog for an attribute and value pair can include the confidence score for that information.)
It would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to modify Agarwal-Luo-Sarma for a validity measure for an assertion as taught by Gudupally. One of ordinary skill in the art would have been motivated to employ the teachings of Gudupally for the benefits achieved from a system that designates the accuracy of determined scoring parameter information (higher confidence, lower confidence). (see Gudupally col 10, lines 10-41)
8. Claim 12 is rejected under 35 U.S.C. 103 as being unpatentable over Agarwal in view of Luo and further in view of Sarma and Kumar et al. (US PGPUB No. 20190236637).
Regarding Claim 12, Agarwal-Luo-Sarma discloses the method of claim 11, further comprising:
a) training a machine learning model using the generated training dataset. (see Agarwal paragraph [0126]: some or all of the inputs used to derive S.sub.s and S.sub.t as described above may be input to a machine learning model that that scores a product. The machine learning model may have been trained to output a score based on these inputs, the score indicating a likelihood of user interest in a product (user context). Note that inputs as input to the machine learning model or in the calculation of S.sub.s and S.sub.t may further be weighted by regency such that more recently received data is given greater weight than earlier received data.)
Furthermore, Agarwal discloses for b) content based on attribute-value pairs of the new content. (see Agarwal paragraph [0033]: Textual data may include one or more attribute/value pairs, e.g. (color, blue) describing features or attributes for one or more items of clothing depicted in the image. Where multiple items of clothing are shown (image, digital content), each item of clothing may be identified, and its corresponding attribute/value pairs associated with its identifier in the text; (namespace, attribute space used to generate attribute-value pairs))
Agarwal-Luo-Sarma does not specifically disclose for b) predict user interest in new content.
However, Kumar discloses:
b) using the trained model to predict user interest in new content. (see Kumar paragraph [0103]: using a conventional recommender system, huge amounts of raw data can be used to predict products (digital content) that can be of interest to users)
It would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to modify Agarwal-Luo-Sarma for b) predict user interest in new content as taught by Kumar. One of ordinary skill in the art would have been motivated to employ the teachings of Kumar for the flexibility achieved from a system that enables the determination of predictions of interest to a particular user. (see Kumar paragraph [0103])
9. Claim 13 is rejected under 35 U.S.C. 103 as being unpatentable over Agarwal in view of Luo and further in view of Sarma and Drake et al. (US PGPUB No. 20140236769).
Regarding Claim 13, Agarwal-Luo-Sarma discloses the method of claim 1.
Agarwal-Luo-Sarma does not specifically disclose content is part of an edition having a defined lifetime, and preventing changes to the score (content) after the lifetime of the edition has ended.
However, Drake discloses wherein the digital content is part of an edition having a defined lifetime, and further comprising preventing changes to the score after the lifetime of the edition has ended. (see Drake paragraph [0021]: the update is a rule (or modification of a rule) that is imposed upon content stored in the product and/or product package proximity-based sensor and/or transceiver. For example, the rule may be a content life cycle rule that is fixed. The fixed content life cycle rule prevents transmission of the content from the product and/or product package proximity-based sensor and/or transceiver to a computing device (such as a mobile phone) after expiration of a static period of time. For instance, a user may be able to obtain static life cycle content from the product and/or product package proximity-based sensor and/or transceiver with a computing device. The user may only be able to obtain that static life cycle content for a fixed period of time. The static life cycle content may be automatically deleted by the product and/or product package proximity-based sensor and/or transceiver after expiration of the fixed period of time.)
It would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to modify Agarwal-Luo-Sarma for content is part of an edition having a defined lifetime, and preventing changes to the score (digital content) after the lifetime of the edition has ended as taught by Drake. One of ordinary skill in the art would have been motivated to employ the teachings of Drake for the flexibility achieved from a system that enables content to be controlled and limited to a fixed time period for updates. (see Drake paragraph [0021])
10. Claim 17 is rejected under 35 U.S.C. 103 as being unpatentable over Agarwal in view of Luo and further in view of Sarma and Czuba et al. (US Patent No. 10,223,637).
Regarding Claim 17, Agarwal-Luo-Sarma discloses the method of claim 1.
Agarwal-Luo-Sarma does not specifically disclose for a) determining a level of expertise, and for b) leveling a complexity of information presented based on determined level of expertise.
However, Czuba discloses further comprising:
a) determining a level of expertise of the user; and b) leveling a complexity of information presented in the measure based on the determined level of expertise. (see Czuba col ,5 line 58 - col 6, 12: A user profile for registered or unregistered users may include user interactions with subsystems of the search system, e.g., a web search system, an image search system, a map system, an email system, a social network system, a blogging system, a shopping system, just to name a few, topics of interest, and an indication of a level of expertise of the user for each of the topics of interest, e.g., novice or expert. The topics of interest and levels of expertise may include user-provided data or system-generated data based on a user's interaction with the search system.)
It would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to modify Agarwal-Luo-Sarma for a) determining a level of expertise, and for b) leveling a complexity of information presented based on determined level of expertise as taught by Czuba. One of ordinary skill in the art would have been motivated to employ the teachings of Czuba for the flexibility of a system that utiulizes previous processing information such as a determined level of expertise in evaluating the processing of digital content in a network environment. (see Czuba col ,5 line 58 - col 6, 12)
11. Claim 18 is rejected under 35 U.S.C. 103 as being unpatentable over Agarwal in view of Luo and further in view of Sarma and Lokanath et al. (US PGPUB No. 20210241241).
Regarding Claim 18, Agarwal-Luo-Sarma discloses the method of claim 1, further comprising recording the score and associated attribute-value pairs. (see Agarwal paragraph [0033]: Textual data may include one or more attribute/value pairs, e.g. (color, blue) describing features or attributes for one or more items of clothing depicted in the image. Where multiple items of clothing are shown, each item of clothing may be identified, and its corresponding attribute/value pairs associated with its identifier in the text; (namespace, attribute space used to generate attribute-value pairs))
Agarwal-Luo-Sarma does not specifically disclose a notarized ledger.
However, Lokanath discloses wherein information associated with a notarized ledger. (see Lokanath paragraph [0115]: The blockchain network ensures that the information that is shared amongst the bots is persistently and immutably (e.g., unchangeably or permanently) stored in a database architecture called the ledger or blockchain ledger which is distributed across multiple hosts, as is depicted by the architecture 601 in which there are multiple blockchain ledgers 605A, 605B, 605C, and 605D.; paragraph [0103]: transmit data objects consisting of attribute-value pairs and array data types or any other serializable value.)
It would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to modify Agarwal-Luo-Sarma for a notarized ledger as taught by Lokanath. One of ordinary skill in the art would have been motivated to employ the teachings of Lokanath for the enhanced security achieved from the utilization of blockchain technology in the storage of processed digital content within a network environment. (see Lokanath paragraph [0115])
Conclusion
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/CJ/
November 17, 2025
/SHEWAYE GELAGAY/Supervisory Patent Examiner, Art Unit 2436