Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
DETAILED ACTION
The Action is responsive to the Amendments and Remarks filed on 6/29/2026. Claims 1, 3-6, 8, 10-16, and 19 are pending claims. Claims 1, 16, and 19 are written in independent form. Claims 2, 7, 9, and 17-18 have been cancelled.
Claim Objections
Claims 6 and 19 are objected to because of the following informalities:
Claim 6 appears to recite a typographical error by reciting “wherein generating the prompt…” when the independent claims have been amended to recite “a text string prompt”. The intent is understood as reciting “wherein generating the text string prompt…”.
Claim 19 appears to recite typographical errors by reciting “process, by a processor, the first feature vector…” when the claim previously recites “computer-readable instructions that, when executed by a processor, cause the processor to:…”. The intent is understood as reciting “process, by [[a]] the processor, the first feature vector…”.
Appropriate 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, 3-6, 8, 10-16, and 19 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-patentable subject matter. The claimed invention is directed to one or more abstract ideas without significantly more. The judicial exception is not integrated into a practical application. The claims do not include additional elements that are sufficient to amount to significantly more than judicial exception. The eligibility analysis in support of these findings is provided below.
As per Claims 1, 16, and 19,
STEP 1:In accordance with Step 1 of the eligibility inquiry (as explained in MPEP 2106), the claimed method (claims 1, 3-6, 8, and 10-15), system (claim 16), and non-transitory computer-readable medium (claim 19) are directed to one of the eligible categories of subject matter and therefore satisfies Step 1.
STEP 2A Prong One:The independent claims 1, 16, and 19 recite the following limitations directed to an abstract idea:
Mapping the user data to a first feature vector or other mathematical representation in a vector space corresponding to a pre-determined set of metadata properties;
The limitation recites a mental process of observation, evaluation, judgement, and/or opinion capable of being performed by the human mind, or by a human using a pen and paper, by observing and evaluating the user data, a first feature vector or other mathematical representation in a vector space, and a pre-determined set of metadata properties, and based on the observation and evaluation, making a judgement and/or opinion of mapping the user data to the first feature vector or other mathematical representation in the vector space.
Mapping the content metadata associated with the group of content items to a second feature vector in the same vector space;
The limitation recites a mental process of observation, evaluation, judgement, and/or opinion capable of being performed by the human mind, or by a human using a pen and paper, by observing and evaluating the content metadata associated with the group of content items and a second feature vector in the same vector space, and based on the observation and evaluation, making a judgement and/or opinion of mapping the content metadata to the second feature vector in the vector space.
Processing the first feature vector or other mathematical representation and the second feature vector to determine a dot product or other measure of overlap between the first feature vector or other mathematical representation and the second feature vector;
The limitation recites a mathematical concept of executing a mathematical formula in the form of “a dot product or other measure of overlap” that takes as input “the first feature vector or other mathematical representation and the second feature vector or other mathematical representation” and outputs “the dot product or other measure of overlap”.
generating a text string prompt for a descriptive text generator model based on the dot product or other measure of overlap
The limitation recites a mental process of observation, evaluation, judgement, and/or opinion capable of being performed by the human mind, or by a human using a pen and paper, by observing and evaluating a dot product or other measure of overlap between two feature representations, and based on the observation and evaluation, making a judgement and/or opinion of a text string prompt for a descriptive text generator model.
Generating, by the descriptive text generator model at the remote model server, a descriptor for the group of content items in response to receiving the API call request, based on the text string prompt; and
The limitation recites a mathematical concept of executing a mathematical formula/function at a high level that takes as input the request comprising the “text string prompt” and returns/outputs “a descriptor for the group of content items” based on the input/prompt.
STEP 2A Prong Two:Claim 1 recites that the steps are “computer-implemented” using “at least a first data source”, “at least a second data source”, “a processing resource”, “an application programming interface (API)”, “a remote model server”, and “a display”, which is a high-level recitation of generic computer components and represents mere instructions to apply on a computer as in MPEP 2106.05(f), which does not provide integration into a practical application.
Claim 16 recites that the steps are performed using “processing circuitry”, “at least one data source”, “at least a second data source”, “an application programming interface (API)”, “a remote model server”, and “a display”, which is a high-level recitation of generic computer components and represents mere instructions to apply on a computer as in MPEP 2106.05(f), which does not provide integration into a practical application.
Claim 19 recites that the steps are performed using instructions comprised in “a non-transitory computer-readable medium”, “a processor”, “at least one data source”, “at least a second data source”, “an application programming interface (API)”, “a remote model server”, and “a display”, which is a high-level recitation of generic computer components and represents mere instructions to apply on a computer as in MPEP 2106.05(f), which does not provide integration into a practical application.
The claims recite the following additional elements:
Displaying, by a display of a user device, a content selection interface representing a plurality of content items comprising the group of content items, wherein the content items are displayed on the content selection interface in response to receiving user input;, and
The limitation recites “displaying…in response to receiving user input”, which is a high-level recitation of generic computer components and represents mere instructions to apply on a computer as in MPEP 2106.05(f), which does not provide integration into a practical application.
Obtaining content metadata associated with the group of content items;
The limitation recites an insignificant extra solution activity as retrieval of data (ie. Mere data gathering) such as ‘obtaining information’ as identified in MPEP 2106.05(g) and does not provide integration into a practical application.
Obtaining user data for the user,
The limitation recites an insignificant extra solution activity as retrieval of data (ie. Mere data gathering) such as ‘obtaining information’ as identified in MPEP 2106.05(g) and does not provide integration into a practical application.
Wherein the user data is based on content engagement data;
The limitation recites an insignificant extra-solution activity as selecting a particular type of data being used to base the user data on as identified in MPEP 2106.05(g) and does not provide integration into a practical application.
Transmitting, via an application programming interface (API), an API call request over a network to a remote model server,
The limitation recites an insignificant extra solution activity as sending/receiving data (ie. Mere data gathering) such as ‘obtaining information’ as identified in MPEP 2106.05(g) and does not provide integration into a practical application.
Wherein the API call request comprises the generated text string prompt packaged as part of the API call request;
The limitation recites an insignificant extra-solution activity as selecting a particular type of data being included in the API call request as identified in MPEP 2106.05(g) and does not provide integration into a practical application.
Receiving, by the processing resource, the generated descriptor from the remote model server,
The limitation recites an insignificant extra solution activity as sending/receiving data (ie. Mere data gathering) such as ‘obtaining information’ as identified in MPEP 2106.05(g) and does not provide integration into a practical application.
Displaying, by the display the generated descriptor as part of the content selection interface.
The limitation recites a high-level recitation of generic computer components and represents mere instructions to apply on a computer as in MPEP 2106.05(f), which does not provide integration into a practical application.
Viewing the additional limitations together and the claim as a whole, nothing provides integration into a practical application.
STEP 2B:
The conclusions for the mere implementation using a computer are carried over and does not provide significantly more.
With respect to “Obtaining content metadata associated with the group of content items;” identified as insignificant extra-solution activity above this is also WURC as court-identified see MPEP 2106.05(d)(II)(i).
With respect to “Obtaining user data for the user,” identified as insignificant extra-solution activity above this is also WURC as court-identified see MPEP 2106.05(d)(II)(i).
With respect to “Wherein the user data is based on content engagement data;” identified as insignificant extra-solution activity above this is also WURC as court-identified see MPEP 2106.05(d)(II)(iv).
With respect to “Transmitting, via an application programming interface (API), an API call request over a network to a remote model server,” identified as insignificant extra-solution activity above this is also WURC as court-identified see MPEP 2106.05(d)(II)(i).
With respect to “Wherein the API call request comprises the generated text string prompt packaged as part of the API call request;” identified as insignificant extra-solution activity above this is also WURC as court-identified see MPEP 2106.05(d)(II)(iv).
With respect to “Receiving, by the processing resource, the generated descriptor from the remote model server,” identified as insignificant extra-solution activity above this is also WURC as court-identified see MPEP 2106.05(d)(II)(i).
Looking at the claim as a whole does not change this conclusion and the claim is ineligible.
As per Dependent Claims 3-6, 8, and 10-15,
STEP 1:In accordance with Step 1 of the eligibility inquiry (as explained in MPEP 2106), the claimed method (claims 1, 3-6, 8, and 10-15), system (claim 16), and non-transitory computer-readable medium (claim 19) are directed to one of the eligible categories of subject matter and therefore satisfies Step 1.
STEP 2A Prong One:The dependent claims 3-6, 8, and 10-15 recite the following limitations directed to an abstract idea:
The limitation(s) of Dependent Claim 3 includes the step(s) of:
Generating the descriptor based on the obtained user data to provide a personalized descriptor for the group of content items.
The limitation recites a mental process of observation, evaluation, judgement, and/or opinion capable of being performed by the human mind, or by a human using a pen and paper, by observing and evaluating the obtained user data, and based on the observation and evaluation, making a judgement and/or opinion of a personalized descriptor for the group of content items.
The limitation(s) of Dependent Claim 4 includes the step(s) of:
Performing a comparison between the content metadata and the user data,
The limitation recites a mental process of observation, evaluation, judgement, and/or opinion capable of being performed by the human mind, or by a human using a pen and paper, by observing and evaluating the content metadata and the user data, and based on the observation and evaluation, making a judgement and/or opinion of similarities and differences (thus performing a comparison) between the content metadata and the user data.
Wherein the comparison may include determining the content metadata associated with the content items that is most relevant to the user and selecting and/or filtering the content metadata based on the comparison.
The limitation recites a mental process of observation, evaluation, judgement, and/or opinion capable of being performed by the human mind, or by a human using a pen and paper, by observing and evaluating by observing and evaluating the content metadata associated with the content, and based on the observation and evaluation, making a judgement and/or opinion of the content metadata that is most relevant to the user and making a judgement and/or opinion of selecting and/or filtering the content metadata.
The limitation(s) of Dependent Claim 5 includes the step(s) of:
Filtering the content metadata, a subset of the content metadata representing a customized and/or personalized set of the content metadata for the user is generated.
The limitation recites a mental process of observation, evaluation, judgement, and/or opinion capable of being performed by the human mind, or by a human using a pen and paper, by observing and evaluating the content metadata, and based on the observation and evaluation, making a judgement and/or opinion to filter the content metadata and generate a subset of content metadata representing a customized and/or personalized set of content metadata for the user.
The limitation(s) of Dependent Claim 6 includes the step(s) of:
Wherein generating the prompt for the descriptive text generator is based on the obtained user data and content metadata.
The limitation recites a mental process of observation, evaluation, judgement, and/or opinion capable of being performed by the human mind, or by a human using a pen and paper, by observing and evaluating the obtained user data and content metadata, and based on the observation and evaluation, making a judgement and/or opinion of a prompt for a descriptive text generator.
The limitation(s) of Dependent Claim 8 includes the step(s) of:
Determining an overlap and/or determining common metadata between the content metadata and the user data and
The limitation recites a mental process of observation, evaluation, judgement, and/or opinion capable of being performed by the human mind, or by a human using a pen and paper, by observing and evaluating metadata between the content metadata and the user data, and based on the observation and evaluation, making a judgement and/or opinion of an overlap or common metadata between the content metadata and the user data.
Generating the descriptor based on the overlap and/or common metadata.
The limitation recites a mental process of observation, evaluation, judgement, and/or opinion capable of being performed by the human mind, or by a human using a pen and paper, by observing and evaluating the overlap and/or common metadata, and based on the observation and evaluation, making a judgement and/or opinion of a descriptor.
The limitation(s) of Dependent Claim 10 includes the step(s) of:
Combining the metadata into group metadata for the group.
The limitation recites a mental process of observation, evaluation, judgement, and/or opinion capable of being performed by the human mind, or by a human using a pen and paper, by observing and evaluating the metadata, and based on the observation and evaluation, making a judgement and/or opinion to combine the metadata into group metadata for the group.
The limitation(s) of Dependent Claim 11 includes the step(s) of:
Wherein the plurality of content items are grouped into two or more groups,
The limitation recites a mental process of observation, evaluation, judgement, and/or opinion capable of being performed by the human mind, or by a human using a pen and paper, by observing and evaluating the plurality of content items, and based on the observation and evaluation, making a judgement and/or opinion to group the plurality of content items into two or more groups.
Generating a descriptor for each group using content metadata for each content item of the group
The limitation recites a mental process of observation, evaluation, judgement, and/or opinion capable of being performed by the human mind, or by a human using a pen and paper, by observing and evaluating content metadata for each content item of each group, and based on the observation and evaluation, making a judgement and/or opinion of a descriptor for each group.
The limitation(s) of Dependent Claim 13 includes the step(s) of:
Wherein generating the descriptor further comprises applying a pre-determined generative model or other machine learning or artificial intelligence model to at least the content metadata.
The limitation recites a mathematical concept of executing a mathematical formula in the form of “a pre-determined generative model or other machine learning or artificial intelligence model” that takes as input “the content metadata” as part of the generation of the descriptor.
STEP 2A Prong Two:The claim(s) recite the following additional elements:
The limitation(s) of Dependent Claim 3 includes the step(s) of:
Obtaining user data for the user;
The limitation recites an insignificant extra solution activity as retrieval of data (ie. Mere data gathering) such as ‘obtaining information’ as identified in MPEP 2106.05(g) and does not provide integration into a practical application.
The limitation(s) of Dependent Claim 6 includes the step(s) of:
Obtaining user data for the user and content metadata for a content item
The limitation recites an insignificant extra solution activity as retrieval of data (ie. Mere data gathering) such as ‘obtaining information’ as identified in MPEP 2106.05(g) and does not provide integration into a practical application.
The limitation(s) of Dependent Claim 10 includes the step(s) of:
Obtaining metadata associated with each content item in a group of content items
The limitation recites an insignificant extra solution activity as sending/receiving data (ie. Mere data gathering) such as ‘obtaining information’ as identified in MPEP 2106.05(g) and does not provide integration into a practical application.
The limitation(s) of Dependent Claim 11 includes the step(s) of:
Each group represented in the content selection interface as part of or associated with an interactive graphical element and
The limitation recites an insignificant extra-solution activity as selecting a particular type of data being used to represent each group in the content selection interface as identified in MPEP 2106.05(g) and does not provide integration into a practical application.
Displaying the interactive graphical element together with the descriptor.
The limitation recites “displaying…”, which is a high-level recitation of generic computer components and represents mere instructions to apply on a computer as in MPEP 2106.05(f), which does not provide integration into a practical application.
The limitation(s) of Dependent Claim 12 includes the step(s) of:
Wherein the content selection interface comprises a plurality of scrollable carousels and
The limitation recites an insignificant extra-solution activity as selecting a particular type of data being included in the content selection interface as identified in MPEP 2106.05(g) and does not provide integration into a practical application.
Providing respective at least said group of content to the user in a scrollable carousel of the user interface together with the generated descriptor.
The limitation recites an insignificant extra-solution activity as selecting a particular type of data being included in the user interface as identified in MPEP 2106.05(g) and does not provide integration into a practical application.
The limitation(s) of Dependent Claim 14 includes the step(s) of:
Wherein the generating of the descriptor is performed as part of a content recommendation process.
The limitation recites an insignificant extra-solution activity as selecting a particular type of process being used as identified in MPEP 2106.05(g) and does not provide integration into a practical application.
The limitation(s) of Dependent Claim 15 includes the step(s) of:
Obtaining a group of candidate items and/or their identifiers, optionally based on user data, as part of a content recommendation process and
The limitation recites an insignificant extra solution activity as sending/receiving data (ie. Mere data gathering) such as ‘obtaining information’ as identified in MPEP 2106.05(g) and does not provide integration into a practical application.
Retrieving metadata for said group of candidate items as part of the content recommendation process.
The limitation recites an insignificant extra solution activity as sending/receiving data (ie. Mere data gathering) such as ‘obtaining information’ as identified in MPEP 2106.05(g) and does not provide integration into a practical application.
Viewing the additional limitations together and the claim as a whole, nothing provides integration into a practical application.
STEP 2B:
The conclusions for the mere implementation using a computer are carried over and does not provide significantly more.
With respect to Claim 3 reciting “Obtaining user data for the user;” identified as insignificant extra-solution activity above this is also WURC as court-identified see MPEP 2106.05(d)(II)(i).
With respect to Claim 6 reciting “Obtaining user data for the user and content metadata for a content item” identified as insignificant extra-solution activity above this is also WURC as court-identified see MPEP 2106.05(d)(II)(i).
With respect to Claim 10 reciting “Obtaining metadata associated with each content item in a group of content items” identified as insignificant extra-solution activity above this is also WURC as court-identified see MPEP 2106.05(d)(II)(i).
With respect to Claim 11 reciting “Each group represented in the content selection interface as part of or associated with an interactive graphical element” identified as insignificant extra-solution activity above this is also WURC when claimed in a merely generic manner as court-identified see MPEP 2106.05(d)(II)(iv).
With respect to Claim 12 reciting “Wherein the content selection interface comprises a plurality of scrollable carousels” identified as insignificant extra-solution activity above this is also WURC when claimed in a merely generic manner as court-identified see MPEP 2106.05(d)(II)(iv).
With respect to Claim 12 reciting “Providing respective at least said group of content to the user in a scrollable carousel of the user interface together with the generated descriptor.” identified as insignificant extra-solution activity above this is also WURC when claimed in a merely generic manner as court-identified see MPEP 2106.05(d)(II)(iv).
With respect to Claim 14 reciting “Wherein the generating of the descriptor is performed as part of a content recommendation process.” identified as insignificant extra-solution activity above this is also WURC when claimed in a merely generic manner as court-identified see MPEP 2106.05(d)(II)(iv).
With respect to Claim 15 reciting “Obtaining a group of candidate items and/or their identifiers, optionally based on user data, as part of a content recommendation process” identified as insignificant extra-solution activity above this is also WURC when claimed in a merely generic manner as court-identified see MPEP 2106.05(d)(II)(iv).
With respect to Claim 15 reciting “Retrieving metadata for said group of candidate items as part of the content recommendation process.” identified as insignificant extra-solution activity above this is also WURC when claimed in a merely generic manner as court-identified see MPEP 2106.05(d)(II)(iv).
Looking at the claim as a whole does not change this conclusion and the claim is ineligible.
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.
Claim(s) 1, 3-6, 8, 10-11, 13-16, and 19 are rejected under 35 U.S.C. 103 as being unpatentable over Docherty et al. (U.S. Pre-Grant Publication No. 2019/0364338, hereinafter referred to as Docherty) and further in view of Wu et al. (U.S. Pre-Grant Publication No. 2010/0198837, hereinafter referred to as Wu) and Smith et al. (U.S. Pre-Grant Publication No. 2020/0117446, hereinafter referred to as Smith)
Regarding Claim 1:
Docherty teaches a computer-implemented method for generating text data comprising a descriptor for a group of content of items, the method comprising:
Displaying, by a display of a user device, a content selection interface representing a plurality of content items, comprising the group of content items, wherein the content items are displayed on the content selection interface in response to receiving user input;
Docherty teaches “the user is then presented, via a display of the user device, with a content selection screen displayed on a display screen and/or user interface, which presents the user with a choice of viewing different content items from the content source” (Para. [0142]). Therefore, Docherty teaches displaying a plurality of content items on “a content selection screen” where a user is able to select, or choose, viewing different content items (thus displaying different content items in response to receiving user input/choosing).
Obtaining, from at least a first data source, content metadata associated with a group of content items;
Docherty teaches “obtaining content information concerning content available from one or more content sources” (Para. [0011]) where “the content information comprises metadata of content” (Para. [0110]). Docherty further teaches content items in a group by teaching “unique identifiers for content item groups” (Para.[0129]).
Docherty also teaches “EPG content items and VOD content items sharing certain characteristics can be arranged into groups” (Para. [0204]).
Obtaining, from at least a second data source, user data for a user,
Docherty teaches “opening a content recommendation session for a selected user; retrieving at least some user data for the selected user from a first storage resource;” (Para. [0011]).
Docherty teaches multiple sources of data by teaching “using the user data from the second storage resource and content information concerning content available from one or more content sources” (Abstract).
Wherein the user data is based on content engagement data;
Docherty teaches “The user data may comprise at least one of: user action data representing previous user actions, optionally content selection, viewing or recording actions, user language data and/or episode data” (Para. [0030]) thereby teaching the user data being based on the user’s engagement with content.
Generating a descriptor for the group of content items;
Docherty teaches “For each content item group, either EPG or VOD, the information that is stored may include: an identifier for the group; a name for the group; a flag indicating if the group is free to view and therefore available to all customers; an indicator of video format of the group e.g. unknown, standard definition, high definition and 3D; one or more language labels; primary and secondary geographic area information. Concerning VOD content item groups, the primary and secondary geographic information can be used to allow customers from different countries access to different content. If the group is associated with a channel then an identifier and mapping to the channel may also be stored. One or more content item groups can be associated with a channel number.” (Para. [0205]). Therefore, Docherty teaches generating a descriptor for the group of content items based on content metadata associated with the group.
Docherty further teaches “EPG content items and VOD content items sharing certain characteristics can be arranged into groups” (Para. [0204]) and generating “a given name” which “can be associated with, for example, an actor or director involved with or appearing in the content item. For a given name associated with the content item, an identifier for the role of the content item is also stored” (Para. [0208]).
At least one of displaying, by the display of the user device, the generated descriptor, and storing, by a storage resource, the generated descriptor.
Docherty teaches “that the content item group information “is stored” (Para. [0205]). Docherty further teaches “the collated content information source collates content information concerning content items stored on at least one of the plurality of data stores, storage devices and/or storage areas” (Para. [0050]).
Docherty also teaches “the user is then presented, via a display of the user device, with a content selection screen displayed on a display screen and/or user interface, which presents the user with a choice of viewing different content items from the content source” (Para. [0142]).
Docherty explicitly teaches all of the elements of the claimed invention as recited above except:
Mapping the user data to a first feature vector or other mathematical representation in a vector space corresponding to a pre-determined set of metadata properties;
Mapping the content metadata associated with the group of content items to a second feature vector in the same vector space;
Processing, by a processing resource, the first feature vector or other mathematical representation and the second feature vector or mathematical representation to determine a dot product or other measure of overlap between the first feature vector or other mathematical representation and the second feature vector;
Generating a text string prompt for a descriptive text generator model based on the dot product or other measure of overlap;
Transmitting, via an application programming interface (API), an API call request over a network to a remote model server, wherein the API call request comprises the generated text string prompt packaged as part of the API call request;
Generating, by the descriptive text generator model at the remote model server, a descriptor for the group of content items in response to receiving the API call request, based on the text string prompt;
Receiving, by the processing resource, the generated descriptor from the remote model server;
Displaying, by the display, the generated descriptor as part of the content selection interface.
However, in the related field of endeavor of providing search results, Wu in combination with Docherty teaches:
Mapping the user data to a first feature vector or other mathematical representation in a vector space corresponding to a pre-determined set of metadata properties;
Wu teaches “A feature vector is generated for each search result in D.sub.i and D.sub.i. For example, a feature vector can include one or more features (e.g., terms) and a corresponding statistical measure of the importance of the feature to the user” (Para. [0055]). Therefore, Wu teaches representing both the user data (importance of features to the user) and the content metadata in feature vectors (features of the search result).
Mapping the content metadata associated with the group of content items to a second feature vector in the same vector space;
Wu teaches “A feature vector is generated for each search result in D.sub.i and D.sub.i. For example, a feature vector can include one or more features (e.g., terms) and a corresponding statistical measure of the importance of the feature to the user” (Para. [0055]). Therefore, Wu teaches representing both the user data (importance of features to the user) and the content metadata in feature vectors (features of the search result).
Processing, by a processing resource, the first feature vector or other mathematical representation and the second feature vector or mathematical representation to determine a dot product or other measure of overlap between the first feature vector or other mathematical representation and the second feature vector;
Wu teaches “a summary of the entity in accordance with one of the aspects is presented. A summary of an entity in accordance with an aspect is a direct presentation of information that is available through search results corresponding to the entity and the aspect. For example, the ski and snow report presented in box 706 is a summary of information for the entity "mount bachelor" and the aspect "weather." A user interested in the "weather" aspect is likely interested in knowing the current weather, so rather than requiring the user to click on a search result to see weather information, the system can instead directly present information on the weather. As another example, if the entity is "University of Southern California football team" and the aspect is "season record," a summary of the team's season record can be presented. As yet another example, if the entity is a particular movie, and the aspect is movie reviews, then multiple reviews can be presented side by side.” (Para. [0106]).
Wu further teaches “the system associates the entity with a class on the fly, for example, by accessing knowledge base information (e.g., crawling a website such as Wikipedia.TM.) and identifying a class associated with the received entity, or issuing a query with a Hearst pattern including the entity. Other techniques for associating an entity with a class are also possible. For example, the entity can be classified based on machine learning techniques, such as support vector machines. Alternatively, a user can specify the class that is associated with an entity.” (Para. [0047]).Therefore, Wu teaches obtaining user and content metadata (both represented by feature vectors as taught in Para. [0055]) and generating a prompt to a machine learning model (for performing machine learning techniques) which generates and returns a class label/descriptive text for the entity.It is further noted that Wu teaches “Other similarity measures can also be used, for example…calculating the similarity scores based on the similarity of the two feature vectors, e.g., based on the cosine distance” (Para. [0065]) and calculating cosine distances between feature vectors necessarily relies on the use of dot products.
Generating a text string prompt for a descriptive text generator model based on the dot product or other measure of overlap;
Wu teaches “the system associates the entity with a class on the fly, for example, by accessing knowledge base information (e.g., crawling a website such as Wikipedia.TM.) and identifying a class associated with the received entity, or issuing a query with a Hearst pattern including the entity. Other techniques for associating an entity with a class are also possible. For example, the entity can be classified based on machine learning techniques, such as support vector machines. Alternatively, a user can specify the class that is associated with an entity.” (Para. [0047]).Therefore, Wu teaches obtaining user and content metadata (both represented by feature vectors as taught in Para. [0055]) and generating a prompt to a machine learning model (for performing machine learning techniques) which generates and returns a class label/descriptive text for the entity.It is further noted that Wu teaches “Other similarity measures can also be used, for example…calculating the similarity scores based on the similarity of the two feature vectors, e.g., based on the cosine distance” (Para. [0065]) and calculating cosine distances between feature vectors necessarily relies on the use of dot products.
Wu further teaches generating a text string prompt by teaching a “issuing a query with a Hearst pattern including the entity” (Para. [0047]) which Wu teaches an example of a query with a Hearst pattern by teaching “querying the search system 114 for Hearst patterns, e.g., if the entity is "Boston," a query for "X such as Boston" can be issued to the search system” (Para. [0046]).
Transmitting, via an application programming interface (API), an API call request over a network to a remote model server, wherein the API call request comprises the generated text string prompt packaged as part of the API call request;
Wu further teaches “the system associates the entity with a class on the fly, for example, by accessing knowledge base information (e.g., crawling a website such as Wikipedia.TM.) and identifying a class associated with the received entity, or issuing a query with a Hearst pattern including the entity. Other techniques for associating an entity with a class are also possible. For example, the entity can be classified based on machine learning techniques, such as support vector machines. Alternatively, a user can specify the class that is associated with an entity.” (Para. [0047]) thereby teaching transmitting a prompt as a request to a model, performing machine learning techniques, for classifying an entity.Docherty teaches using an API call request for communicating by teaching “an application programming interface (the recommendation engine API)” for data “to be communicated between the user device and the CRE 22” (Para. [0118]).
Generating, by the descriptive text generator model at the remote model server, a descriptor for the group of content items in response to receiving the API call request, based on the text string prompt;
Wu teaches “a summary of the entity in accordance with one of the aspects is presented. A summary of an entity in accordance with an aspect is a direct presentation of information that is available through search results corresponding to the entity and the aspect. For example, the ski and snow report presented in box 706 is a summary of information for the entity "mount bachelor" and the aspect "weather." A user interested in the "weather" aspect is likely interested in knowing the current weather, so rather than requiring the user to click on a search result to see weather information, the system can instead directly present information on the weather. As another example, if the entity is "University of Southern California football team" and the aspect is "season record," a summary of the team's season record can be presented. As yet another example, if the entity is a particular movie, and the aspect is movie reviews, then multiple reviews can be presented side by side.” (Para. [0106]).
Wu further teaches “the system associates the entity with a class on the fly, for example, by accessing knowledge base information (e.g., crawling a website such as Wikipedia.TM.) and identifying a class associated with the received entity, or issuing a query with a Hearst pattern including the entity. Other techniques for associating an entity with a class are also possible. For example, the entity can be classified based on machine learning techniques, such as support vector machines. Alternatively, a user can specify the class that is associated with an entity.” (Para. [0047]).Therefore, Wu teaches obtaining user and content metadata and generating a prompt to a pre-determined machine learning model (for performing machine learning techniques) which generates and returns a class label/descriptive text for the entity.
Receiving, by the processing resource, the generated descriptor from the remote model server;
Wu teaches “The system receives one or more sets of search results (step 606). Each set of search results corresponds to an entity and one of the identified aspects. For example, if the entity was "Hawaii" and the identified aspects were "beaches," "hotels," "weather," and "food," separate sets of search results could be received for "Hawaii beaches," "Hawaii hotels," "Hawaii weather," and "Hawaii food." The search results can be received in response to a query issued to the search engine 130 for the entity and an aspect. The system presents the search results based on the identified aspects (step 608). In some implementations, the search results are presented in a "mashup," where relevant results and other information for one or more of the aspects are presented in one display, organized according to aspect.” (Paras. [0102] – [0103]) thereby teaching receiving the generated class label/descriptive text.
Displaying, by the display, the generated descriptor as part of the content selection interface.
Wu teaches displaying generated descriptors for groups of content items by teaching “a summary of the entity in accordance with one of the aspects is presented. A summary of an entity in accordance with an aspect is a direct presentation of information that is available through search results corresponding to the entity and the aspect. For example, the ski and snow report presented in box 706 is a summary of information for the entity "mount bachelor" and the aspect "weather." A user interested in the "weather" aspect is likely interested in knowing the current weather, so rather than requiring the user to click on a search result to see weather information, the system can instead directly present information on the weather. As another example, if the entity is "University of Southern California football team" and the aspect is "season record," a summary of the team's season record can be presented. As yet another example, if the entity is a particular movie, and the aspect is movie reviews, then multiple reviews can be presented side by side. In some implementations, the summary is associated with an aspect and an entity in advance and stored, for example, in a database. The system can then retrieve the summary when needed.” (Para. [0106]).
Thus, it would have been obvious to one of ordinary skill in the art, having the teachings of Wu and Docherty at the time that the claimed invention was effectively filed, to have combined the faceted browsing of search results, as taught by Wu, with the systems and methods for providing one or more content item recommendations for a user of a content distribution system, as taught by Docherty.
One would have been motivated to make such combination because Wu teaches “Particular embodiments of the subject matter described in this specification can be implemented so as to realize one or more of the following advantages. Aspects of an entity in a search query can be identified. Aspects can be presented to make it easy for users to explore the search space along multiple axes. The use of aspects allows a user to explore the search space beyond the scope of his or her original query. The presentation of aspects also allows a user to quickly gain an overview of what the possible axes of search are. The presentation of aspects can allow a user to browse a search space efficiently, for example, by using faceted browsing. Information related to the aspects can be identified and presented to the user. This information can allow a user to quickly gain information he or she needs about multiple aspects of the entity. Mashups can be presented to a user as a way of visualizing information about the aspects of the entity. The mashups present information associated with several aspects in a single integrated interface.” (Para. [0016]). It would have been obvious to a person having ordinary skill in the art that allowing users to view multiple aspects of an entity related to a search query and using faceted browsing would improve the user’s ability to visually navigate to the entity or facet of interest, thus improving the user’s experience by not having to visually filter through results related to uninteresting entities or facets.
Regarding Claim 3:
Wu and Docherty further teach:
Obtaining user data for the user;
Docherty teaches “retrieving at least some user data for the selected user from a first storage resource” (Para. [0011]).
Generating the descriptor based on the obtained user data to provide a personalized descriptor for the group of content items.
Docherty teaches “In a simple example, if it is determined from the user data that a user has previously watched movies starring a particular actor, or watched football matches featuring a particular team, then the CRE 22 may produce a recommendation for the user to watch a movie or other content featuring that actor, or a programme concerning that football team, if such movie, programme or other content is currently available or will soon be available via the available content sources.” (Para. [0148]).
Docherty further teaches “EPG content items and VOD content items sharing certain characteristics can be arranged into groups” (Para. [0204]) and generating “a given name” which “can be associated with, for example, an actor or director involved with or appearing in the content item. For a given name associated with the content item, an identifier for the role of the content item is also stored” (Para. [0208]).Therefore, Docherty teaches generating the descriptor based on the overlap of actor metadata to generate a “given name” for a group of content items related to an actor or particular team a user previously watched (represented in the user data).
Regarding Claim 4:
Wu and Docherty further teach:
Performing a comparison between the content metadata and the user data,
Docherty teaches “User data can be stored in respect of a variety of different user actions or events, for example selecting, viewing, recording or searching for content.” (Para. [0124]) thereby teaching performing a comparison via a search between the user data and metadata of the content being searched.Docherty further teaches “Following retrieval of user data and obtaining content source information, the CRE 22 is configured to use the user data located in the user cache 6 together with the available content information to generate a personalised or other content item recommendation for the user” (Para. [0148]).
Wherein the comparison may include determining the content metadata associated with the content items that is most relevant to the user and selecting and/or filtering the content metadata based on the comparison.
Docherty teaches the comparison including determining content metadata associated with content items that are most relevant (personalized) to the user and selecting the content metadata based on the comparison (generate…content item recommendation for the user) by teaching “Following retrieval of user data and obtaining content source information, the CRE 22 is configured to use the user data located in the user cache 6 together with the available content information to generate a personalised or other content item recommendation for the user” (Para. [0148]).
Docherty further teaches “In a simple example, if it is determined from the user data that a user has previously watched movies starring a particular actor, or watched football matches featuring a particular team, then the CRE 22 may produce a recommendation for the user to watch a movie or other content featuring that actor, or a programme concerning that football team, if such movie, programme or other content is currently available or will soon be available via the available content sources.” (Para. [0148]).
Docherty also teaches filtering the content metadata by teaching “It is also known for viewers to filter the large choice of content using a search function.” (Para. [0004]).
Regarding Claim 5:
Wu and Docherty further teach:
Filtering the content metadata, a subset of the content metadata representing a customized and/or personalized set of the content metadata for the user is generated.
Docherty teaches filtering the content metadata by teaching “It is also known for viewers to filter the large choice of content using a search function.” (Para. [0004]).
Docherty further teaches the comparison including determining content metadata associated with content items that are most relevant (personalized) to the user and selecting the content metadata based on the comparison (generate…content item recommendation for the user) by teaching “Following retrieval of user data and obtaining content source information, the CRE 22 is configured to use the user data located in the user cache 6 together with the available content information to generate a personalised or other content item recommendation for the user” (Para. [0148]).
Regarding Claim 6:
Wu and Docherty further teach:
Obtaining user data for the user and content metadata for a content item and wherein generating the prompt for the descriptive text generator is based on the obtained user data and content metadata.
Wu teaches “a summary of the entity in accordance with one of the aspects is presented. A summary of an entity in accordance with an aspect is a direct presentation of information that is available through search results corresponding to the entity and the aspect. For example, the ski and snow report presented in box 706 is a summary of information for the entity "mount bachelor" and the aspect "weather." A user interested in the "weather" aspect is likely interested in knowing the current weather, so rather than requiring the user to click on a search result to see weather information, the system can instead directly present information on the weather. As another example, if the entity is "University of Southern California football team" and the aspect is "season record," a summary of the team's season record can be presented. As yet another example, if the entity is a particular movie, and the aspect is movie reviews, then multiple reviews can be presented side by side.” (Para. [0106]).
Wu further teaches “the system associates the entity with a class on the fly, for example, by accessing knowledge base information (e.g., crawling a website such as Wikipedia.TM.) and identifying a class associated with the received entity, or issuing a query with a Hearst pattern including the entity. Other techniques for associating an entity with a class are also possible. For example, the entity can be classified based on machine learning techniques, such as support vector machines. Alternatively, a user can specify the class that is associated with an entity.” (Para. [0047]).Therefore, Wu teaches obtaining user and content metadata and generating a prompt to a machine learning model (for performing machine learning techniques) which generates and returns a class label/descriptive text for the entity.
Regarding Claim 8:
Wu and Docherty further teach:
Determining an overlap and/or determining common metadata between the content metadata and the user data and
Docherty teaches the comparison including determining content metadata associated with content items that are most relevant (personalized) to the user and selecting the content metadata based on the comparison (generate…content item recommendation for the user) by teaching “Following retrieval of user data and obtaining content source information, the CRE 22 is configured to use the user data located in the user cache 6 together with the available content information to generate a personalised or other content item recommendation for the user” (Para. [0148]).
Docherty further teaches “In a simple example, if it is determined from the user data that a user has previously watched movies starring a particular actor, or watched football matches featuring a particular team, then the CRE 22 may produce a recommendation for the user to watch a movie or other content featuring that actor, or a programme concerning that football team, if such movie, programme or other content is currently available or will soon be available via the available content sources.” (Para. [0148]).
Generating the descriptor based on the overlap and/or common metadata.
Docherty teaches “In a simple example, if it is determined from the user data that a user has previously watched movies starring a particular actor, or watched football matches featuring a particular team, then the CRE 22 may produce a recommendation for the user to watch a movie or other content featuring that actor, or a programme concerning that football team, if such movie, programme or other content is currently available or will soon be available via the available content sources.” (Para. [0148]).
Docherty further teaches “EPG content items and VOD content items sharing certain characteristics can be arranged into groups” (Para. [0204]) and generating “a given name” which “can be associated with, for example, an actor or director involved with or appearing in the content item. For a given name associated with the content item, an identifier for the role of the content item is also stored” (Para. [0208]).Therefore, Docherty teaches generating the descriptor based on the overlap of actor metadata to generate a “given name” for a group of content items.
Regarding Claim 10:
Wu and Docherty further teach:
Obtaining metadata associated with each content item in a group of content items and
Docherty teaches “A group of EPG content items may be considered as equivalent to a broadcast television channel. VOD content items can be grouped into logical groups, for example, movie categories. VOD content item groups can be used to enable or restrict access to content items on a per customer basis. PVR content information is collected and stored in the PVR table 32” (Para. [0204]).
Combining the metadata into group metadata for the group.
Docherty teaches “A group of EPG content items may be considered as equivalent to a broadcast television channel. VOD content items can be grouped into logical groups, for example, movie categories. VOD content item groups can be used to enable or restrict access to content items on a per customer basis. PVR content information is collected and stored in the PVR table 32” (Para. [0204]).
Regarding Claim 11:
Wu and Docherty further teach:
Wherein the plurality of content items are grouped into two or more groups,
Docherty teaches “if it is determined from the user data that a user has previously watched movies starring a particular actor, or watched football matches featuring a particular team, then the CRE 22 may produce a recommendation for the user to watch a movie or other content featuring that actor, or a programme concerning that football team, if such movie, programme or other content is currently available or will soon be available via the available content sources” (Para. [0148]) thereby teaching grouping content items into different groups for recommendation, such as one based on “a particular actor” and another based on “a particular [football] team”.
each group represented in the content selection interface as part of or associated with an interactive graphical element and
Wu teaches each group represented in an interface as part of or associated with an interactive graphical element of “Search results and other information corresponding to aspects for Mount Bachelor (e.g., "weather," "hotels" "community college" and "mountains") are labeled in accordance with the aspect and presented to the user in the boxes 706, 708, 710, and 712” (Para. [0104]).
Generating a descriptor for each group using content metadata for each content item of the group and
Docherty teaches “For each content item group, either EPG or VOD, the information that is stored may include: an identifier for the group; a name for the group; a flag indicating if the group is free to view and therefore available to all customers; an indicator of video format of the group e.g. unknown, standard definition, high definition and 3D; one or more language labels; primary and secondary geographic area information. Concerning VOD content item groups, the primary and secondary geographic information can be used to allow customers from different countries access to different content. If the group is associated with a channel then an identifier and mapping to the channel may also be stored. One or more content item groups can be associated with a channel number.” (Para. [0205]).
Docherty further teaches “if it is determined from the user data that a user has previously watched movies starring a particular actor, or watched football matches featuring a particular team, then the CRE 22 may produce a recommendation for the user to watch a movie or other content featuring that actor, or a programme concerning that football team, if such movie, programme or other content is currently available or will soon be available via the available content sources” (Para. [0148])
Therefore, Docherty teaches generating a descriptor for each group (the particular football team and the particular actor) of content items based on content metadata associated with the group.
Displaying the interactive graphical element together with the descriptor.
Wu teaches each group represented in an interface as part of or associated with an interactive graphical element of “Search results and other information corresponding to aspects for Mount Bachelor (e.g., "weather," "hotels" "community college" and "mountains") are labeled in accordance with the aspect and presented to the user in the boxes 706, 708, 710, and 712” (Para. [0104] & Fig. 7).
Regarding Claim 13:
Wu and Docherty further teach:
Wherein generating the descriptor further comprises applying a pre-determined generative model or other machine learning or artificial intelligence model to at least the content metadata.
Wu teaches “a summary of the entity in accordance with one of the aspects is presented. A summary of an entity in accordance with an aspect is a direct presentation of information that is available through search results corresponding to the entity and the aspect. For example, the ski and snow report presented in box 706 is a summary of information for the entity "mount bachelor" and the aspect "weather." A user interested in the "weather" aspect is likely interested in knowing the current weather, so rather than requiring the user to click on a search result to see weather information, the system can instead directly present information on the weather. As another example, if the entity is "University of Southern California football team" and the aspect is "season record," a summary of the team's season record can be presented. As yet another example, if the entity is a particular movie, and the aspect is movie reviews, then multiple reviews can be presented side by side.” (Para. [0106]).
Wu further teaches “the system associates the entity with a class on the fly, for example, by accessing knowledge base information (e.g., crawling a website such as Wikipedia.TM.) and identifying a class associated with the received entity, or issuing a query with a Hearst pattern including the entity. Other techniques for associating an entity with a class are also possible. For example, the entity can be classified based on machine learning techniques, such as support vector machines. Alternatively, a user can specify the class that is associated with an entity.” (Para. [0047]).Therefore, Wu teaches obtaining user and content metadata and generating a prompt to a pre-determined machine learning model (for performing machine learning techniques) which generates and returns a class label/descriptive text for the entity.
Regarding Claim 14:
Wu and Docherty further teach:
Wherein the generating of the descriptor is performed as part of a content recommendation process.
Docherty teaches “The present invention relates to a content recommendation system and method.” (Para. [0001]) thereby teaching all of the actions being performed as part of a content recommendation process/method.
Regarding Claim 15:
Wu and Docherty further teach:
Obtaining a group of candidate items and/or their identifiers, optionally based on user data, as part of a content recommendation process and
Docherty teaches “Following retrieval of user data and obtaining content source information, the CRE 22 is configured to use the user data located in the user cache 6 together with the available content information to generate a personalised or other content item recommendation for the user.” (Para. [0148]).
Retrieving metadata for said group of candidate items as part of the content recommendation process.
Docherty teaches “Entries from the databases on the hard disk storage resource 4 can be retrieved by the content recommendations module via requests made through the data access layer. Entries in the databases may also be updated via the data access layer.” (Para. [0120]) thereby teaching retrieving metadata for said group of candidate items as entries from the databases “by the content recommendations module”, and thus as part of the content recommendation process.
Regarding Claim 16:
Some of the limitations herein are similar to some or all of the limitations of Claim 1.
Wu and Docherty further teach:
A system comprising processing circuitry (Docherty – Paras. [0080] & [0094]).
Regarding Claim 19:
Some of the limitations herein are similar to some or all of the limitations of Claim 1.
Wu and Docherty further teach:
A non-transitory computer-readable medium that comprises computer-readable instructions that, when executed by a processor, cause the processor to perform steps (Docherty – Paras. [0080] & [0094]).
Claim(s) 12 is rejected under 35 U.S.C. 103 as being unpatentable over Docherty and Wu, and further in view of Leach et al. (U.S. Pre-Grant Publication No. 2024/0386046, hereinafter referred to as Leach).
Regarding Claim 12:
Wu and Docherty explicitly teach all of the elements of the claimed invention as recited above except:
Wherein the content selection interface comprises a plurality of scrollable carousels and
Providing respective at least said group of content to the user in a scrollable carousel of the user interface together with the generated descriptor.
However, in the related field of endeavor of providing search results, Wu teaches:
Wherein the content selection interface comprises a plurality of scrollable carousels and
Leach teaches “The themes and/or search results organized by theme by the thematic search engine may be rendered in the search results page according to a variety of different ways, e.g., lists, user interface (UI) cards or objects, horizontal carousel, vertical carousel, etc.” (Para. [0051]).
Leach further teaches “a plurality” by teaching in Fig. 1B that each theme cycles horizontally off the screen, rather than all of the themes being a single cycling display together.
Providing respective at least said group of content to the user in a scrollable carousel of the user interface together with the generated descriptor.
Leach teaches “The themes and/or search results organized by theme by the thematic search engine may be rendered in the search results page according to a variety of different ways, e.g., lists, user interface (UI) cards or objects, horizontal carousel, vertical carousel, etc.” (Para. [0051])
Thus, it would have been obvious to one of ordinary skill in the art, having the teachings of Leach, Wu, and Docherty at the time that the claimed invention was effectively filed, to have combined the carousel interface for navigating the different groupings of search results, as taught by Leach, with the faceted browsing of search results, as taught by Wu, and with the systems and methods for providing one or more content item recommendations for a user of a content distribution system, as taught by Docherty.
One would have been motivated to make such combination because Leach teaches “The themes and/or search results organized by theme by the thematic search engine may be rendered in the search results page according to a variety of different ways, e.g., lists, user interface (UI) cards or objects, horizontal carousel, vertical carousel, etc” (Para. [0051]) and it would have been obvious to a person having ordinary skill in the art that rendering search results in a carousel would be advantageous for users with a small display screens to be able to see more results at a reasonable display size for each group/theme.
Response to Amendment
Applicant’s Amendments, filed on 6/29/2026, are acknowledged and accepted.
In light of the Amendments and Remarks filed on 6/29/2026, the claim objections to Claims 1, 16, 17, and 19 have been withdrawn.
In light of the Amendments and Remarks filed on 6/29/2026, the 112(b) rejections to Claims 1, 3-6, 8, 10-17, and 19 have been withdrawn.
Response to Arguments
On page 10-11 of the Remarks filed on 6/29/2026, Applicant argues, with respect to the 101 rejection, that “Claim 1, as amended, provides clear technical efficiencies and improvements by reciting a method that maps user data and content metadata to the same vector space and forms a text string prompt based on the overlap between the resulting feature vectors. By calculating an overlap in a vector space and generating a text string prompt based on that overlap, rather than sending the user data and content metadata themselves directly to the text generation model, a more efficient prompt generation and descriptor generation process is obtained. In particular, claim 1, as amended, first maps the user data and content metadata to feature vectors in the same vector space, calculates a dot product or other measure of overlap between those vectors, and generates a text string prompt based on that overlap. This approach provides a reduction in data bandwidth transmitted between the various parts of the network. In addition, since the method allows for the prompt complexity to be significantly reduced and focused, model performance and efficiency is improved, including improving model processing time and accuracy.”Applicant’s argument is not convincing because nowhere in the specification is there support for the argued benefits. Upon further review, the present specification does not even refer to “efficiency” in any form nor does it refer to any reduction in data bandwidth and Applicant has not provided any citation that might support to the conclusion that the claimed features would lead to the argued “more efficient prompt generation and descriptor generation process” or the “reduction in data bandwidth”.
On page 12 of the Remarks filed on 6/29/2026, Applicant argues, with respect to the 103 rejection, that “the combination of Docherty and Wu does not teach or suggest the features of claim 1, as amended” because “in contrast [to Wu’s feature vectors], claim 1, as amended, recites mapping user data from at least a second data source based on content engagement data to a first feature vector in a vector space corresponding to a pre-determined set of metadata properties, and mapping the content metadata associated with the group of content items to a second feature vector in the same vector space.Applicant’s argument is not convincing for at least the reason that the argued scope and the scope of the claims does not match. In particular, the amended claims recite obtaining user data, the user data being based on content engagement data, and does not recite the mapping of the user data being based on content engagement data, as is being argued.
On page 12 of the Remarks filed on 6/29/2026, Applicant argues, with respect to the 103 rejection, that “the combination of Docherty and Wu does not teach or suggest the features of claim 1, as amended” because while Wu describes generating and processing feature vectors that both include importance of features to a user, claim 1 recites a first feature vector based on user data and a second feature vector that is not based on user data but rather represents metadata for a group of content items.Applicant’s argument is not convincing because the claims do not necessitate that the metadata for the group of content items is not related to user data. To the contrary, the claims actually recite displaying the content items “by a display of a user device” and “wherein the content items are displayed on the content selection interface in response to receiving user input” thereby linking the content items in at least some form to at least the user providing the input.
On page 12 of the Remarks filed on 6/29/2026, Applicant argues, with respect to the 103 rejection, that “the combination of Docherty and Wu does not teach or suggest the features of claim 1, as amended” because “Wu's similarity scores are used for a different purpose than that recited in claim 1, as amended. Wu describes calculating similarity scores between feature vectors of different aspects for an entity to identify candidate aspects that should be combined into a single aspect.” which is “categorically different from claim 1, as amended, which recites determining a dot product or other measure of overlap between a first feature vector representing user data and a second feature vector representing content metadata, and generating a text string prompt for a descriptive text generator model based on that overlap. Wu does not teach or suggest using vector overlap to generate a text string prompt for a descriptive text generator model.”Applicant’s argument is not convincing because the claims are silent as to how the “dot product or other measure of overlap” is used in generating the text string prompt. The claims broadly recite that the generation is merely “based on the dot product or other measure of overlap”.It is recommended to review Paras. [0181]-[0184] of the present specification and to amend the claims to clarify how the “dot product or other measure of overlap” (Para. [0181]-[0182]) is used to generate the text string prompt, such as through filtering of content metadata to represent a customized and/or personalized set of the content metadata for the user, the subset of content metadata and/or filtered feature vector from step 1006 being processed to generate the prompt (Paras. [0183]-[0184]).
On page 13 of the Remarks filed on 6/29/2026, Applicant argues, with respect to the 103 rejection, that “the combination of Docherty and Wu does not teach or suggest the features of claim 1, as amended” because “Wu's machine learning techniques are for classification, not text generation. Wu describes classifying an entity based on machine learning techniques such as support vector machines, which produces class labels. See Wu, paragraph [0047]. Classifying an entity into a predefined class is fundamentally different from generating descriptive text via a text string prompt to a descriptive text generator model at a remote model server, as recited in claim 1, as amended. Wu does not teach transmitting a text string prompt packaged as part of an API call request to a remote model server to generate descriptive text.”
Applicant’s argument is convincing that Wu alone does not teach the amended language of transmitting a text string prompt packaged as part of an API call request. However, upon further review, it was found that Wu in combination with Docherty teaches the amended language, as is explained in full in the rejection above.
On page 13 of the Remarks filed on 6/29/2026, Applicant argues, with respect to the 103 rejection, that “the combination of Docherty and Wu does not teach or suggest the features of claim 1, as amended” because “there is no motivation to use vector comparison results to generate prompts for text generation models. The specific technological pipeline recited in claim 1, as amended, from feature vector mapping to prompt generation to API call transmission to a remote model server to descriptor display on a content selection interface, is not present in the cited references.”Applicant’s argument is not convincing because the claims are silent as to how the “dot product or other measure of overlap” (vector comparison results) is used in generating the text string prompt. The claims broadly recite that the generation is merely “based on the dot product or other measure of overlap”.As is mentioned above, it is recommended to review Paras. [0181]-[0184] of the present specification and to amend the claims to clarify how the “dot product or other measure of overlap” (Para. [0181]-[0182]) is used to generate the text string prompt, such as through filtering of content metadata to represent a customized and/or personalized set of the content metadata for the user, the subset of content metadata and/or filtered feature vector from step 1006 being processed to generate the prompt (Paras. [0183]-[0184]).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Freed et al. (U.S. Patent No. 11,562,029) teaches an intelligent computer platform to identify a lexical answer type (LAT), a first concept relevant to the received request and a second concept related to the identified first concept. The LAT, together with the first and second concepts are utilized to create a first and second cluster. Documents are selectively populated into the clusters. The clusters are subject to sorting based on a relevancy protocol.
Hohwald et al. (U.S. Pre-Grant Publication No. 2018/0189325) teaches clustering search results based on image composition. A system may, for each image in a set of images, determine a compositional vector representing one or more objects and corresponding locations within the image using a trained computer-operated convolutional neural network. The system may provide each image through a clustering algorithm to produce one or more clusters based on compositional similarity. The system may provide images from the set of images clustered by composition, in which the images include a different listing of images for each of the one or more clusters. The system may provide a prioritized listing of images responsive to a user search query, in which the prioritized listing of images includes a different listing of images for each cluster of compositional similarity based on the metadata of each image associated with the cluster.
Ramesh et al. (U.S. Pre-Grant Publication No. 2023/0023201) teaches generating dynamic merchant similarity predictions. In one aspect, a system is configured for receiving historical datasets that include a series of merchants from historical browsing sessions generated by one or more users. The merchants are converted into corresponding vector representations for training a predictive model to output associated merchants based on a generated weighted vector space. Once sufficiently trained, data from a new browsing session may be received, which may include a target merchant. The target merchant is input into the predictive model as a vector to output one or more context merchants having vectors with the highest cosine similarity value to the target merchant vector. Selected context merchants may then be transmitted to the user device as targeted merchant suggestions in the new browsing session. The predictive models may be continuously trained using data received from subsequent browsing sessions.
Salowitz et al. (U.S. Pre-Grant Publication No. 2025/0005072) teaches Machine learning techniques are leveraged to provide personalized assistance on a computing device. In some configurations a timeline of a user's interactions with the computing device is generated. For example, screenshots and audio streams may be saved as entries in the timeline. Context—the state of the computing device when the entry is created, such as which documents and websites are open—is also stored. Entries in the timeline are processed by a model to generate embedding vectors. The timeline may be searched by finding the embedding vector that is closest to an embedding vector derived from a search query. The user may select a query result, causing the associated context to be restored. For example, if the query is “show me all documents related to my upcoming trip to Japan”, the query result may open documents and websites that were open when booking a flight to Japan.
Smith et al. (U.S. Pre-Grant Publication No. 2020/0117446) teaches “if a cluster of search results is identified, one of the cluster may be selected as representative of the cluster and displayed in the search results. Any other search results which are in the cluster may be associated with the representative example and accessible in the displayed search results. For example, in some embodiments, a search result display for a representative example of a cluster may include a link or button which links to more search results similar to that search result. In this way, a link or button such as a “see more like this” link may facilitate follow-on searches for similar search results.” (Para. [0146])
Stoop et al. (U.S. Pre-Grant Publication No. 2018/0101540) teaches “FIG. 7 illustrates an example method 700 for diversifying video search results. The method may begin at step 710, where the social-networking system 160 may receive, from a client system 130 of a first user, a search query inputted by the first user. At step 720, the social-networking system 160 may retrieve an initial set of videos that match the search query. At step 730, the social-networking system 160 may filter the initial set of videos to determine a filtered set of videos, wherein the filtering comprises, for each of one or more modal videos in the initial set of videos, removing from the initial set of videos one or more duplicate videos based on the one or more duplicate videos having a digital fingerprint that is within a threshold degree of sameness from a digital fingerprint of the modal video. At step 740, the social-networking system 160 may calculate, for each video in the filtered set, one or more similarity-scores with respect to one or more other videos in the filtered set, respectively, wherein each similarity-score corresponds to a degree of similarity in the features of the video with the respective other video. At step 750, the social-networking system 160 may group the videos in the filtered set into a plurality of clusters, each cluster comprising videos having a similarity-score greater than a threshold similarity-score with respect to each other video in the cluster. At step 760, the social-networking system 160 may send, to the client system 130 of the first user for display, a search-results interface comprising one or more search results for one or more videos in the filtered set, respectively, wherein the search results are organized within the search-results interface based on the respective clusters of their corresponding videos.” (Para. [0069] & Fig. 7).
Swen (U.S. Pre-Grant Publication No. 2006/0117002) teaches predetermine and record the classes of each indexed document with respect to each of its index keywords, and to provide high quality and relevant classification of the document when it is searched with said keyword. Document classes, recorded in advance, are used as the clustering information of each document in the search results to realize efficient, large-scale and high quality search result clustering. One embodiment provides a method for search result clustering, which includes recording the classes of each indexed document when the document is searched with each of its index keywords. This method further includes grouping the search results according to the classes of each result document with respect to the keyword or keywords contained in the search query. By prerecording the classes of each document with respect to each index keyword, the classes of each document in the search results in response to a search query can be directly determined via the keywords included in the search query. Each result document is put into each of its classes associated with each of the search keywords, and the union of all the classes of the result documents is used to construct the final document clusters for the search results. The clusters are ranked according to the ranks of documents included in each cluster and the weights of the clustered documents in the corresponding cluster. The clustered search results are presented to the user in such a way that clusters with higher ranks, and documents with higher ranks in each cluster are preferentially presented. Each cluster can be displayed and navigated in an independent framed subarea of the output window.
Vadrevu et al. (U.S. Pre-Grant Publication No. 2012/0016877) teaches clustering a plurality of documents using one or more clustering algorithms to obtain one or more first sets of clusters, wherein: each first set of clusters results from clustering the documents using one of the clustering algorithms; and with respect to each first set of clusters, each of the documents belongs to one of the clusters from the first set of clusters; accesses a search query; identifies a search result in response to the search query, wherein the search result comprises two or more of the documents; and clusters the search result to obtain a second set of clusters, wherein each document of the search result belongs to one of the clusters from the second set of clusters.
The reference further teaches “by presenting a search result of news articles in cluster format…it is much easier for a user to browse the search result in terms of news stories or topics rather than individual news articles” (Para. [0024]).
Gupta et al. (U.S. Pre-Grant Publication No. 2023/0394040) teaches providing, based on a partial query string, a plurality of autosuggestions that are diverse in nature such that the user is more likely to see the preferred complete query terms and therefore more likely to select one of the preferred suggestions which will increase search efficiency. As described herein, such functionality relates to generating cluster groups of candidate suggestions, each cluster including sub-topics, then performing the search based on a selected cluster or sub-topic. To generate the cluster groups, systems and methods, as described herein, analyze the similarity between candidate suggestions as well as the popularity of generated sub-topics. The cluster groups and sub-topics may be displayed visually, and in certain embodiments the cluster groups are ordered vertically from top to bottom and aligned to the left side of the display, while sub-topics are ordered horizontally from left to right following the cluster label.The reference further teaches “setting the cluster name based on the highest ranked sub-topic within the cluster if the highest ranked sub-topic shares a common prefix with most of the suggestions within the cluster; searching on the search feature for “top 5 popular sub-topics within cluster” and using the title of the first search result as the cluster name; utilize machine learning to train a natural language generation model to take the top 5 sub-topics within the cluster as input to the model, then generating a cluster name based on the input; using words which occur in most of the highest ranked sub-topics to form the cluster name; and/or matching a common prefix of several sub-topics within the cluster with a standard entity list from a knowledge base to form the cluster name. It should be appreciated that the exemplary methods for choosing a cluster name are not an exhaustive list but are offered as examples of the myriad of options available.” (Para. [0065]).
Venkatesh (U.S. Pre-Grant Publication No. 2026/0065540) teaches triggering functionality on data to be generated in a user interface and/or data shown or visualized in a user interface based on a natural language request that references actions to be performed and data items to use in performing the actions. The user interface actions are triggered based on a structured object generated by a large language model (LLM), which may then be processed, validated, and used to carry out the actions. The LLM may be instructed to use control(s) of a displayed representation of a set of data, and the structured object generated by the LLM may cause updating, on the user interface, the displayed representation to reflect change(s) requested (e.g., to adjust filters, change a visualization or view, or zoom in or out on a set of multidimensional data). The control(s) may be selected from among representation transformation action(s) that are also available to be performed against the displayed representation via direct user input.The reference further teaches “an example user interface 1700 showing a result of selecting content 1702 in main region 916 of the user interface 1700 and typing “Generate a narrative for this table in 3 bullet points” into the input region 914 of the chat region or interaction region 306, with a narrative response 1726 generated in the chat region or interaction region 306 pursuant to interaction with an LLM and execution of a corresponding action. Chat history is shown in interaction region 306, including history 1622, 1724, and 1726.” (Para. [0088]).
Shirwadkar et al. (U.S. Pre-Grant Publication No. 2025/0259035) teaches a first group of content items may be identified. A content interface may be provided for display on a client device. The content interface may comprise a first selectable input for accessing a first content item of the first group of content items, a second selectable input for accessing a second content item of the first group of content items, and/or a question and answer interface. A query may be received via the question and answer interface. Using a generative AI tool, a response to the query may be generated based upon the first group of content items. A representation of the response to the query may be displayed via the question and answer interface.The reference further teaches “the first generative AI tool 554 is configured to generate a content item (e.g., an article, such as a customized article, a summary, etc. wherein the content item may comprise text and/or at least one of images, audio, video, etc., for example) based upon an input. In some examples, the first AI-generated content item 556 may comprise a summary of the first group of content items 524, an article based upon the first group of content items 524 (e.g., a customized article that focuses on one or more entities of the set of salient entities), entity-focused content (e.g., content that focuses on one or more entities of the set of salient entities) and/or other type of content.” (Para. [0054]).
Kong et al. (U.S. Pre-Grant Publication No. 2025/0209544) teaches providing content events that are relevant to a first user of a social network are provided. In particular, a computing device may obtain content data associated with one or more content events, obtain user engagement data associated with the first user, determine a relevance score for each of the one or more content events using a relevance predictive model based on the user engagement data and attributes associated with the respective content event, the relevance score of each of the one or more content events representing a likelihood of the first user to engage with the respective content event, ranking the content events based on the relevance score for each of the one or more content events, and presenting a subset of the content events to the first user on a user interface of a device based on the ranking.
Nguyen (U.S. Pre-Grant Publication No. 2025/0384101) teaches identify a user of a mobile device based on an interaction of the user on a communications network, retrieve from a database an aggregated history of activity of the user with the service on the communications network, receive a request to access a first page on the mobile device belonging to a set of pages that is formed of page elements that are linkable to each other as a sequence of pages, and in response to the request to access the first page on the mobile device, dynamically select a second page of the set of pages, generate a personalized page element for the second page, integrate the personalized page element into the second page to generate a personalized second page, and link the personalized second page to the first page, the second page being accessible as a next page of the first page.The reference further teaches “the disclosed system can extract historical user activity on a UI platform (e.g., types of services used, frequently used interactable elements, etc.) to submit a custom prompt to the LLM for generating an output response (e.g., text string) describing a unique combination of UI element characteristics (e.g., background color, font size, location, API connections, etc.). In some implementations, the custom prompt can be further configured to elicit a structured output response (e.g., JSON format) from the LLM that enables compatibility with programmatic frameworks.” (Para. [0018]) and “there is a need for a robust system that can generate custom interface workflows that adapts application content to match preferences (e.g., personalized recommendations) and individual requirements of each end user. Additionally, there is a need for a smart system that can simplify navigation (e.g., interconnections between pages) within interface workflows to enable users to seamlessly traverse the application.” (Para. [0022]).
Non-Patent Literature Eberhart et al., "Generating Clarifying Questions for Query Refinement in Source Code Search", 24 Jan 2022, < https://doi.org/10.48550/arXiv.2201.09974> (Year: 2022) teaches in source code search, a common information seeking strategy involves providing a short initial query with a broad meaning, and then iteratively refining the query using terms gleaned from the results of subsequent searches. This strategy requires programmers to spend time reading search results that are irrelevant to their development needs. In contrast, when programmers seek information from other humans, they typically refine queries by asking and answering clarifying questions. Clarifying questions have been shown to benefit general-purpose search engines, but have not been examined in the context of code search. We present a method for generating natural-sounding clarifying questions using information extracted from function names and comments. Our method outperformed a keyword based method for single-turn refinement in synthetic studies, and was associated with shorter search duration in human studies. Index Terms—source code search, code retrieval, clarifying questions, query refinement, software maintenance.The reference further teaches “This mechanism can be used for SCS engines that embed functions and queries in the same vector space; it works by creating an updated vector representation of the query shifted towards candidate result vectors and away from rejected ones (according to predefined hyperparameters), and then reranking each result by cosine similarity” (Page 5 Column 1).
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/ROBERT F MAY/Examiner, Art Unit 2154 8/7/2026
/SYED H HASAN/Primary Examiner, Art Unit 2154
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