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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 8-19-2026 has been entered.
2. Claims 1 - 22 are pending. Claims 1, 2, 4, 6, 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 8-19-2026, with respect to the rejection(s) under Agarwal in view of Luo and further in view of Sarma and Yang 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 Therefore, the rejection has been withdrawn.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 and Yang and OMalley.
A. Applicant argues on page 8 of Remarks: ... 1. The Art of Record Does Not Teach or Suggest a Bias Score Correlated with a Viewpoint.
The Examiner respectfully disagrees. OMalley discloses a determination of a bias score and the bias score is associated with a particular viewpoint. (see OMalley cols 193-194: the IPACE system would preferably continually track and analyze data/content to generate a bias score per participant (bias score associated with a particular participant viewpoint), reporter, TV-talent, publisher, media outlet, editor, source, witness, contributor, photographer, consumer, where the IPACE system analysis would comprise a stated, actor-stated, ascertained, discerned, relatively perceived, and/or predicted database of participant profiles. The participant profile may comprise all previous and current addresses, schools attended, sports participant, a born location, political affiliations, religious affiliation, organization affiliations, web posting analysis, email analysis,; the IPACE system would preferably provide an ability to search, data/content, where a particular user can include and/or incorporate filtering for a particular preferred bias)
B. Applicant argues on pages 8-9 of Remarks: ... that (1) a bias score correlated with a viewpoint is calculated for the digital content and (2) a measure for the digital content is generated based on the calculated bias score.
The Examiner respectfully disagrees. OMalley discloses a determination of a bias score and the bias associated with a particular viewpoint. (see OMalley cols 193-194: the IPACE system would preferably continually track and analyze data/content to generate a bias score per participant (bias score associated with a particular participant viewpoint), reporter, TV-talent, publisher, media outlet, editor, source, witness, contributor, photographer, consumer, where the IPACE system analysis would comprise a stated, actor-stated, ascertained, discerned, relatively perceived, and/or predicted database of participant profiles. The participant profile may comprise all previous and current addresses, schools attended, sports participant, a born location, political affiliations, religious affiliation, organization affiliations, web posting analysis, email analysis,; the IPACE system would preferably provide an ability to search, data/content, where a particular user can include and/or incorporate filtering for a particular preferred bias)
C. Applicant argues on page 9 of Remarks: ... Accordingly, claim 1 and its associated dependent claims are patentable over the art of record. Independent claims 21 and 22 recite similar features to those discussed above with respect to claim 1. Thus, claims 21 and 22 are patentable over the art of record for at least the same reasons as those discussed above with respect to claim 1.
Independent claims 21, 22 have similar limitations as independent claim 1. Responses to arguments against independent claim 1 also answer arguments against independent claims 21, 22. Responses to arguments against the independent claims also answer arguments against the associated dependent claims.
D. Applicant argues on page 9 of Remarks: ... 2. Yang Does Not Teach or Suggest Comparing a Product of a Distance and a Measure to a Threshold Value
The Examiner respectfully disagrees. Yang discloses a combination of a distance parameter and a similarity parameter utilized in the determination of filtering of pixel information within digital content. Yang discloses as the distance parameter increases the associated weight value is decreased and as the similarity parameter increases the associated weight value is increased. And, Yang discloses a comparison to threshold parameters for determining filtering. (see Yang paragraph [0063]: according to the current search frame in each neighborhood pixels P11 to P77 and a distance and the similarity and the intensity parameter of the pixel P11 to the current pixel Pc is determined P77 with respect to a current pixel Pc of the weights (MEG) (step. . when the one neighbourhood pixel distance of the current pixel Pc is far, decreases the weight of the neighborhood pixel, when a neighborhood pixel distance is closer, the current pixel Pc increases the weight of the neighbourhood pixel; when the similarity of a neighborhood pixel and the current pixel Pc, decreases the weight of the neighborhood pixel, when a neighborhood pixel and the current pixel Pc of the high similarity, increases the weight of the adjacent pixels. all the adjacent pixels P11 to in the current search block of P77 according to the weight proportion is determined with distance and similarity of the current pixel Pc, matched strength parameter can determine each neighbourhood pixel P11 to P77 relative to the weight of the current pixel Pc,; paragraph [0051]: image noise filtering method of the embodiment of the invention, the image denoising method of the present invention is directed to removing noise front image (noisy) for image reconstruction to removing noise after image (denoised) is formed,; paragraph [0050]: the sum of the energy and is compared with the threshold value; to determine the frequency parameter (step S223). wherein the threshold value is a numerical value, the size can be determined according to the noise filtering effect to be achieved.)
E. Applicant argues on page 11 of Remarks: ... Yang is Non-Analogous Art, and There Is No Motivation to Combine Yang with Agarwal, Luo, and Sarma.
The Examiner respectfully disagrees. Yang discloses manipulating digital content. The claimed invention discloses manipulating digital content such as receiving and analyzing digital content.
Yang discloses a combination of a distance parameter and a similarity parameter utilized in the determination of filtering of pixel information within digital content. Yang discloses as the distance parameter increases the associated weight value is decreased and as the similarity parameter increases the associated weight value is increased. And, Yang discloses a comparison to threshold parameters for determining filtering. (see Yang paragraph [0063]: according to the current search frame in each neighborhood pixels P11 to P77 and a distance and the similarity and the intensity parameter of the pixel P11 to the current pixel Pc is determined P77 with respect to a current pixel Pc of the weights (MEG) (step. . when the one neighbourhood pixel distance of the current pixel Pc is far, decreases the weight of the neighborhood pixel, when a neighborhood pixel distance is closer, the current pixel Pc increases the weight of the neighbourhood pixel; when the similarity of a neighborhood pixel and the current pixel Pc, decreases the weight of the neighborhood pixel, when a neighborhood pixel and the current pixel Pc of the high similarity, increases the weight of the adjacent pixels. all the adjacent pixels P11 to in the current search block of P77 according to the weight proportion is determined with distance and similarity of the current pixel Pc, matched strength parameter can determine each neighbourhood pixel P11 to P77 relative to the weight of the current pixel Pc,; paragraph [0051]: image noise filtering method of the embodiment of the invention, the image denoising method of the present invention is directed to removing noise front image (noisy) for image reconstruction to removing noise after image (denoised) is formed,; paragraph [0050]: the sum of the energy and is compared with the threshold value; to determine the frequency parameter (step S223). wherein the threshold value is a numerical value, the size can be determined according to the noise filtering effect to be achieved.)
F. Applicant argues on page 13 of Remarks: ... 2. There Is No Motivation to Combine Yang with Agarwal, Luo, and Sarma.
The Examiner respectfully disagrees. A 103 rejection based on multiple references is a legitimate technique according to the MPEP. The 103 rejection allows portions of the rejection citations for a claimed invention to come from different prior art references. The rejection to each independent and dependent claim includes a citation from the referenced prior art that discloses the basis for the rejection. Each obviousness combination clearly indicates the claim limitation(s) the combined referenced prior art teaches. In addition, a cited passage from the referenced prior art indicates the motivation for the obviousness combination. Each obviousness combination’s disclosure is equivalent to the Applicant’s claim limitation(s) for the claimed invention. Achieved advantage is a valid motivation for the combination of referenced prior art. The rejection of each referenced prior art combination states a motivation for the combination, which translates to an achieved advantage for the combination.
The following responses are reiterated:
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)
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.)
Yang discloses a combination of a distance parameter and a similarity parameter utilized in the determination of filtering of pixel information within digital content as stated above. Yang discloses as the distance parameter increases the associated weight value is decreased and as the similarity parameter increases the associated weight value is increased as stated above. And, Yang discloses a comparison to threshold parameters for determining filtering 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.)
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) and Yang et al. (Patent No. CN 102045514 A) and O’Malley (US Patent No. 12,094,018, “referred to as OMalley”).
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
f) 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 bias 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-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])
Agarwal-Luo-Sarma does not specifically disclose for c) a bias score for the digital content that is correlated with a viewpoint; and for d) a bias score.
However, OMalley discloses for c) a bias score for the digital content that is correlated with a viewpoint; and for d) a bias score. (see OMalley cols 193-194: the IPACE system would preferably continually track and analyze data/content to generate a bias score per participant, reporter, TV-talent, publisher, media outlet, editor, source, witness, contributor, photographer, consumer, where the IPACE system analysis would comprise a stated, actor-stated, ascertained, discerned, relatively perceived, and/or predicted database of participant profiles. The participant profile may comprise all previous and current addresses, schools attended, sports participant, a born location, political affiliations, religious affiliation, organization affiliations, web posting analysis, email analysis,; the IPACE system would preferably provide an ability to search, data/content, where a particular user can include and/or incorporate filtering for a particular preferred bias)
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 c) a bias score for the digital content that is correlated with a viewpoint; and for d) a bias score as taught by OMalley. One of ordinary skill in the art would have been motivated to employ the teachings of OMalley for the benefits achieved from the flexibility of a system that enables the processing of multiple parameters such as a bias score associated with a viewpoint. (see OMalley cols 193-194)
Agarwal-Luo-Sarma- OMalley does not explicitly disclose for e) controlling whether the digital content (pixel information is filtered) is displayed (pixel information is filtered) based on a combination of the distance and the measure, and down-weights the distance and up-weights the measure and compares product to a threshold value to control whether the digital content is displayed (filtered).
However, Yang discloses:
e) controlling, by the computing device, whether the digital content is displayed based on a combination of the distance and the measure wherein the combination of the distance and the measure down-weights the distance and up-weights the measure and compares a product thereof to a threshold value to control whether the digital content is displayed. (see Yang paragraph [0063]: according to the current search frame in each neighborhood pixels P11 to P77 and a distance and the similarity and the intensity parameter of the pixel P11 to the current pixel Pc is determined P77 with respect to a current pixel Pc of the weights (MEG) (step. . when the one neighbourhood pixel distance of the current pixel Pc is far, decreases the weight of the neighborhood pixel, when a neighborhood pixel distance is closer, the current pixel Pc increases the weight of the neighbourhood pixel; when the similarity of a neighborhood pixel and the current pixel Pc, decreases the weight of the neighborhood pixel, when a neighborhood pixel and the current pixel Pc of the high similarity, increases the weight of the adjacent pixels. all the adjacent pixels P11 to in the current search block of P77 according to the weight proportion is determined with distance and similarity of the current pixel Pc, matched strength parameter can determine each neighbourhood pixel P11 to P77 relative to the weight of the current pixel Pc,; paragraph [0051]: image noise filtering method of the embodiment of the invention, the image denoising method of the present invention is directed to removing noise front image (noisy) for image reconstruction to removing noise after image (denoised) is formed,; paragraph [0050]: the sum of the energy and is compared with the threshold value; to determine the frequency parameter (step S223). wherein the threshold value is a numerical value, the size can be determined according to the noise filtering effect to be achieved.)
It would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to modify Agrawal-Luo-Sarma for e) controlling whether the digital content (pixel information is filtered) is displayed based on a combination of the distance and the measure, and down-weights the distance and up-weights the measure and compares product to a threshold value to control whether the digital content is displayed (filtered) as taught by Yang. One of ordinary skill in the art would have been motivated to employ the teachings of Yang for the flexibility of a system that enables the utilization of multiple parameters such as distance and similarity in the determination of displaying content. (see Yang paragraph [0063]; paragraph [0051]; paragraph [0050])
Specification paragraph [0147] discloses content filtering based upon distance and similarity parameters. (Specification paragraph [0147]: “ ... The inverse of the number of links multiplied by a similarity score (e.g., a Euclidean distance in the attribute space, hamming distance, etc.) could be used for filtering. This approach down-weights distances, but up-weights similarity. If the product of the two measures, possibly normalized, fails to satisfy a threshold value, then the associated content would be filtered.)
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-Yang-OMalley discloses the method of claim 1, wherein the viewpoint is associated with a political affiliation. (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))
Bingham does not explicitly disclose viewpoint is associated with a political affiliation.
However, OMalley discloses wherein the viewpoint is associated with a political affiliation. (see OMalley cols 193-194: the IPACE system would preferably continually track and analyze data/content to generate a bias score per participant, reporter, TV-talent, publisher, media outlet, editor, source, witness, contributor, photographer, consumer, where the IPACE system analysis would comprise a stated, actor-stated, ascertained, discerned, relatively perceived, and/or predicted database of participant profiles. The participant profile may comprise all previous and current addresses, schools attended, sports participant, a born location, political affiliations, religious affiliation, organization affiliations, web posting analysis, email analysis,; the IPACE system would preferably provide an ability to search, data/content, where a particular user can include and/or incorporate filtering for a particular preferred bias)
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 viewpoint is associated with a political affiliation as taught by OMalley. One of ordinary skill in the art would have been motivated to employ the teachings of OMalley for the benefits achieved from the flexibility of a system that enables the processing of multiple parameters such as a bias score associated with a viewpoint. (see OMalley cols 193-194)
Regarding Claim 3 Agarwal-Luo-Sarma-Yang-OMalley 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-Yang-OMalley 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))
OMalley discloses a bias score as stated above.
Regarding Claim 5, Agarwal-Luo-Sarma-Yang-OMalley 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-Yang-OMalley 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))
OMalley discloses a bias score as stated above.
Regarding Claim 7, Agarwal-Luo-Sarma-Yang-OMalley 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-Yang-OMalley 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-Yang-OMalley 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-Yang-OMalley 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-Yang-OMalley 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-Yang-OMalley 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-Yang-OMalley 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 Yang and OMalley and Dykstra et al. (US Patent No. 8,554,640).
Regarding Claim 8, Agarwal-Luo-Sarma-Yang-OMalley 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-Yang-OMalley 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-Yang-OMalley 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-Yang-OMalley 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-Yang-OMalley 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-Yang-OMalley 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 Yang and OMalley and Gudupally et al. (US Patent No. 11,741,521).
Regarding Claim 10, Agarwal-Luo-Sarma-Yang-OMalley 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-Yang-OMalley 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-Yang-OMalley 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 Yang and OMalley and Kumar et al. (US PGPUB No. 20190236637).
Regarding Claim 12, Agarwal-Luo-Sarma-Yang-OMalley 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-Yang-OMalley 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-Yang-OMalley 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 Yang and OMalley and Drake et al. (US PGPUB No. 20140236769).
Regarding Claim 13, Agarwal-Luo-Sarma-Yang-OMalley discloses the method of claim 1.
Agarwal-Luo-Sarma-Yang-OMalley 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-Yang-OMalley 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 Yang and OMalley and Czuba et al. (US Patent No. 10,223,637).
Regarding Claim 17, Agarwal-Luo-Sarma-Yang-OMalley discloses the method of claim 1.
Agarwal-Luo-Sarma-Yang-OMalley 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-Yang-OMalley 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 Yang and OMalley and Lokanath et al. (US PGPUB No. 20210241241).
Regarding Claim 18, Agarwal-Luo-Sarma-Yang-OMalley 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-Yang-OMalley 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-Yang-OMalley 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
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any extension fee pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the date of this final action.
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/CJ/
September 7, 2026
/SHEWAYE GELAGAY/ Supervisory Patent Examiner, Art Unit 2436
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