DETAILED ACTION
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 .
Notice to Applicant
Claims 1-20 are pending.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 are rejected under 35 U.S.C. § 101 because the claimed invention is directed to a judicial exception (i.e. a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
Step 1 – Statutory Categories of Invention:
Claims 1-8 are drawn to a method, claims 9-16 are drawn to a non-transitory machine readable medium and claims 17-20 are drawn to a device which are one of the statutory categories of invention.
Step 2A – Judicial Exception Analysis, Prong 1:
Independent claims 1, 9, and 17 recite, in part, a method, a non-transitory machine readable medium, and a device comprising the following:
………predict eligibility scores of users to content items based upon popularity of the content items and similarities of the users with other users that have engaged with the content items, wherein ……includes:
inputting positive events and negative events…….., wherein the positive events are input from an external content provider feed and relate to positive user engagement with a content item, and wherein the negative events correspond to a random negative sample;
generating an eligibility score corresponding to a ratio between a first number of users that are similar to a user and have engaged with the content item to a second number of users that are similar to the user and are part of a user population represented by the positive events and the negative events; and
generating and ………….based upon the eligibility score; and
utilizing ………..to select and provide a selected content item ………to the user.
These steps amount to methods of organizing human activity which includes functions relating to interpersonal and intrapersonal activities, such as managing relationships or transactions between people, social activities, and human behavior; satisfying or avoiding a legal obligation; advertising, marketing, and sales activities or behaviors; and managing human mental activity (MPEP § 2106.04(a)(2)(II)(C) citing the abstract idea grouping for methods of organizing human activity for managing personal behavior or relationships or interactions between people).
Dependent claims 2 and 20 recite, in part, defining an eligibility score threshold used to determine whether the user is eligible or ineligible to be provided with the content item.
Dependent claim 3 recites, in part, setting the eligibility score threshold based upon a percentage of a target user population that will be considered eligible.
Dependent claim 4 recites, in part, modifying the eligibility score threshold to optimize performance and provide the content item to users that are more likely to engage with the content item.
Dependent claim 5 recites, in part, :in response to training the model, producing scores for a test sample of users; for each user within the test sample of users, scoring content items of the content provider with a plurality of eligibility scores; storing a distribution of highest eligibility scores, of the plurality of eligibility scores, for each user; utilizing a threshold and the distribution to determine a percentage of users that will be considered eligible based upon the users having eligibility scores above the threshold; and defining the eligibility score threshold based upon at least one of the threshold or the percentage of users that will be considered eligible.
Dependent claim 6 recites, in part, receiving a specified threshold from the content provider, wherein the specified threshold corresponds to the percentage of users; and selecting the threshold so that the percentage of users will have eligibility scores higher than the threshold for the content item of the content provider.
Dependent claim 7 recites, in part, adjusting the eligibility score threshold to increase a pool of eligible users to include additional eligible users with lower eligibility scores than eligible users that were in the pool before adjustment of the eligibility score threshold.
Dependent claim 8 recites, in part, adjusting the eligibility score threshold to decrease a pool of eligible users to exclude eligible users with lower eligibility scores than remaining eligible users within the pool after adjustment of the eligibility score threshold.
Dependent claim 10 recites, in part, wherein the operations comprise:bounding the model by a set number of content items; and allocating content items, of content item groups, in the model based upon prior spending associated with recommending the content items.
Dependent claim 11 recites, in part, wherein a first content item group with a larger budget has more content items allocated within the model than a second content item group with a smaller budget.
Dependent claim 12 recites, in part, wherein a minimum limit is set for each content item group to avoid starvation.
Dependent claim 13 recites, in part, identifying users that are similar to the user based upon the users and the user having similar user features.
Dependent claims 14 and 18 recite, in part, identifying users that are similar to the user based upon the users and the user having the same gender and age.
Dependent claim 15 recites, in part, wherein the second number of users, that are similar to the user and are part of the user population, correspond to the first number of users that are similar to the user and have engaged with the content item in addition to a number of users similar to the user in the random negative sample from a native impression feed of a content serving platform.
Dependent claim 16 recites, in part, wherein the positive events include purchase events and add to cart events.
Dependent claim 19 recites, in part, wherein the content item is represented by content item features that include a content item ID, a content item set ID, and a content provider ID.
Each of these steps of the preceding dependent claims 2-8, 10-16, and 18-20 only serve to further limit or specify the features of independent claims 1, 9, and 17 accordingly, and hence are nonetheless directed towards fundamentally the same abstract idea as the independent claim and utilize the additional elements already analyzed in the expected manner.
Step 2A – Judicial Exception Analysis, Prong 2:
This judicial exception is not integrated into a practical application because the additional elements within the claims only amount to instructions to implement the judicial exception using a computer [MPEP 2106.05(f)].
Independent Claims 1, 9, and 17 recites, in part, training a model, a computing device, a display, and/or a non-transitory computer readable medium, and/or processor-executable instructions, and/or a processor, and/or a memory, and/or a content serving platform. The specification defines training a model as positive and negative events are input into the training algorithm, (Example Embodiments in ¶ 0003), a computing device as such as a portable media player (e.g., an electronic text reader, an audio device, or a portable gaming, exercise, or navigation device); a portable communication device (e.g., a camera, a phone, a wearable or a text chatting device); a workstation; and/or a laptop form factor computer, (Example Embodiments in ¶ 0023), a display as a display adapter, such as a graphical processing unit (GPU), (Example Embodiments in ¶ 0027), a non-transitory computer readable medium as a memory semiconductor (e.g., a semiconductor utilizing static random access memory (SRAM), dynamic random access memory (DRAM), and/or synchronous dynamic random access memory (SDRAM) technologies), a platter of a hard disk drive, a flash memory device, or a magnetic or optical disc (such as a compact disk (CD), a digital versatile disk (DVD), or floppy disk), (Example Embodiments in ¶ 0057), processor-executable instructions as when executed by a processor 716 cause performance (e.g., by the processor 716), (Example Embodiments in ¶ 0057), a processor as may optionally include a plurality of cores; one or more coprocessors, such as a mathematics coprocessor or an integrated graphical processing unit (GPU); and/or one or more layers of local cache memory, (Example Embodiments in ¶ 0026), a memory as one or more storage components 216, such as a hard disk drive, a solid-state storage device (SSD), a flash memory device, and/or a magnetic and/or optical disk reader, (Example Embodiments in ¶ 0026), a content serving platform as an architecture through which content items of content providers can be provided to computing devices of users, (Example Embodiments in ¶ 0001). The training a model, a computing device, a display, and/or a non-transitory computer readable medium, and/or processor-executable instructions, and/or a processor, and/or a memory, and/or a content serving platform.in these steps are recited at a high-level generality, such that it amounts to no more than mere instructions to apply the exception using a generic computer component. The use of training a model, a computing device, a display, and/or a non-transitory computer readable medium, and/or processor-executable instructions, and/or a processor, and/or a memory, and/or a content serving platform are only recited as a tool to perform an existing process and only amounts to an instruction to implement the abstract idea using a computer (MPEP § 2106.05(f)(2) see case requiring the use of software to tailor information and provide it to the user on a generic computer within the “Other examples.. v.”).
Dependent claim 5 recites, in part, training the model. The limitations are only recited as a tool to perform an existing process and only amounts to an instruction to implement the abstract idea using a computer (MPEP § 2106.05(f)(2) see case requiring the use of software to tailor information and provide it to the user on a generic computer within the “Other examples.. v.”).
Dependent claims 10 and 11, recite in part, the model. The limitations are only recited as a tool to perform an existing process and only amounts to an instruction to implement the abstract idea using a computer (MPEP § 2106.05(f)(2) see case requiring the use of software to tailor information and provide it to the user on a generic computer within the “Other examples.. v.”).
Dependent claim 15, recites in part, content serving platform. The limitations are only recited as a tool to perform an existing process and only amounts to an instruction to implement the abstract idea using a computer (MPEP § 2106.05(f)(2) see case requiring the use of software to tailor information and provide it to the user on a generic computer within the “Other examples.. v.”).
The above claims, as a whole, are therefore directed to an abstract idea.
Step 2B – Additional Elements that Amount to Significantly More:
The present claims do not include additional elements that are sufficient to amount to more than the abstract idea because the additional elements or combination of elements amount to no more than a recitation of instructions to implement the abstract idea on a computer.
Independent Claims 1, 9, and 17 recites, in part, training a model, a computing device, a display, and/or a non-transitory computer readable medium, and/or processor-executable instructions, and/or a processor, and/or a memory, and/or a content serving platform. Each of these elements is only recited as a tool for performing steps of the abstract idea, such as use of training a model, a computing device, a display, and/or a non-transitory computer readable medium, and/or processor-executable instructions, and/or a processor, and/or a memory, and/or a content serving platform to input, store, analyze, and process data. These additional elements therefore only amount to mere instructions to perform the abstract idea using a computer and are not sufficient to amount to significantly more than the abstract idea (MPEP 2016.05(f) see for additional guidance on the “mere instructions to apply an exception”).
Each additional element under Step 2A, Prong 2 is analyzed in light of the specification’s explanation of the additional element’s structure. The claimed invention’s additional elements do not have sufficient structure in the specification to be considered a not well-understood, routine, and conventional use of generic computer components. Note that the specification can support the conventionality of generic computer components if “the additional elements are sufficiently well-known that the specification does not need to describe the particulars of such additional elements to satisfy 35 U.S.C. § 112(a)” (Berkheimer in III. Impact on Examination Procedure, A. Formulating Rejections, 1. on p. 3).
Dependent claim 5 recites, in part, training the model. The courts have decided that storing and retrieving information in memory as well-understood, routine, conventional activity as a computer function when claimed in a merely generic manner (e.g., at a high level of generality) or as insignificant extra-solution activity (MPEP § 2106.05(d)(II)).
Dependent claims 10 and 11, recite in part, the model. The courts have decided that storing and retrieving information in memory as well-understood, routine, conventional activity as a computer function when claimed in a merely generic manner (e.g., at a high level of generality) or as insignificant extra-solution activity (MPEP § 2106.05(d)(II)).
Dependent claim 15, recites in part, content serving platform. The courts have decided that receiving or transmitting data over a network as well-understood, routine, conventional activity when claimed in a merely generic manner (e.g., at a high level of generality) or as insignificant extra-solution activity (MPEP § 2106.05(d)(II) other types of activities example i. receiving or transmitting data over a network, OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network).
Thus, taken alone, the additional elements do not amount to significantly more than the above-identified judicial exception. Looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements taken individually. Their collective functions merely provide conventional computer implementation.
Claims 1-20 are therefore rejected under 35 U.S.C. § 101 as being directed to non-statutory subject matter.
Claim Rejections - 35 USC § 102
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claims 1-20 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by United States Patent Application Publication 2013/0290110, LuVogt, et al., hereinafter LuVogt.
Regarding claim 1, LuVogt discloses a method executing on a processor of a computing device that causes the computing device to perform operations comprising:
training a model to predict eligibility scores of users to content items based upon popularity of the content items and similarities of the users with other users that have engaged with the content items, (para. 91, the user model 3324 is built using the Vector Space Model which is a list of terms (words and phrases) or other features, with associated weights and modified dot-product for measuring similarity, and para. 136, A direct comparison of two user models using a similarity measure describe herein can result in a recommendation that users connect to each other, either on one of the client services (TWITTER, FACEBOOK etc.) or within the recommendations module), wherein the training includes:
inputting positive events and negative events into the model, wherein the positive events are input from an external content provider feed and relate to positive user engagement with a content item, and wherein the negative events correspond to a random negative sample, (para. 77, the recommendations module 210 takes into consideration the user's past actions/interactions with the items when determining their rankings so that, for example, items that have been read or otherwise provided positive feedback for example, will be ranked higher, and para. 105, the number of positive and negative feedback signals the item has received from the overall user population and para. 120, as the user provides positive or negative feedback for an item of a given content type, its percentage is adjusted upwards or downwards by a small amount, so that for example, interacting positively with a given type will increase its prevalence in the items recommended);
generating an eligibility score corresponding to a ratio between a first number of users that are similar to a user and have engaged with the content item to a second number of users that are similar to the user and are part of a user population represented by the positive events and the negative events, (para. 3, a user selection of a content item from the selected top scoring content items is received by the processor that recalculates the relevance of the user selected content item to user models of other users on receiving the user selection, and para. 76, in real-time, the content items forwarded to other users related to the user such as those that are connected to the user in an online social network or those that the recommendation module 210 has determined have similar profiles as the user making the selections and para. 88, These can include without limitation, collections of seen items, unseen items, saved items, deleted items, items recommended to other users, liked or disliked items); and
generating and training the model based upon the eligibility score, (para. 135, The content items added to the unseen items lists in the respective user PE are restored to determine an updated relevance score in view of the updated user model as shown at 1256. At 1258, the newly determined relevance score is compared to the relevance threshold for the user); and
utilizing the model to select and provide a selected content item to a computing device for display to the user, (para. 122, either only the relevant new content items `X` as requested by the user are displayed to the user on a user device or a combination of the `X` relevant new content items and top `N-X` content items previously viewed by the user can be displayed to the user.).
Regarding claim 2, LuVogt discloses the method of claim 1 as described above. LuVogt further discloses comprising: defining an eligibility score threshold used to determine whether the user is eligible or ineligible to be provided with the content item, (para. 10, the user model parameters such as relevance threshold are adjusted based on user behavior and/or system response and para. 117, if a plurality of content items are received by the recommendations module 210, the feature PEs substantially filter out irrelevant content and forward only content that has at least some degree of relevance to the user.).
Regarding claim 3, LuVogt discloses the method of claims 1 and 2 as described above. LuVogt further discloses comprising: setting the eligibility score threshold based upon a percentage of a target user population that will be considered eligible, (para. 120, the item mixture weights for different content types are determined. As described herein, the recommendations module 210 takes a wide variety of content types as input and determines not only how to score them but also how to ensure that there is a reasonable amount of diversity in the recommendations or content lists forwarded to the user).
Regarding claim 4, LuVogt discloses the method of claims 1 and 2 as described above. LuVogt further discloses comprising: modifying the eligibility score threshold to optimize performance and provide the content item to users that are more likely to engage with the content item, (para. 91, when the user is issuing a keyword search (either within or outside of the recommendations module 210), terms from the respective user model can be used as search suggestions or as input to the ranking functions so that content items including terms from the user model 3324 are ranked higher in the result set and claim 11, modifying a respective item type mixture weight of an item type selected by the user upon receiving the user selection.).
Regarding claim 5, LuVogt discloses the method of claims 1 and 2 as described above. LuVogt further discloses wherein the defining the eligibility score threshold comprises:
in response to training the model, producing scores for a test sample of users, (para. 109, explicit training of the user model by the user 3324 can be optional and that the user model 3324 can implement machine learning techniques to automatically fine-tune the user model 3324 based on implicit feedback obtained from different sources including the user actions and/or actions of other users who may share similar interests and hence who have similar user models);
for each user within the test sample of users, scoring content items of the content provider with a plurality of eligibility scores, (para. 109, explicit training of the user model by the user 3324 can be optional and that the user model 3324 can implement machine learning techniques to automatically fine-tune the user model 3324 based on implicit feedback obtained from different sources including the user actions and/or actions of other users who may share similar interests and hence who have similar user models, and para. 136, . A direct comparison of two user models using a similarity measure describe herein can result in a recommendation that users connect to each other, either on one of the client services (TWITTER, FACEBOOK etc.) or within the recommendations module);
storing a distribution of highest eligibility scores, of the plurality of eligibility scores, for each user, (para. 73, the feed manager 212 is operable to store the received content streams or feeds 110 in a recommendation database 214 and/or to further process the content streams 110 for efficient distribution. For example, the feed manager 212 can extract data and/or metadata from the content included in the various feeds 110 and normalize the metadata into standardized format and inject events into the recommendations module);
utilizing a threshold and the distribution to determine a percentage of users that will be considered eligible based upon the users having eligibility scores above the threshold, (para. 80, the recommendations module 210 qualifies or disqualifies the article/content item as being relevant enough and if it passes this gate, a reference to the article is placed in the user's unseen items list, which in turn is sent to the user and para. 105, the content type of the item, when the item was published, as well as the number of positive and negative feedback signals the item has received from the overall user population. The tank is used to ensure the score stays in the [0 . . . 1] range, as well as to shape the score and spread out the distribution); and
defining the eligibility score threshold based upon at least one of the threshold or the percentage of users that will be considered eligible, (para. 120, each major content type (e.g., News, FACEBOOK, TWITTER, Email) has a target percentage associated with it, which varies over time as the user interacts with the recommendations module…… the user PE 322 walks through each content type (starting with the one with the largest target), and tries to recommend enough items from that type that pass the relevance threshold so that the ratio is preserved).
Regarding claim 6, LuVogt discloses the method of claims 1, 2, and 5 as described above. LuVogt further discloses comprising:
receiving a specified threshold from the content provider, wherein the specified threshold corresponds to the percentage of users, (para. 120, each major content type (e.g., News, FACEBOOK, TWITTER, Email) has a target percentage associated with it, which varies over time as the user interacts with the recommendations module…… the user PE 322 walks through each content type (starting with the one with the largest target), and tries to recommend enough items from that type that pass the relevance threshold so that the ratio is preserved); and
selecting the threshold so that the percentage of users will have eligibility scores higher than the threshold for the content item of the content provider, (para. 10, the user model parameters such as relevance threshold are adjusted based on user behavior and/or system response and para. 117, if a plurality of content items are received by the recommendations module 210, the feature PEs substantially filter out irrelevant content and forward only content that has at least some degree of relevance to the user).
Regarding claim 7, LuVogt discloses the method of claims 1 and 2 as described above. LuVogt further discloses comprising:
adjusting the eligibility score threshold to increase a pool of eligible users to include additional eligible users with lower eligibility scores than eligible users that were in the pool before adjustment of the eligibility score threshold, (para. 10, the user model parameters such as relevance threshold are adjusted based on user behavior and/or system response, para. 13, the processor executes storing logic, for storing frequency of user requests for new content so that the processor can lower a relevance threshold associated with determination of relevance for providing the recommendations such that more content items can be recommended if the user request new content frequently, and para. 117, if a plurality of content items are received by the recommendations module 210, the feature PEs substantially filter out irrelevant content and forward only content that has at least some degree of relevance to the user).
Regarding claim 8, LuVogt discloses the method of claims 1 and 2 as described above. LuVogt further discloses comprising: adjusting the eligibility score threshold to decrease a pool of eligible users to exclude eligible users with lower eligibility scores than remaining eligible users within the pool after adjustment of the eligibility score threshold, (para. 138, the type of content item selected by the user is determined and the item type mixture weight associated with that content type is updated as shown at 1406. In one embodiment, the item type mixture weight of the particular type of content selected by the user is increased and correspondingly, the item type mixture weights of other content types can be decreased so that the ratio the content type selected by the user in the overall content transmitted to the user is increased).
Regarding claim 9, this claim is rejected for the same reasons as set forth above with regard to claim 1. LuVogt further discloses a non-transitory machine readable medium having stored thereon processor-executable instructions that when executed cause performance of operations, (paragraphs 149-151).
Regarding claim 10, LuVogt discloses the medium of claim 9 as described above. LuVogt further discloses wherein the operations comprise:
bounding the model by a set number of content items, (para. 101, The weights of the user vector (which is a "Max One" vector) can be associated with a specific lower-bound, a parameter called MIN_WEIGHT. Only real-valued weights between MIN_WEIGHT and 1 are allowed for the "Max One" vector, with any operations that cause the weight to go above 1 will instead reset it to 1. For example, the vector values can be limited to the range [0 . . . 1]. Any operations that cause the weight to go below MIN_WEIGHT instead cause the term to be removed from the list. A fixed most important terms to represent a user's interests and which, if some operation causes to grow longer, will automatically remove the lowest weighted terms so as to stay within the maximum length.); and
allocating content items, of content item groups, in the model based upon prior spending associated with recommending the content items, (para. 101, assigning a minimum weight to a term and removing terms that go below the MIN_WEIGHT, automatically keeps finite, the list of terms associated with the user so that only highly rated terms are maintained in the user model 3324 and a user's fleeting or less enduring interest in a particular category/term is forgotten and not maintained within the user model 3324. This mitigates the possibility that content items associated with such passing interests are discarded as less relevant and are not forwarded to the user.).
Regarding claim 11, LuVogt discloses the medium of claims 9 and 10 as described above. LuVogt further discloses wherein a first content item group with a larger budget has more content items allocated within the model than a second content item group with a smaller budget, (para. 79, "Ad serving" refers to methods used to place online advertisements on websites, in applications, or other places where users are more likely to see them, such as during an online session or during computing platform use, for example. Advertising may be beneficial to users, advertisers or web portals if displayed advertisements are relevant to interests of one or more users. In an embodiment, advertisements can be presented to users in a targeted audience based at least in part upon predicted user behavior(s) or user profile information, para. 112, different types of advertisements which are relevant to information in each of the content items in the aggregated content stream, and para. 121, as described herein various content items including public and private data such as emails, new items, alerts, advertisements, social networking feeds and input from other selected sources can be included in the content sent to the user).
Regarding claim 12, LuVogt discloses the medium of claims 9 and 10 as described above. LuVogt further discloses wherein a minimum limit is set for each content item group to avoid starvation, (para. 120, the user PE 322 walks through each content type (starting with the one with the largest target), and tries to recommend enough items from that type that pass the relevance threshold so that the ratio is preserved. If it cannot recommend the requisite amount of a particular content type, it then readjusts the target percentages of the remaining content types so as to maintain their relative ratios. Furthermore, as the user provides positive or negative feedback for an item of a given content type, its percentage is adjusted upwards or downwards by a small amount, so that for example, interacting positively with a given type will increase its prevalence in the items recommended. There are both minimum and maximum limits on the percentages, so no one type comes to dominate the stream of recommended items).
Regarding claim 13, LuVogt discloses the medium of claim 9 as described above. LuVogt further discloses wherein the operations comprise:
identifying users that are similar to the user based upon the users and the user having similar user features, (para. 88, The data 3320 can comprise user attributes such as age, location, demographic information of the user in addition to different item collections 410 that are unique to the user who is represented by the user PE 332, para. 113, since the user is also represented by a vector, typical clustering techniques can be used to create groups of related users and aggregate their vectors to create the vector for a new user, again by looking at other attributes they share in common like age, sex, location, News category selections, Avatar selection, or other selections the user makes when configuring the recommendations module 210 for personal use, and para. 137, The method begins at 1310 wherein two user models are compared. This comparison can occur, for example, when a user selects certain category vectors to be included into the user model. Based on such selection and/or other user attributes such as, location, age, sex, profession for example, other users who have similar category vectors in their user models can be selected for comparison….. If the two user models are not similar the procedure terminates on the end block. If it is determined at 1320 that there is sufficient similarity between the two user models, it is determined at 1330 if the two users are connected within the recommendations module 210. If the two users are not connected, then a message can be sent at 1340 suggesting that the users connect within the recommendations module 210 or any other social network of the users' choice and the procedure terminates on the end block. If it is determined at 1330 that the users are connected, the content item recommendations made by the users to their social contacts or the content items indicated as being of interest to the users are exchanged at 1350. Therefore, users with similar user models can be sources of content recommendations within the recommendations module 210.).
Regarding claim 14, LuVogt discloses the medium of claim 9 as described above. LuVogt further discloses wherein the operations comprise:
identifying users that are similar to the user based upon the users and the user having the same gender and age, (para. 137, The method begins at 1310 wherein two user models are compared. This comparison can occur, for example, when a user selects certain category vectors to be included into the user model. Based on such selection and/or other user attributes such as, location, age, sex, profession for example, other users who have similar category vectors in their user models can be selected for comparison….. If the two user models are not similar the procedure terminates on the end block. If it is determined at 1320 that there is sufficient similarity between the two user models, it is determined at 1330 if the two users are connected within the recommendations module 210. If the two users are not connected, then a message can be sent at 1340 suggesting that the users connect within the recommendations module 210 or any other social network of the users' choice and the procedure terminates on the end block. If it is determined at 1330 that the users are connected, the content item recommendations made by the users to their social contacts or the content items indicated as being of interest to the users are exchanged at 1350. Therefore, users with similar user models can be sources of content recommendations within the recommendations module 210.).
Regarding claim 15, LuVogt discloses the medium of claim 9 as described above. LuVogt further discloses wherein the second number of users, that are similar to the user and are part of the user population, correspond to the first number of users that are similar to the user and have engaged with the content item in addition to a number of users similar to the user in the random negative sample from a native impression feed of a content serving platform., (para. 137, The method begins at 1310 wherein two user models are compared. This comparison can occur, for example, when a user selects certain category vectors to be included into the user model. Based on such selection and/or other user attributes such as, location, age, sex, profession for example, other users who have similar category vectors in their user models can be selected for comparison….. If the two user models are not similar the procedure terminates on the end block. If it is determined at 1320 that there is sufficient similarity between the two user models, it is determined at 1330 if the two users are connected within the recommendations module 210. If the two users are not connected, then a message can be sent at 1340 suggesting that the users connect within the recommendations module 210 or any other social network of the users' choice and the procedure terminates on the end block. If it is determined at 1330 that the users are connected, the content item recommendations made by the users to their social contacts or the content items indicated as being of interest to the users are exchanged at 1350. Therefore, users with similar user models can be sources of content recommendations within the recommendations module 210).
Regarding claim 16, LuVogt discloses the medium of claim 9 as described above. LuVogt further discloses wherein the positive events include purchase events and add to cart events, (para. 82, configuring the user processing element 332 to listen for actions taken by the user it represents, e.g., clicking on a given item or forwarding items to contacts, voting ("like" or "dig") for the items or other user action that is recorded and can be employed to update the user model 3324, and para. 138, a user click on a particular content item. At 1404, the type of content item selected by the user is determined and the item type mixture weight associated with that content type is updated as shown at 1406. In one embodiment, the item type mixture weight of the particular type of content selected by the user is increased and correspondingly, the item type mixture weights of other content types can be decreased so that the ratio the content type selected by the user in the overall content transmitted to the user is increased).
Regarding claim 17, this claim is rejected for the same reasons as set forth above with regard to claim 1. LuVogt further discloses a processor; and memory comprising processor-executable instructions that when executed by the processor cause performance of operations, (paragraphs 149-150 and 155-156).
Regarding claim 18, this claim is rejected for the same reasons as set forth above with regard to claim 14.
Regarding claim 19, LuVogt discloses the device of claim 17 as described above. LuVogt further discloses wherein the content item is represented by content item features that include a content item ID, a content item set ID, and a content provider ID, (para. 86, every item or article is preferably represented by a vector: D.sub.g, c, d where the item has a guid (global unique identifier) g, is possibly labeled with a category c, and was published/received on day).
Regarding claim 20, this claim is rejected for the same reasons as set forth above with regard to claim 2.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
GENERATING DYNAMIC CONTENT ITEM RECOMMENDATIONS (US 20220005070 A1) teaches generating dynamic content item recommendations are provided. Content item information, extracted from message data, is aggregated to calculate popularity and attributes of content items
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/AMBER A MISIASZEK/Primary Examiner, Art Unit 3682