DETAILED ACTION
This action is in response to communication filed on 5/13/2026.
Claims 2-21 are pending.
Claims 2, 11, and 17 have been amended.
Claims 1 have been cancelled previously.
Notice of Pre-AIA or AIA Status
The present application is being examined under the pre-AIA first to invent provisions.
Response to Arguments
Applicant's arguments filed 5/13/2026 have been fully considered but they are not persuasive.
Applicant argues that the amended claims distinguish over the cited prior art because:
1) Baughman teaches making recommendation to the purchaser who has already selected/purchased an item (or recommending another member to that purchaser), and does not teach identifying a separate non-consuming “second user” based on correlation derived from consumption information of other users and then presenting item-related digital content to that second user; and
2) Sundaresan discloses community group item list sharing and review solicitation but does not teach determining correlations or identifying new non-consumer targets in the manner claimed. Applicant submits that the explicit limitation that the identified second/particular user “is not one of the one or more user identified by the consumption information as having consumed the item” further distinguishes the claims.
Examiner respectfully disagrees. The amended claims remain obvious over the combination of Sundaresan and Baughman for the reason set forth below. The amendments clarify the status of the recipient but do patentably distinguish over the cited prior art when the references are combined under 35 USC § 103.
Sundaresan teaches or suggest accessing user data that includes item information associated with a first user and consumption information from connected user, and causing digital content related to the item to be presented to a second user who shares a connection with the first user and who is not one of the users identified as having consumed the item.
Sundaresan discloses a network-based marketplace system in which a first user (item list creator) creates an item list (specific products or broad categories, e.g., gifts or occasions) and communicates that list to a community group. The community group includes users sharing direct or indirect relationships/attributes with the first user (e.g., family, friends, reputable/trusted users such as “shopper1,” or secondary groups). Consumption information includes reviews, opinions, satisfaction levels, add to cart actions, and purchases by one or more members of the group. The system updates the item list with member input (recommendations of specific items for broad categories, reviews) and presents the updated digital content (item lists, recommendations, shared carts, status notifications) back to the community group members via user interfaces.
Specifically, Sundaresan teaches presenting this item related digital content to group members who have not yet consumed or purchased the items on the list. The lists are created and shared precisely to solicit collaboration, recommendations, and input from potential consumers within the connected group (e.g., broad “gift for 12 year old daughter” categories or “portable music player” solicitation where many member have no prior purchase). Indirect relationship, reputable user granted selective access for reviews/recommendation, and rules/roles controlling visibility further demonstrates presentation to connected non-consumers. Messaging applications notify and promote to group members. See Sundaresan [0039-0040, 0043, 0047-0048, 0049, 0051-0053].
Thus, Sundaresan teaches or suggests the amended limitation that the second user “is not one of the one or more suer identified by the consumption information as having consumed the item” while still sharing a connection/relationship attribute with the first user.
Baughman teaches or suggests determining a set of correlations based on item information cand consumption information, and in response to determining the set of correlations, identifying a second user based on the set of correlations.
Baughman discloses techniques for making recommendation in a social network context. Upon selection of an item, a plurality of features of the item are determined. These features are prioritized by comparing them to features of previously purchased items (the purchaser’s own history or purchases by other members of the purchaser’s social network). Correlation values (explicitly including Pearson’s r) are calculated between features; low or negative correlations are treated as anomalous and given higher priority/significance. Time based filtering (purchases within a predetermined period) may be applied. Dendrograms may be constructed to group items by shared features. Based on the correlations and prioritized features, the system identifies relevant users or products. (See Baughman [0016-0023, 0027-0030]).
Baughman further teaches using consumption/purchase data from others users in the social network (not just the immediate purchasers) for these correlations and identification, nothing that it may be desirable to compare features to items purchased by others known to have similar interest or beliefs. This directly supports determining correlations for items and consumption information associated with connected users and identifying a second user based on those correlations (e.g., shared features or attributes indicating likelihood of interest)
It would have been obvious to one of ordinary skill in the art at the time the invention was filed to combine the teachings of Sundaresan and Baughman. One would have been motivated to combine Sundaresan’s community group item list sharing and presentation mechanism with Baughman’s correlation determination and feature prioritization techniques for the predictable result of providing more targeted, relevant recommendations within a social or community network. Specifically, it would have been obvious to:
Apply Baughman’s correlation engine (Pearson’s r on item features vs. consumption/purchase data, anomaly detection via low/negative correlations, time-based filtering, and social network purchase data) to the item information and consumption information (reviews, cart additions, purchases) within or extended from Sundaresan’s community groups;
Use the resulting correlations to identify which non-consuming second users (who share relationship attributes/connections with the first user and/or with actual consumers in the group) have a higher likelihood of consuming the item; and
Use Sundaresan’s exiting infrastructure (list updates, recommendation added to lists, messaging applications, rules/roles for targeted visibility) to cause the digital content related to the item to be presented to those identified high likelihood non-consuming second users.
This combination is nothing more than the predictable user or improvement of known techniques (correlation based recommendation and anomaly detection in social network per Baughman) in a known social/group shopping environment that already presents content to non-consuming connected user (per Sundaresan).
Applicant’s emphasis on the second suer not having consumed the item is directly taught by Sundaresan’s presentation of item lists and updated recommendations to the fully community group, including members without prior purchases. Baughman’s primary flow being “to the purchaser” does not negate the obviousness of adapting its correlation logic (which already draws on other user’s social network consumption data) to identify and target non-consumers when combined with Sundaresan’s group presentation model. The “in response to determining the set of correlations, identifying a second user based on the set of correlations” limitation is satisfied by Baughman’s correlation and identification steps applied in the Sundaresan environment.
The same rationale applies to independent claim 11 and 17, who amended limitations parallel those of claim 2.
Double Patenting
The rejection presented in the previous office action is withdrawn in view of Terminal Disclaimer filed 5/13/2026.
Claim Rejections - 35 USC § 103
The following is a quotation of pre-AIA 35 U.S.C. 103(a) which forms the basis for all obviousness rejections set forth in this Office action:
(a) A patent may not be obtained though the invention is not identically disclosed or described as set forth in section 102 of this title, if the differences between the subject matter sought to be patented and the prior art are such that the subject matter as a whole would have been obvious at the time the invention was made to a person having ordinary skill in the art to which said subject matter pertains. Patentability shall not be negatived by the manner in which the invention was made.
The factual inquiries set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied for establishing a background for determining obviousness under pre-AIA 35 U.S.C. 103(a) are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 2-21 are rejected under 35 U.S.C. 103 as being unpatentable over Sundaresan (US 2007/0239552) in view of Baughman et al. (US 2013/0138531).
Regarding claim 2, Sundaresan discloses a method comprising:
accessing, by a computer system, user data (Sundaresan discloses accesses user data via applications and interfaces to create and share items lists within community groups. This includes accessing data inputted by users; see [0029] “Item list application(s) 216 may be used in the network-based marketplace 112 by a user to create an item list to be shared within a community group created or designated by the user. The item list applications 216 may be accessed via a user interface that allows the user to create the item list and may operate in conjunction with the social networking applications 210, the rules applications 208 and the roles applications 209, thereby allowing the user to associate the item list with the community group) that includes:
item information that relates to an item associated with a first user (Sundaresan discloses the user data includes item information such as shared items lists and product listings associated with a first user (the item list creator); see [0015] “A data exchange platform, in an example form of a network-based provider 112, provides server-side functionality, via a network 114 (e.g., the Internet) to one or more clients. The one or more clients may include users that may utilize the network system 100 and more specifically, the network-based provider 112, to exchange data over the network 114. These transactions may include receiving and processing data from a multitude of users. The data may include, but is not limited to, shared item (shopping) lists, shared electronic shopping carts, product and service reviews, product, service, manufacture, and vendor recommendations, product and service listings, auction bids, feedback, etc”); and
consumption information that relates to a consumption of the item by one or more users, wherein the one or more users share a connection with the first (Sundaresan discloses consumption information includes reviews (opinions on satisfaction after use/purchase and purchase (adding to shared cart) by connected users (community group members sharing connections like family/friends with the first user); see [0024] “the network-based marketplace 112 includes review and recommendation applications 205. The social networking applications 210 may work in conjunction with the review and recommendation applications 205 to provide a user interface to facilitate the entry of reviews of the items on the list and recommendations for items on the list. A review may be a text entry of the community group member's opinion, a standard review form including check boxes indicating a level satisfaction, a combination of both, etc” and [0030] “electronic shopping cart application(s) 218 are used to create a shared electronic shopping cart used by the members of the community group to add and store items from the item list and its derivatives, such as the associated recommendations for more broadly defined items”);
wherein the second user is not one of the one or more users identified by the consumption information as having consumed the item (Sundaresan discloses presenting this item related digital content to group members who have not yet consumed or purchased the items on the list. The lists are created and shared precisely to solicit collaboration, recommendations, and input from potential consumers within the connected group (e.g., broad “gift for 12 year old daughter” categories or “portable music player” solicitation where many member have no prior purchase). Indirect relationship, reputable user granted selective access for reviews/recommendation, and rules/roles controlling visibility further demonstrates presentation to connected non-consumers. Messaging applications notify and promote to group members. See [0043] “This example illustrates the flexibility of the item list since an item can be specifically requested or can be broadly requested, in which case the item list creator is soliciting recommendations”); and
causing, by the computer system, digital content related to the item to be presented to the second user via a user interface on a device associated with the second user (Sundaresan discloses causing item-related content (shared item list) to be presented to second users (group members) via UI on their devices; see [0017] “The networking applications 130 may allow a user to generate an ace this item list, which may include items (e.g., products and services) and categories associated with items, or a combination thereof. In one embodiment, the items and categories of items are gifts associated with a designated entity or event (e.g., pet, friend, housewarming, etc.). The networking applications 130 may allow the user to distribute the item list to one or more groups defined by user (e.g., "my family," "my friends," etc.) or to groups at various levels in a predefined category (e.g., "auto group," "Ford group," "Ford Mustang Group," etc.)” and [0029] “The item list applications 216 may be accessed via a user interface that allows the user to create the item list and may operate in conjunction with the social networking applications 210”).
However, the prior art does not explicitly disclose the following:
determining, by the computer system, a set of correlations based on the item information and the consumption information;
in response to determining the set of correlations, the computer system identifying a second user based on the set of correlations; and
Baughman in the field of the same endeavor discloses techniques for making a recommendation to a purchaser and/or member of a social network. In particular, Baughman teaches the following:
determining, by the computer system, a set of correlations based on the item information and the consumption information (Baughman discloses determining correlation (e.g., Pearson’s r) based on item features (information) and previous consumptions (purchases) by users; see [0020] “In making a comparison between one or more feature of a selected item and features of a previously-purchased item or items, some embodiments of the invention include the calculation of a correlation score or value. One skilled in the art will recognize that there are any number of correlation scores or values that may be calculated. One such correlation value is a Pearson's correlation value, typically expressed as r” and [0024] “at S2A, a comparison is made to previously-purchased items”); and
in response to determining the set of correlations, the computer system identifying a second user based on the set of correlations (Baughman discloses after determining correlation ([0020]), identifying a second user (another member not yet connected) based on shared features from correlation; see [0023] “At S4, a recommendation is made to a purchaser and/or member of a social network. In some embodiments of the invention, the recommendation may include a product that shares at least one feature of the item selected by the purchaser/social network member. In other embodiments of the invention, the recommendation may include another member of the social network with whom the purchaser/social network member is not yet connected, but who has selected or purchased an item sharing at least one feature of the item selected by the purchaser/social network member”.
Therefore, it would have been obvious to a person of ordinary skill in the art at the time the invention was effectively filed to combine the teachings of Sundaresan and Baughman. One would have been motivated to combine Sundaresan’s community group item list sharing and presentation mechanism with Baughman’s correlation determination and feature prioritization techniques for the predictable result of providing more targeted, relevant recommendations within a social or community network. Specifically, it would have been obvious to:
Apply Baughman’s correlation engine (Pearson’s r on item features vs. consumption/purchase data, anomaly detection via low/negative correlations, time-based filtering, and social network purchase data) to the item information and consumption information (reviews, cart additions, purchases) within or extended from Sundaresan’s community groups;
Use the resulting correlations to identify which non-consuming second users (who share relationship attributes/connections with the first user and/or with actual consumers in the group) have a higher likelihood of consuming the item; and
Use Sundaresan’s exiting infrastructure (list updates, recommendation added to lists, messaging applications, rules/roles for targeted visibility) to cause the digital content related to the item to be presented to those identified high likelihood non-consuming second users.
This combination is nothing more than the predictable user or improvement of known techniques (correlation-based recommendation and anomaly detection in social network per Baughman) in a known social/group shopping environment that already presents content to non-consuming connected user (per Sundaresan).
Regarding claim 3, Sundaresan-Baughman discloses the method of claim 2, wherein the identifying of the second user includes:
determining, by the computer system, that the second user is likely to consume the item based on the set of correlations indicating that the second user shares one or more relationship attributes with one or more other users of the one or more users who consumed the item (Baughman [0023] “the recommendation may include another member of the social network with whom the purchaser/social network member is not yet connected, but who has selected or purchased an item sharing at least one feature of the item selected by the purchaser/social network member”).
Regarding claim 4, Sundaresan-Baughman discloses the method of claim 2, wherein the causing of the digital content to be presented to the second user includes:
generating, by the computer system based on the set of correlations, a recommendation that is related to the item, wherein the recommendation is presented via the user interface of the device associated with the second user (Baughman [0038] “recommendation program 430 enables computer system 420 to make a recommendation to a purchaser/social network member. To this extent, computer system 420 can acquire and/or utilize information before, during, and after making a recommendation to a purchaser/social network member”).
Regarding claim 5, Sundaresan-Baughman discloses the method of claim 2, wherein the determining of the set of correlations includes:
determining, by the computer system, a correlation between the consumption of the item by a particular one of the one or more users and a type of relationship between the particular user and the first user (Sundaresan [0041-0042] “the reputable user 410 may be a user of the network-based marketplace 112 that has a particularly high user rating within the community. The user 402 may, for example, only grant the reputable user 410 access or visibility only to those items regarding which the user 402 would like to receive the reputable user's 410 recommendation or review. As previously discussed, the rules applications 208 and the roles applications 209 may be used in conjunction with other applications to allow and enforce these privileges and restrictions on members of the community with respect to the item list”).
Regarding claim 6, Sundaresan-Baughman discloses the method of claim 2, further comprising:
filtering, by the computer system, the user data to produce a filtered subset of data (Baughman [0018] “the previously-purchased products may be limited to those purchased within a predetermined period. This may be useful, for example, where it is desirable to account for longer-term changes in a purchaser's buying habits. For example, as a purchaser's habits or tastes change, it may be necessary or desirable to restrict from the comparison items purchased in the more distant past, which may necessarily possess features dissimilar from those of more recently purchased items. That is, by restricting the comparison to purchases made within a predetermined period, the likelihood of spuriously detected anomalies is reduced”), wherein the determining of the set of correlations is based on the filtered subset of data and not an entirety of the user data (Baughman [0025] “At S2B, it may be determined whether the previously-purchased item was purchased within a predetermined period. If so (i.e., Yes at S2B), the previously-purchased item may be included in a correlation value calculated at S2D. If not (i.e., No at S2B), the previously-purchased item may be excluded from the correlation values (S2C), with flow iteratively looped to S2A for comparison to other previously-purchased items, if desired. In other embodiments of the invention, determining whether the previously-purchased item was purchased within a predetermined period may be performed before comparing its feature(s) to feature(s) of the selected item”).
Regarding claim 7, Sundaresan-Baughman discloses the method of claim 2, wherein the causing of the digital content to be presented to the second user is performed automatically and not in response to a request from the second user for a recommendation (Baughman; [0028]; “the messaging applications 214 may be used in conjunction with the social networking applications 210 to provide promotional and/or marketing to the community members associated with the item list to assist them in finding and purchasing items on the item list”).
Regarding claim 8, Sundaresan-Baughman discloses the method of claim 2, wherein the digital content includes another item determined to be similar to the item (Baughman [0027] “at S2F, a dendrogram may be constructed to aid in making a recommendation to a purchaser/social network member at S3 (FIG. 1). Dendrograms are well known in the industry as a method of classifying or grouping items according to shared or similar characteristics”).
Regarding claim 9, Sundaresan-Baughman discloses the method of claim 2, wherein the digital content allows the second user to initiate a transaction to purchase the item from a merchant (Sundaresan [0014] “the network system 100 may be a trading/commerce system where clients may communicate and exchange data with the trading/commerce system, the data may pertain to various functions (e.g., online purchases)”).
Regarding claim 10, Sundaresan-Baughman discloses the method of claim 2, wherein the second user shares a connection with the first user and is not one of the one or more users identified by the consumption information as having consumed the item (Sundaresan [0041-0042] “the reputable user 410 may be a user of the network-based marketplace 112 that has a particularly high user rating within the community. The user 402 may, for example, only grant the reputable user 410 access or visibility only to those items regarding which the user 402 would like to receive the reputable user's 410 recommendation or review. As previously discussed, the rules applications 208 and the roles applications 209 may be used in conjunction with other applications to allow and enforce these privileges and restrictions on members of the community with respect to the item list”).
Regarding claim(s) 11, do(es) not teach or further define over the limitation in claim(s) 1 respectively. Therefore claim(s) 11 is/are rejected for the same rationale of rejection as set forth in claim(s) 1 respectively. Further, with the limitation “in response to determining the set of correlations, identifying, based on the set of correlations, a particular user that has a certain likelihood of consuming the item, wherein the particular user is not identified by the user data as having consumed the item” is taught by Baughman by disclosing that the particular user has not consumed the item (recommendations are for items not already selected or purchased by the user); see [0004] “recommend to the member other selections made by other members of the social network who have also made the same selection as the member”. This implies non-consumed items, as further explained in [0017] “prioritizing the features of an item may include comparing one or more features of the selected item to one or more features of a product or products previously purchased by the purchaser”. This is interpreted as the recommendation focus on new items (not previously purchased by the user), using user data (purchase history) to avoid duplicates
Regarding claim 12, Sundaresan-Baughman discloses the server of claim 11, wherein the determining of the digital content includes: generating a recommendation identifying another item determined to be similar to the item, wherein the recommendation is presented via the user interface of the device associated with the particular user (Baughman [0038] “recommendation program 430 enables computer system 420 to make a recommendation to a purchaser/social network member. To this extent, computer system 420 can acquire and/or utilize information before, during, and after making a recommendation to a purchaser/social network member”).
Regarding claim 13, Sundaresan-Baughman discloses the server of claim 11, wherein the determining of the set of correlations includes: determining a correlation between the consumption of the item and a category of the item, wherein the correlation is a negative correlation between the category of the item and consumption of items of the category (Baughman [0020] “Pearson's correlation values range from +1.0 to -1.0, representing, respectively, a perfect positive correlation and a perfect negative correlation. In the context of feature prioritization, and more particularly anomaly detection, a feature having a negative correlation may be of greater significance than a feature having a positive correlation”).
Regarding claim 14, Sundaresan-Baughman discloses the server of claim 11, wherein the identifying includes determining that the particular user is likely to consume the item based on the set of correlations indicating that the particular user shares one or more relationship attributes with one or more other users of the plurality of users who consumed the item (Sundaresan discloses user sharing relationship attributes (e.g., family, friends, trusted associated) within a community group (plurality of users). Baughman discloses using correlations on purchase data from connected social network members who share attributes like similar interests/beliefs, indicating the shared relationships through the correlation process; see Sundaresan [0040] “The community group 400 this that may be associated with the item list created by the user 402 may include direct relationships (first level) and indirect relationships (second level). The direct relationships in this example include a mother 404, an uncle 406, a bowling team 408 and a reputable user 410, screen name Shopper1 (e.g., a trusted user with whom the first user has previously conducted one or more transactions within the system). The indirect relationships may include other users via the one or more direct relationships. The indirect relationships in this example include a father 412 and an aunt 414 that obtained access to the item list via the mother 404, and a nephew 416 and a niece 418 that obtained access via the uncle 406” and [0019] “the previously-purchased products may include products purchased by members of the purchaser's social network with whom the purchaser is somehow connected. For example, it may be desirable or useful to compare features of a selected item to the features of items purchased by others known to have similar interests, beliefs, etc”).
Regarding claim 15, Sundaresan-Baughman discloses the server of claim 11, wherein the operations further comprise: filtering the user data to produce a filtered subset of user data that includes only user data associated with a particular time frame, wherein the determining of the set of correlations is based on the filtered subset of user data (Baughman [0018] “the previously-purchased products may be limited to those purchased within a predetermined period. This may be useful, for example, where it is desirable to account for longer-term changes in a purchaser's buying habits. For example, as a purchaser's habits or tastes change, it may be necessary or desirable to restrict from the comparison items purchased in the more distant past, which may necessarily possess features dissimilar from those of more recently purchased items. That is, by restricting the comparison to purchases made within a predetermined period, the likelihood of spuriously detected anomalies is reduced”).
Regarding claim 16, Sundaresan-Baughman discloses the server of claim 11, wherein the user data includes connection information that identifies a set of users sharing connections with one or more of the plurality of users, and wherein the particular user is identified from the set of users (Sundaresan [0040] “The community group 400 may include levels of association or relatedness to the user 402 (e.g., the item list generator). The community group 400 this that may be associated with the item list created by the user 402 may include direct relationships (first level) and indirect relationships (second level). The direct relationships in this example include a mother 404, an uncle 406, a bowling team 408 and a reputable user 410, screen name Shopper1 (e.g., a trusted user with whom the first user has previously conducted one or more transactions within the system). The indirect relationships may include other users via the one or more direct relationships. The indirect relationships in this example include a father 412 and an aunt 414 that obtained access to the item list via the mother 404, and a nephew 416 and a niece 418 that obtained access via the uncle 406”).
Regarding claim(s) 17, do(es) not teach or further define over the limitation in claim(s) 1 respectively. Therefore claim(s) 17 is/are rejected for the same rationale of rejection as set forth in claim(s) 1 respectively. Further, the limitation “in response to determining the set of correlations, identifying a first user and a second user, the first user with a certain threshold of consuming the item and a second user based on the set of correlations that has a certain likelihood of consuming the item, wherein the second user is not identified by the consumption information as having consumed the item” is taught by Baughman disclosing a second user (another member of the social network who has selected a similar item) based on the set of correlations (correlation values between shared features), where the second user has a certain likelihood of consuming the item because they have consumed similar items (indicating likelihood via feature correlation matching); see [0023] “At S4, a recommendation is made to a purchaser and/or member of a social network. In some embodiments of the invention, the recommendation may include a product that shares at least one feature of the item selected by the purchaser/social network member. In other embodiments of the invention, the recommendation may include another member of the social network with whom the purchaser/social network member is not yet connected, but who has selected or purchased an item sharing at least one feature of the item selected by the purchaser/social network member. As described above, it may be desirable to recommend to the purchaser/social network member an item (or member of the social network) sharing a highly-ranked feature, such as a feature indicative of an anomalous selection by the purchaser/social network member”.
Regarding claim 18, Sundaresan-Baughman discloses the non-transitory machine-readable medium of claim 17, wherein the identifying of the second user includes determining that the second user is likely to consume the item based on the set of correlations indicating that the second user shares one or more relationship attributes with one or more other users of the plurality of users who consumed the item (Sundaresan [0026] “using rules applications 208 and roles applications 209, a seller may customize an item list and its attributes by exclusively providing entities within the user defined community group rules and roles pertaining to one or more items of the item list. For example, the item list creator may not want a member of a community group to be able to view, purchase, edit, etc. any or all of the items on the item list”).
Regarding claim 19, Sundaresan-Baughman discloses the non-transitory machine-readable medium of claim 17, wherein the operations further comprise automatically causing the digital content to be provided to the second user via the user interface of the device associated with the second user without receiving a request from the second user for the digital content (Baughman; [0028]; “the messaging applications 214 may be used in conjunction with the social networking applications 210 to provide promotional and/or marketing to the community members associated with the item list to assist them in finding and purchasing items on the item list”).
Regarding claim 20, Sundaresan-Baughman discloses the non-transitory machine-readable medium of claim 17, wherein the determining of the digital content includes determining an item recommendation based on a correlation between a platform used to provide the item and consumption of the item (Baughman; [0020] “In making a comparison between one or more feature of a selected item and features of a previously-purchased item or items, some embodiments of the invention include the calculation of a correlation score or value… One such correlation value is a Pearson's correlation value, typically expressed as r. Pearson's correlation values range from +1.0 to -1.0, representing, respectively, a perfect positive correlation and a perfect negative correlation”).
Regarding claim 21, Sundaresan-Baughman discloses the non-transitory machine-readable medium of claim 17, wherein the determining of the digital content includes determining an item recommendation based on a negative correlation between a sharing of the item and consumption of the item (Baughman [0020] “Pearson's correlation values range from +1.0 to -1.0, representing, respectively, a perfect positive correlation and a perfect negative correlation. In the context of feature prioritization, and more particularly anomaly detection, a feature having a negative correlation may be of greater significance than a feature having a positive correlation”).
Conclusion
THIS ACTION IS MADE FINAL. 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 nonprovisional extension fee (37 CFR 1.17(a)) 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 mailing date of this final action.
For the reason above, claims 2-21 have been rejected and remain pending.
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JIMMY H TRAN
Primary Examiner
Art Unit 2451
/JIMMY H TRAN/Primary Examiner, Art Unit 2451
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