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 .
This communication is responsive to the amendment filed on 12/09/2025
Status of claims
Claim 26 and newly added.
Claims 1, 4, 6, 9, 11, 14, 16, 18-19, 21-22 and 24
Claims 1-26 are presented for examination.
Response to Arguments
Applicant’s arguments with respect to the amended claims have been considered in view of the new ground(s) of rejection necessitated by amendment..
Claim Objections
Claim 5 is objected to because of the following informalities:
- Claim 5 recites the terms “The method of claim 1”, while claim 1 is “A computer-implemented method”. Correction is required.
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 1-26 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Claims 1, 6 and 11:Step 1: Statutory Category
The claims are directed to one of the four statutory categories of invention, i.e., process, machine, manufacture, or composition of matter.
Step 2A, Prong One:
The claim recites the limitations “determining…;inputting…; generating…” are processes that, under its broadest reasonable interpretation, covers a mental process as a form of evaluation or judgement, but for the recitation of generic computer components. That is nothing in the claim element precludes the steps from practically being performed in a human mind.
If a claim limitation, under its broadest reasonable interpretation, covers
performance of the limitation in the mind but for the recitation of generic computer
components, then it falls within the "Mental Processes" grouping of abstract ideas.
Accordingly, the claims recite an abstract idea.
Step 2A, Prong Two: Integrated into a Practical Application
This judicial exception is not integrated into a practical application. The claim recites the additional elements “
“obtaining …; obtaining …; providing…”, represent(s) an extra solution activity because it is a mere nominal or tangential addition to the claim, a mere generic transmission and presenting of collected and analyzed data. (See MPEP 2106.05(g)).
“memory; hardware processor, memory, a user interface; non-transitory, computer-readable medium” are recited at a high level of generality such that they amount to on more than mere instructions to apply the exception using a generic component. (see MPEP 2106.05(f)).
“trained model; machine learning model” is a mere implementation using a computer. It is at best generally linking the abstract idea to a particular field of use or technological environment of machine learning (see MPEP 2106.05(h).
Step 2B: Claim provides an Inventive Concept
“obtaining …; obtaining …; providing…”. These are identified as insignificant extra-solution activity above when re-evaluated this element is well-understood, routine, and conventional as evidenced by the court cases in MPEP 2106.05(d)(II), "i. Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information); … OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network); buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network);" and thus remains insignificant extra-solution activity that does not provide significantly more.
“memory; hardware processor, memory, a user interface; non-transitory, computer-readable medium”, amount to elements that have been recognized as well-understood, routine, and conventional activity in particular fields, as demonstrate by: relevant court decision: the followings are example of the court decisions demonstrating well-understood, routine and conventional activities, See e.g., MPEP 2106.05(d)(II) and MPEP 2106.05(f)(2): computer readable storage media comprising instructions to implement a method, e.g., see versata Dev. Group, Inc. v SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015).
The conclusions for the mere implementation using a computer, mere field of use, and using generic computer components (i.e. ML) as a tool are carried over and do not provide significantly more.
The claims as a whole, does not amount to significantly more than the abstract
idea itself. This is because the claims do not affect an improvement to the functioning
of a computer itself; and the claims do not move beyond a general link of the use of an
abstract idea to a particular technological environment.
Accordingly, claims are directed to an abstract idea.
Claims 2-3, recite the additional limitations, which represent(s) an extra solution activity because it is a mere nominal or tangential addition to the claim, a mere generic transmission and presenting of collected and analyzed data. (See MPEP 2106.05(g)).
Claim 4, recite the additional limitations “the set of users of the first platform…”, which represent(s) an extra solution activity because it is a mere nominal or tangential addition to the claim, a mere generic transmission and presenting of collected and analyzed data. (See MPEP 2106.05(g)).
“selecting, based on the scores, the set…” is process that, under its broadest reasonable interpretation, covers a mental process as a form of evaluation or judgement, but for the recitation of generic computer components. That is nothing in the claim element precludes the steps from practically being performed in a human mind. Therefore, the claims falls within the "Mental Processes" grouping of abstract ideas.
Claim 5, recite the limitations are processes that, under its broadest reasonable interpretation, covers a mental process as a form of evaluation or judgement, but for the recitation of generic computer components. That is nothing in the claim element precludes the steps from practically being performed in a human mind. Therefore, the claim falls within the "Mental Processes" grouping of abstract ideas.
Claims 7-10, are rejected under the same analysis as applied to claims 2-5 above.
Claims 11-15, are rejected under the same analysis as applied to claims 2-5 above.
Claims 16, 19 and 22:Step 1: Statutory Category
The claims are directed to one of the four statutory categories of invention, i.e., process, machine, manufacture, or composition of matter.
Step 2A, Prong One:
The claim recites the limitations “determining…;identifying…; performing comparison…;identifying…; providing…” are processes that, under its broadest reasonable interpretation, covers a mental process as a form of evaluation or judgement, but for the recitation of generic computer components. That is nothing in the claim element precludes the steps from practically being performed in a human mind.
If a claim limitation, under its broadest reasonable interpretation, covers
performance of the limitation in the mind but for the recitation of generic computer
components, then it falls within the "Mental Processes" grouping of abstract ideas.
Accordingly, the claims recite an abstract idea.
Step 2A, Prong Two: Integrated into a Practical Application
This judicial exception is not integrated into a practical application. The claim recites the additional elements “
“providing…”, represent(s) an extra solution activity because it is a mere nominal or tangential addition to the claim, a mere generic transmission and presenting of collected and analyzed data. (See MPEP 2106.05(g)).
“memory; hardware processor, memory, a user interface; non-transitory, computer-readable medium” are recited at a high level of generality such that they amount to on more than mere instructions to apply the exception using a generic component. (see MPEP 2106.05(f)).
“trained model” is a mere implementation using a computer. It is at best generally linking the abstract idea to a particular field of use or technological environment of machine learning (see MPEP 2106.05(h).
Step 2B: Claim provides an Inventive Concept
“providing…”. These are identified as insignificant extra-solution activity above when re-evaluated this element is well-understood, routine, and conventional as evidenced by the court cases in MPEP 2106.05(d)(II), "i. Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information); … OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network); buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network);" and thus remains insignificant extra-solution activity that does not provide significantly more.
“memory; hardware processor, memory, a user interface; non-transitory, computer-readable medium”, amount to elements that have been recognized as well-understood, routine, and conventional activity in particular fields, as demonstrate by: relevant court decision: the followings are example of the court decisions demonstrating well-understood, routine and conventional activities, See e.g., MPEP 2106.05(d)(II) and MPEP 2106.05(f)(2): computer readable storage media comprising instructions to implement a method, e.g., see versata Dev. Group, Inc. v SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015).
The conclusions for the mere implementation using a computer, mere field of use, and using generic computer components (i.e. ML) as a tool are carried over and do not provide significantly more.
The claims as a whole, does not amount to significantly more than the abstract
idea itself. This is because the claims do not affect an improvement to the functioning
of a computer itself; and the claims do not move beyond a general link of the use of an
abstract idea to a particular technological environment.
Accordingly, claims are directed to an abstract idea.
Claim 17, recite the limitations are process that, under its broadest reasonable interpretation, covers a mental process as a form of evaluation or judgement, but for the recitation of generic computer components. That is nothing in the claim element precludes the steps from practically being performed in a human mind. Therefore, the claims falls within the "Mental Processes" grouping of abstract ideas.
Claim 18, recite the limitation “obtaining…; imputing…; generating…” are processes that, under its broadest reasonable interpretation, covers a mental process as a form of evaluation or judgement, but for the recitation of generic computer components. That is nothing in the claim element precludes the steps from practically being performed in a human mind. Therefore, the claims falls within the "Mental Processes" grouping of abstract ideas.
the additional limitations “obtaining…; wherein providing…”, which represent(s) an extra solution activity because it is a mere nominal or tangential addition to the claim, a mere generic transmission and presenting of collected and analyzed data. (See MPEP 2106.05(g)).
Claims 20-21, are rejected under the same analysis as applied to claims 17-18 above.
Claims 23-24, are rejected under the same analysis as applied to claims 17-18 above.
Claim 25, recite the limitation is a process that, under its broadest reasonable interpretation, covers a mental process as a form of evaluation or judgement, but for the recitation of generic computer components. That is nothing in the claim element precludes the steps from practically being performed in a human mind. Therefore, the claims falls within the "Mental Processes" grouping of abstract ideas.
Claim 26, recite the limitation, which is a mere implementation using a computer. It is at best generally linking the abstract idea to a particular field of use or technological environment of machine learning (see MPEP 2106.05(h).
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, 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.
Claims 1-24 and 26 are rejected under 35 U.S.C. 103 as being unpatentable over Shtof et al., (US 2021/0264297), hereinafter “Shtof”, in view of Swaminathan et al., (US 2017/0161618 A1), hereinafter “Swaminathan”.
Claim 1, Shtof discloses a computer-implemented method, comprising:
- determining, based on user activity data, that a first user of a first platform has not interacted with content of a content platform during within a predetermined time period (par. [0002]-[0003], wherein a model to predict likelihoods that a particular user will interact with content items and outputs predictions of how likely the user is to interact with content items based upon content attributes of the content , wherein model specifies whether users interacted with content items (e.g., did a user watch a video provided as a suggestion by a video streaming service or not, did a user click on a recommendation to purchase an item or not; and par. [0062], the model can understand a continuous flow of time where a time period can correspond to similar user behavior, such as where users behave similarly from 1:00 am to 4:00 am, which can be understand by the model because gray codes for those time attribute values of 1:00 am to 4:00 am may correspond to similar options/sub-categories of a time attribute);
- obtaining, from a user account repository stored on the first platform, first attribute data associated with the first user, the first user attribute data including account information of the first user comprising at least one of account activity, account login history, or age of account (par. [0003], specifying user attributes of the users that either did or did not interact with the content items after being provided with an opportunity to interact with the content items, wherein the user attributes correspond to an age of a user, a location of a user, a time at which a user performed an action such as logging into the video streaming service, a current location of the user, a home location of the user, demographic information about the user, interests of the user, and/or a wide variety of information about the user, activities of the user, preferences of the user);
- inputting the first user attribute data into a trained model, wherein the trained model is trained using user attribute data associated with a plurality of users of the first platform and corresponding content preference data for the plurality of users (par. [0003], [0006], and [0039], content recommendation system may utilize machine learning functionality train and generate a model based upon user interaction data and predict how likely certain users are to interact with certain content items) to output a score representing degree of similarity in preferences of the first user and the plurality of users (par. [0010]-[0012], similarity score is determined based on a similarity between user preferences for content items and generate a ranked list of content items having the similarity content preferences of the user; par. [0044], the model can more accurately predict affinity scores corresponding to how likely users will interact with content items; and par. [0064] The content recommendation system utilizes the user attributes of the user and content attributes of available content items to provide to the user as input to the model. The model generations predictions, such as scores, so to how likely the user is to interact with each content item);
However, Shtof does not disclose the “obtaining, using an output generated by the trained model in response to the input first attribute data, an indication of a set of users of the first platform who are expected to have most similar content preferences to the first user”.
On the other hand, Swaminathan discloses obtaining, using an output generated by the trained model in response to the input first user attribute data (par. [0010]-[0012], similarity score is determined based on a similarity between user preferences for content items and generate a ranked list of content items having the similarity content preferences of the user), data identifying a set of users of the first platform that are expected to have most similar content preferences to the first user (par. [0009], attributes associated with the preferred content items are then assigned weights generally proportional to the number of users who indicated their preference for the corresponding content items. For example, Movie A and Movie B are both highly popular, and a large number of users prefer both movies. Meanwhile, Movie C and Movie D are not very popular, and preferred by a small number of users, but most users who preferred Movie C also preferred Movie D; par. [0010]-[0012], similarity score is determined based on a similarity between user preferences for content items and generate a ranked list of content items having the similarity content preferences of the user, par. [0026], the measure of similarity between user preferences is based on historical rating data representing a number of users that indicate a preference for the first content item and a number of users that indicate a preference for the second content item);
- generating, using content with which the set of users has interacted, a list of content items (par. [0010], generate a ranked list of content items having the similarity content preferences of the user); and
- providing, for display in a user interface of the content platform, the list of content items (par. [0010], presenting the ranked list forming a content-based recommendation to the user).
Therefore, it would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to have modified the system of Shtof to include the features as disclosed by Swaminathan in order to produce content-based recommendations with improved quality and accuracy, thereby automatically and effectively allocating of weight data from medium content objects to content attributes.
Swaminathan also discloses the first user attribute data including account information of the first user comprising at least one of account activity, account login history, or age of account (par. [0010]-[0012], similarity score is determined based on a similarity between user preferences for content items and generate a ranked list of content items having the similarity content preferences of the user; and par. [0048], the machine-learned model may predict the user quality rating associated with a content item for a viewing user by weighting user quality ratings received from viewing users who have more user attributes (e.g., age group, click-through rate, etc.) in common with the viewing user more heavily than user quality ratings received from users who have fewer user attributes in common with the viewing user. As an additional example, since purchasing a product or subscribing to a service after clicking through an advertisement for the product or service is a reliable indicator of the quality of the advertisement, the machine-learned model may associate a greater weight with user quality ratings received from viewing users who purchase products or subscribe to services more often in conjunction with clicking on a content item than with user quality ratings received from viewing users who frequently click on advertisements, but do not subsequently make a purchase or subscribe to a service).
Claim 2, the combination of Shtof and Swaminathan discloses the invention as claimed. In addition, Swaminathan discloses the trained model is a tree-based model with a linear regressor (par. [0010]-[0014], linear equation regression techniques).
Claim 3, the combination of Shtof and Swaminathan discloses the invention as claimed. In addition, Swaminathan discloses the first user attribute data includes data for a plurality of attributes, including login history for the first user at the first platform, age of an account of the first user at the first platform, and types of investments held in the account of the first user (par. [0010]-[0012], similarity score is determined based on a similarity between user preferences for content items and generate a ranked list of content items having the similarity content preferences of the user; and par. [0048], the machine-learned model may predict the user quality rating associated with a content item for a viewing user by weighting user quality ratings received from viewing users who have more user attributes (e.g., age group, click-through rate, etc.) in common with the viewing user more heavily than user quality ratings received from users who have fewer user attributes in common with the viewing user. As an additional example, since purchasing a product or subscribing to a service after clicking through an advertisement for the product or service is a reliable indicator of the quality of the advertisement, the machine-learned model may associate a greater weight with user quality ratings received from viewing users who purchase products or subscribe to services more often in conjunction with clicking on a content item than with user quality ratings received from viewing users who frequently click on advertisements, but do not subsequently make a purchase or subscribe to a service).
Shto also discloses the first user attribute data includes data for a plurality of attributes, including login history for the first user at the first platform, age of an account of the first user at the first platform (par. [0064] The content recommendation system utilizes the user attributes of the user and content attributes of available content items to provide to the user as input to the model. The model generations predictions, such as scores, so to how likely the user is to interact with each content item; and par. [0003], specifying user attributes of the users that either did or did not interact with the content items after being provided with an opportunity to interact with the content items, wherein the user attributes correspond to an age of a user, a location of a user, a time at which a user performed an action such as logging into the video streaming service, a current location of the user, a home location of the user, demographic information about the user, interests of the user, and/or a wide variety of information about the user, activities of the user, preferences of the user).
Claim 4, the combination of Shtof and Swaminathan discloses the invention as claimed. In addition, Swaminathan discloses of the set of users of the first platform that are expected to have most similar content preferences to the first user includes a list of users of the first platform and corresponding scores representing a degree of similarity of content preferences between the first user and each respective user in the list of users (par. [0009], attributes associated with the preferred content items are then assigned weights generally proportional to the number of users who indicated their preference for the corresponding content items. For example, Movie A and Movie B are both highly popular, and a large number of users prefer both movies. Meanwhile, Movie C and Movie D are not very popular, and preferred by a small number of users, but most users who preferred Movie C also preferred Movie D; par. [0010]-[0012], similarity score is determined based on a similarity between user preferences for content items and generate a ranked list of content items having the similarity content preferences of the user, par. [0026], the measure of similarity between user preferences is based on historical rating data representing a number of users that indicate a preference for the first content item and a number of users that indicate a preference for the second content item).
Claim 5, the combination of Shtof and Swaminathan discloses the invention as claimed. In addition, Swaminathan discloses generating, using content with which the set of users has interacted, the list of content items, comprises: identifying content items with which each user in the set of users has interacted (par. [0010]-[0012], similarity score is determined based on a similarity between user preferences for content items and generate a ranked list of content items having the similarity content preferences of the user); generating the list of content items from among the identified content items based on a number of user interactions by the set of users with each identified content item (par. [0010]-[0012], similarity score is determined based on a similarity between user preferences for content items and generate a ranked list of content items having the similarity content preferences of the user).
Claim 26, Shtof discloses the computer-implemented method of claim 1, wherein the trained model is a machine learning model (par. [0007], machine learning functionality to generate a model. The model is significantly more accurate because it is additionally trained to take into account multiple options).
Claims 6-10, are system claims, which are corresponding to the method claims 1-5 above. Therefore, they are rejected under the same rational as claims 1-5.
Claims 11-15, are non-transitory claims, which are corresponding to the method claims 1-5. Therefore, they are rejected under the same rational as claims 1-5.
Claim 16, Shtof disclose a computer-implemented method, comprising:
- determining, based on user activity data, that a first user of a first platform has interacted with content of a content platform within a predetermined time period (par. [0002]-[0003], wherein a model to predict likelihoods that a particular user will interact with content items and outputs predictions of how likely the user is to interact with content items based upon content attributes of the content , wherein model specifies whether users interacted with content items (e.g., did a user watch a video provided as a suggestion by a video streaming service or not, did a user click on a recommendation to purchase an item or not; and par. [0062], the model can understand a continuous flow of time where a time period can correspond to similar user behavior, such as where users behave similarly from 1:00 am to 4:00 am, which can be understand by the model because gray codes for those time attribute values of 1:00 am to 4:00 am may correspond to similar options/sub-categories of a time attribute)
- identifying a first set of content items with which the first user has interacted during the within a predetermined time period (par. [0002]-[0003], a model to predict likelihoods that a particular user will interact with content items and outputs predictions of how likely the user is to interact with content items based upon content attributes of the content , wherein model specifies whether users interacted with content items (e.g., did a user watch a video provided as a suggestion by a video streaming service or not, did a user click on a recommendation to purchase an item or not; and par. [0062], the model can understand a continuous flow of time where a time period can correspond to similar user behavior, such as where users behave similarly from 1:00 am to 4:00 am, which can be understand by the model because gray codes for those time attribute values of 1:00 am to 4:00 am may correspond to similar options/sub-categories of a time attribute);
Shtof does not explicitly disclose “performing comparison of textual content between a first dataset representing text contained in the first set of content items and a second dataset representing text contained in other content items with which other users of the first platform have interacted during the predetermined time period”.
Meanwhile, Swaminathan discloses performing comparison of textual content between a first dataset representing text of titles associated with contained in the first set of content items and a second dataset representing text of titles associated with in other content items with which other users of the first platform have interacted during the predetermined time period (par. [0009], attributes associated with the preferred content items are then assigned weights generally proportional to the number of users who indicated their preference for the corresponding content items. For example, Movie A and Movie B (i.e. A and B are titles as indicated in par. [0082] of applicant’s specification) are both highly popular, and a large number of users prefer both movies. Meanwhile, Movie C and Movie D are not very popular, and preferred by a small number of users, but most users who preferred Movie C also preferred Movie D; par. [0010]-[0012], similarity score is determined based on a similarity between user preferences for content items and generate a ranked list of content items having the similarity content preferences of the user, par. [0026], the measure of similarity between user preferences is based on historical rating data representing a number of users that indicate a preference for the first content item and a number of users that indicate a preference for the second content item);
- identifying, based on comparing the first dataset and the second dataset, a list of content items that are similar to the first set of content items represented in the first dataset (par. [0010], and [0023], represent the similarity between any common attribute based on the comparing step and generate a ranked list of content items having the similarity content preferences of the user); and
- providing, for display in a user interface of the content platform, the list of content items (par. [0010], presenting the ranked list forming a content-based recommendation to the user).
Therefore, it would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to have modified the system of Shtof to include the features as disclosed by Swaminathan in order to produce content-based recommendations with improved quality and accuracy, thereby automatically and effectively allocating of weight data from medium content objects to content attributes.
Claim 17, the combination of Shtof and Swaminathan discloses the invention as claimed. In addition, Swaminathan discloses comparing the first dataset representing text in the first set of content items with the second dataset representing text in other content items with which other users of the first platform have interacted during the particular timeframe, comprises:
- generating the first dataset by numerically representing the text in the first set of content items (par. [0023], numerical statistic used as a weighting factor in information retrieval that represents the relative importance of one word with respect to a collection of words, such as may be found in a content attribute);
- generating the second dataset by numerically representing the text in the other content items (par. [0023], the measure of similarity for a given content item f.sub.i can be calculated using a cosine similarity between the TF-IDF vector of a.sub.i and the TF-IDF vector of b.sub.i); and
- comparing, using a word-mover distance algorithm, the first dataset and the second dataset (par. [0017] and [0023], comparing the similarity of information or data to represent a measure of the similarity between the genres of A and B, f.sub.2 represents a measure the similarity between actors in A and B, and f.sub.3 measures the similarity between movie descriptions).
Claim 18, The combination of Shtof and Swaminathan discloses the invention as claimed. In addition, Shtof disclose obtaining, from a user account repository stored on the first platform, first attribute data associated with the first user, the first user attribute data including account information of the first user comprising at least one of account activity, account login history, or age of account (par. [0003], specifying user attributes of the users that either did or did not interact with the content items after being provided with an opportunity to interact with the content items, wherein the user attributes correspond to an age of a user, a location of a user, a time at which a user performed an action such as logging into the video streaming service, a current location of the user, a home location of the user, demographic information about the user, interests of the user, and/or a wide variety of information about the user, activities of the user, preferences of the user));
- inputting the first user attribute data into a trained model, wherein the trained model is trained using user attribute data associated with a plurality of users of the first platform comprising account information and corresponding content preference data for the plurality of users to output a score representing degree of similarity in preferences of the first user and the plurality of users; (par. [0003], [0006], and [0039], content recommendation system may utilize machine learning functionality train and generate a model based upon user interaction data and predict how likely certain users are to interact with certain content items; par. [0010]-[0012], similarity score is determined based on a similarity between user preferences for content items and generate a ranked list of content items having the similarity content preferences of the user; par. [0044], the model can more accurately predict affinity scores corresponding to how likely users will interact with content items; and par. [0064] The content recommendation system utilizes the user attributes of the user and content attributes of available content items to provide to the user as input to the model. The model generations predictions, such as scores, so to how likely the user is to interact with each content item).
However, Shtof does not explicitly disclose “obtaining, using an output generated by the trained model in response to the input first user attribute data, data identifying of a set of users of the first platform that are expected to have most similar content preferences to the first user”.
Meanwhile, Swaminathan discloses obtaining, using an output generated by the trained model in response to the input first user attribute data, data identifying of a set of users of the first platform that are expected to have most similar content preferences to the first user (par. [0009], attributes associated with the preferred content items are then assigned weights generally proportional to the number of users who indicated their preference for the corresponding content items. For example, Movie A and Movie B are both highly popular, and a large number of users prefer both movies. Meanwhile, Movie C and Movie D are not very popular, and preferred by a small number of users, but most users who preferred Movie C also preferred Movie D; par. [0010]-[0012], similarity score is determined based on a similarity between user preferences for content items and generate a ranked list of content items having the similarity content preferences of the user, par. [0026], the measure of similarity between user preferences is based on historical rating data representing a number of users that indicate a preference for the first content item and a number of users that indicate a preference for the second content item);
- generating, using content with which the set of users has interacted, a second list of content items (par. [0010], and [0023], represent the similarity between any common attribute based on the comparing step and generate a ranked list of content items having the similarity content preferences of the user); and
- wherein providing, for display in the user interface of the content platform, the list of content items comprises providing, for display in the user interface of the content platform, the second list of content items and the list of content items (par. [0010], presenting the ranked list forming a content-based recommendation to the user).
Therefore, it would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to have modified the system of Shtof to include the features as disclosed by Swaminathan in order to produce content-based recommendations with improved quality and accuracy, thereby automatically and effectively allocating of weight data from medium content objects to content attributes.
Claims 19-21, are system claims, which are corresponding to the method claims 16-18 above. Therefore, they are rejected under the same rational as claims 16-18.
Claims 22-24, are non-transitory claims, which are corresponding to the method claims 16-18 above. Therefore, they are rejected under the same rational as claims 16-18.
Claim 25 is rejected under 35 U.S.C. 103 as being unpatentable over Shtof, in view of Swaminathan, and further in view of Ahlstrom et al. (US 2021/0097122 A1), hereinafter “Ahlstrom”.
Claim 25, The combination of Shtof and Swaminathan discloses the invention as claimed. In addition, Shtof further discloses determining that the first user of the first platform has not interacted with content of the content platform within the predetermined time period comprises: determining that the first user of the first platform has not interacted with content of the content platform within one of the following timeframes (par. [0002]-[0003], wherein a model to predict likelihoods that a particular user will interact with content items and outputs predictions of how likely the user is to interact with content items based upon content attributes of the content , wherein model specifies whether users interacted with content items (e.g., did a user watch a video provided as a suggestion by a video streaming service or not, did a user click on a recommendation to purchase an item or not; and par. [0062], the model can understand a continuous flow of time where a time period can correspond to similar user behavior, such as where users behave similarly from 1:00 am to 4:00 am, which can be understand by the model because gray codes for those time attribute values of 1:00 am to 4:00 am may correspond to similar options/sub-categories of a time attribute).
However, the combination of Shtof and Swaminathan does not discloses ‘discloses the last ten to fifteen days, the last one to two years, the last thirty days, the last sixty days, the last ninety days, a period of thirty days, or a period of ninety days”.
On the other hand, Ahlstrom discloses the last ten to fifteen days, the last one to two years, the last thirty days, the last sixty days, the last ninety days, a period of thirty days, or a period of ninety days (pars.[0054] and [0040], , those content items that have been viewed within the last month. If the particular user does not have a viewing history, the similarity index module 328 may score each content item in the recommendation list based on a viewing history of a most similar user, wherein an additional consideration to ensure relevancy is how recently a content item was viewed. For example, only content items that were viewed within a predetermined period—such as the last six months are considered. ).
Therefore, it would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to have modified the system of cited references to include the features as disclosed by Ahlstrom in order to provide each user with personalized content recommendations for viewing or reading encourages relevant learning as well as interaction with the content platform, improving user experience.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Loan T. Nguyen whose telephone number is (571) 270-3103. The examiner can normally be reached on Monday from 10:00 am - 6:00 pm, Thursday-Friday from 10:00 am - 2:00 pm. If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Aleksandr Kerzhner can be reached on (571) 270-1760. The fax phone number for the organization where this application or proceeding is assigned is 571-270-4103. Information regarding the status of an application may be obtained from the Patent Application Information Retrieval (PAIR) system. Status information for published applications may be obtained from either Private PAIR or Public PAIR. Status information for unpublished applications is available through Private PAIR only. For more information about the PAIR system, see http://pair-direct.uspto.gov. Should you have questions on access to the Private PAIR system, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative or access to the automated information system, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
06/19/2026
/LOAN T NGUYEN/Examiner, Art Unit 2165
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