DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
Priority
Acknowledgment is made of applicant’s claim for foreign priority under 35 U.S.C. 119 (a)-(d), Korean Application No. 10-2024-0133734 dated 10/02/2024 and 10-2025-0000238 dated 01/02/2025 have been placed of record in the file.
Information Disclosure Statement
The information disclosure statements (IDSs) submitted on 10/08/2025 and 01/13/2026 have been received and considered. The submission is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statements are being considered by the examiner.
Drawings
The applicant’s drawings submitted are acceptable for examination purposes.
Specification
The applicant’s specification submitted is acceptable for examination purposes.
Examiner Notes
(1) In the case of amending the Claimed invention, Applicant is respectfully requested to indicate the portion(s) of the specification which dictate(s) the structure relied on for proper interpretation and also to verify and ascertain the metes and bounds of the claimed invention. This will assist in expediting compact prosecution. MPEP 714.02 recites: “Applicant should also specifically point out the support for any amendments made to the disclosure. See MPEP § 2163.06. An amendment which does not comply with the provisions of 37 CFR 1.121 (b), (c), (d), and (h) may be held not fully responsive. See MPEP § 714.” Amendments not pointing to specific support in the disclosure may be deemed as not complying with provisions of 37 C.F.R. 1.131 (b), (c), (d), and (h) and therefore held not fully responsive. Generic statements such as "Applicants believe no new matter has been introduced" may be deemed insufficient.
(2) Examiner cites particular columns, paragraphs, figures and line numbers in the references as applied to the claims below for the convenience of the applicant. Although the specified citations are representative of the teachings in the art and are applied to the specific limitations within the individual claim, other passages and figures may apply as well. It is respectfully requested that, in preparing responses, the applicant fully consider the references in their entirety as potentially teaching all or part of the claimed invention, as well as the context of the passage as taught by the prior art or disclosed by the Examiner.
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 1-15 are directed to non-statutory subject matter because it does not fall within four category of patentable subject matter recited in 35 U.S.C 101 (Process, machine manufacture or composition of matter).
When considering subject matter eligibility under 35 USC 101, it must be determined whether the claim is directed to one of the four statutory categories of invention, i.e., process, machine, manufacture, or composition of matter (Step 1). If the claim does fall within one of the statutory categories, it must then be determined whether the claim is directed to a judicial exception (i.e., law of nature, natural phenomenon, and abstract idea) (Step 2A), and if so, it must additionally be determined whether the claim is a patent-eligible application of the exception. If an abstract idea is present in the claim, any element or combination of elements in the claim must be sufficient to ensure that the claim amounts to significantly more than the abstract idea itself (Step 2B). Examples of abstract ideas include fundamental economic practices; certain methods of organizing human activities; an idea itself; and mathematical relationships/formulas.
Analysis
STEP 1:
Claims 1, 11 and 15 subject matter falls within the four statutory categories of patentable subject matter identified by 35 U.S.C. § 101: process, machine, manufacture, or composition of matter.
STEP 2A, PRONG l (Claim 1):
Under step 2A, prong 1, of the 2019 Guidance, we first look to whether the claim recites any judicial exceptions, including certain groupings of abstract ideas (i.e., mathematical concepts, certain methods of organizing human activities such as a fundamental economic practice, or mental processes). MPEP § 2106.04(a).
Limitation recites:
receive, [through the communication part], recommended content information obtained external to the electronic apparatus, based on a user input”
input data, based on the user input and use history information corresponding to content information viewed by the user, into [the at least one artificial intelligence model], including a model trained to output recommended content information based on use history information”
provide both the recommended content information obtained external to the electronic apparatus and the recommended content information based on the user input and use history information”
Claim 1 recites limitations “input data, based on the user input and use history information corresponding to content information viewed by the user […] to output recommended content information based on use history information” which are all steps that could be performed in the mind with the aid of pen and paper hence are mental processes. These limitations are processes that, under their broadest reasonable interpretation, covers performance of the limitation in the mind, but for the recitation of generic computer components. For example, based on user request’s criteria/keyword and user history/past data regarding how content being view by user, user can mentally determine the recommendation/preferred contents based on user’s request user request’s criteria/keyword and user history/past data.
Accordingly, limitation (a), (b) and (d) recites a patent-ineligible abstract idea.
STEP 2A, PRONG 2 (Claim 1):
Limitations “receive…” and “provide… ” respectively, which merely constitute extra-insignificant solution activity (mere data gathering and output, selecting a particular data source or type of data to be manipulated; see MPEP 2106.05(g) – presenting offers, selecting information examples).
The additional limitations “a memory configured to store at least one instruction and at least one artificial intelligence model”; “a communication part” and at least one processor” describe generic computer components, akin to adding the word "apply it" in connection with the abstract idea.
Further noted,
STEP 2B (Claim 1):
Under step 2B, the limitations “receive…” and “provide… merely constitute extra-insignificant solution activity (mere data gathering and output, selecting a particular data source or type of data to be manipulated; see MPEP 2106.05(g) – presenting offers, selecting information examples) and is well-known, conventional, and routine in the art (See Mayo, 566 U.S. at 79, 101 USPQ2d at 1968; OIP Techs., Inc. v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1092-93 (Fed. Cir. 2015) (presenting offers and gathering statistics amounted to mere data gathering). Further noted, as “artificial intelligence model” merely use as a tool for processing input and providing output; it does not reflect any technology improvement in artificial intelligence model.
Viewed as a whole, the additional claim elements do not provide meaningful limitations sufficient to transform the abstract idea into a patent eligible application of the abstract idea such that the claims amount to “significantly more” than the abstract idea itself. Therefore, claim 1 is rejected under 35 U.S.C. §101 as being directed to non-statutory subject matter.
Claims 11 and 15 are being rejected under U.S.C. 101 for similar reason.
Claims 2-10, 12-14 are dependent on their respective parent claims 1, 11 and 15, respectively and include all the limitations of claims 1, and 11; since these claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception, thus the claims are direct to abstract idea.
Claims 1-15 are therefore not drawn to eligible subject matter as they are directed to an abstract idea without significantly more.
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 of this title, 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, 7-11 and 15 rejected under 35 U.S.C. 103 as being unpatentable over KIM et al. (U.S. Pub. No. 2020/0221179 A1) in view of Bayer et al. (U.S. Pub. No. 2017/0103135 A1).
Regarding claim 1, KIM teaches an electronic apparatus, comprising: a memory configured to store at least one instruction and at least one artificial intelligence model; a communication part; and at least one processor configured to, by executing the at least one instruction, cause the electronic apparatus to:
input data, based on the user input and use history information corresponding to content information viewed by the user, into the at least one artificial intelligence model, including a model trained to output recommended content information based on use history information (paragraph [0063], receive a user input; also see paragraph [0073], user input correspond to certain event, the certain event may be an event for searching for a channel or content, an event for selecting the channel or content…; also see paragraph [0105], the viewing history information may be obtained by distinguishing a user input actively selecting content and a user input manually selecting content and applying different weight values; also see paragraph [0094]-[0095], values of weights applied to the first weight and the second content may be obtained through operation through a neural network; the neural network may perform information processing, using for example artificial intelligence; also see paragraph [0257]-[0058], the neural network may be an AI network based on AI technology, and may train input data to obtain weight values; the neural network may train the input data to obtain the recommendation list; for example, the neural network may receive the actual viewing history as input data and train the received input data to obtain the recommendation list based on viewing history information; also see paragraph [0112]-[0115]).
KIM does not explicitly disclose: receive, through the communication part, recommended content information obtained external to the electronic apparatus, based on a user input.
Bayer teaches: receive, through the communication part, recommended content information obtained external to the electronic apparatus, based on a user input (paragraph [0050], received search query includes device terms or application terms, select media content items from multiple media content items based on available through media devices or media applications corresponding to the terms in the search query; for example, in response to receiving the query “Romance comedy on my CAST-enable device”, process 100 can select media contents items that are available through the CAST-enabled device indicated I the search query and select a subset of those media content items based on popularity information; also see Fig. 3, paragraph [0054]-[0055], determine that there are multiple content sources for providing the selected media content; for live television content sources, can determine available content sources based on popularity information…).
It would have been obvious to one of ordinary skill in art before the effective filing date of the claim invention to include receive, through the communication part, recommended content information obtained external to the electronic apparatus, based on a user input into content recommendations of KIM.
Motivation to do so would be to include receive, through the communication part, recommended content information obtained external to the electronic apparatus, based on a user input for providing one or more recommended content sources that are available to the user (Bayer, paragraph [0031], line 1-10).
KIM as modified by Bayer further teach:
and provide both the recommended content information obtained external to the electronic apparatus and the recommended content information based on the user input and use history information (Bayer, paragraph [0050], received search query includes device terms or application terms, select media content items from multiple media content items based on available through media devices or media applications corresponding to the terms in the search query; for example, in response to receiving the query “Romance comedy on my CAST-enable device”, process 100 can select media contents items that are available through the CAST-enabled device indicated I the search query and select a subset of those media content items based on popularity information; also see Fig. 3, paragraph [0054]-[0055], determine that there are multiple content sources for providing the selected media content; for live television content sources, can determine available content sources based on popularity information…; also see paragraph [0058], for on demand content source, process can determine available content sources based on popularity information, application installations, and/or historical information; noted, providing both the recommended content obtained from live source and from on-demand source in response to user query, in combination the teaching of KIM, paragraph [0063], receive a user input; also see paragraph [0073], user input correspond to certain event, the certain event may be an event for searching for a channel or content, an event for selecting the channel or content…; also see paragraph [0105], the viewing history information may be obtained by distinguishing a user input actively selecting content and a user input manually selecting content and applying different weight values; also see paragraph [0094]-[0095], values of weights applied to the first weight and the second content may be obtained through operation through a neural network; the neural network may perform information processing, using for example artificial intelligence; also see paragraph [0257]-[0058], the neural network may be an AI network based on AI technology, and may train input data to obtain weight values; the neural network may train the input data to obtain the recommendation list; for example, the neural network may receive the actual viewing history as input data and train the received input data to obtain the recommendation list based on viewing history information; also see paragraph [0122]-[0123], genre may be classified into sports, movies, dramas, music, news, life styles, etc….; also see paragraph [0127]-[0128], the recommendation list using a channel that provided content included in the same genre as the content that the user has view a lot as the recommendation channel based on the viewing history information; also see paragraph [0112]-[0115], it reads on as claimed).
As per claims 11 and 15, these claims are rejected on grounds corresponding to the same rationales given above for rejected claim 1 and are similarly rejected.
Regarding claim 7, KIM as modified by Bayer teach all claimed limitations as set forth in rejection of claim 1, further teach: wherein the recommended content information based on the user input and use history information is internally recommended content information through the at least one artificial intelligence model based on data obtained based on the user input and the use history information, the at least one processor is configured to: obtain a plurality of scores for a plurality of candidate recommended contents included in the internally recommended content information and the recommended content information obtained external to the electronic apparatus, respectively, based on the use history information, and the provided recommended content information including identification information for a plurality of identified recommended contents from among the plurality of candidate recommended contents based on the plurality of scores (KIM, paragraph [0078], the memory may store information including viewing time, channel, and genre information of content viewed by the user for a certain period; also see paragraph [0105], the viewing history information may be obtained by distinguishing a user input actively selecting content and a user input manually selecting content and applying different weight values; also see paragraph [0122]-[0123], genre may be classified into sports, movies, dramas, music, news, life styles, etc….; also see paragraph [0105], the viewing history information may be obtained by distinguishing a user input actively selecting content and a user input manually selecting content and applying different weight values; also see paragraph [0115]-[0116], the aggressiveness of the suer for viewing the content may be lower than the second type of input or the third type of input, therefore, the fourth weight may have a smaller value than the second weight or the third weight; also see paragraph [0127]-[0128], the recommendation list using a channel that provided content included in the same genre as the content that the user has view a lot as the recommendation channel based on the viewing history information; noted, higher weight indicates higher score for content recommendation [genre as the content that user has view a lot…based on viewing history]; in combination with recommended information obtained from external sources taught by KIM, paragraph [0050], received search query includes device terms or application terms, select media content items from multiple media content items based on available through media devices or media applications corresponding to the terms in the search query; for example, in response to receiving the query “Romance comedy on my CAST-enable device”, process 100 can select media contents items that are available through the CAST-enabled device indicated I the search query and select a subset of those media content items based on popularity information; also see Fig. 3, paragraph [0054]-[0055], determine that there are multiple content sources for providing the selected media content; for live television content sources, can determine available content sources based on popularity information…, it reads on as claimed ).
Regarding claim 8, KIM as modified by Bayer teach all claimed limitations as set forth in rejection of claim 1, further teach: wherein the at least one processor is configured to: the provided recommended content information including a plurality of playable contents from among the recommended content information obtained external to the electronic apparatus (KIM, paragraph [0127]-[0128], the recommendation list using a channel that provided content included in the same genre as the content that the user has view a lot as the recommendation channel based on the viewing history information; while Bayer, paragraph [0037], present to the user that includes information on multiple recommended media content items, and in response to select a portion of the lure interface, a number of recommendation interfaces can be presented that include information and playback options for the recommended media content items on the lure interface; in combination with recommended content information obtained from external sources taught by KIM, paragraph [0050], received search query includes device terms or application terms, select media content items from multiple media content items based on available through media devices or media applications corresponding to the terms in the search query; for example, in response to receiving the query “Romance comedy on my CAST-enable device”, process 100 can select media contents items that are available through the CAST-enabled device indicated I the search query and select a subset of those media content items based on popularity information; also see Fig. 3, paragraph [0054]-[0055], determine that there are multiple content sources for providing the selected media content; for live television content sources, can determine available content sources based on popularity information…, it reads on as claimed).
Regarding claim 9, KIM as modified by Bayer teach all claimed limitations as set forth in rejection of claim 1, further teach: wherein the at least one artificial intelligence model includes a model trained to output recommended content information based on both the use history information and the recommended content information obtained external to the electronic apparatus, and the at least one processor is configured to: obtain the recommended content information through the at least one artificial intelligence model based on the recommended content information obtained external to the electronic apparatus and the data (KIM, paragraph [0127]-[0128], the recommendation list using a channel that provided content included in the same genre as the content that the user has view a lot as the recommendation channel based on the viewing history information; also see paragraph [0257]-[0058], the neural network may be an AI network based on AI technology, and may train input data to obtain weight values; the neural network may train the input data to obtain the recommendation list; for example, the neural network may receive the actual viewing history as input data and train the received input data to obtain the recommendation list based on viewing history information; while Bayer, paragraph [0037], present to the user that includes information on multiple recommended media content items, and in response to select a portion of the lure interface, a number of recommendation interfaces can be presented that include information and playback options for the recommended media content items on the lure interface; in combination with recommended content information obtained from external sources taught by KIM, paragraph [0050], received search query includes device terms or application terms, select media content items from multiple media content items based on available through media devices or media applications corresponding to the terms in the search query; for example, in response to receiving the query “Romance comedy on my CAST-enable device”, process 100 can select media contents items that are available through the CAST-enabled device indicated I the search query and select a subset of those media content items based on popularity information; also see Fig. 3, paragraph [0054]-[0055], determine that there are multiple content sources for providing the selected media content; for live television content sources, can determine available content sources based on popularity information…, it reads on as claimed).
Regarding claim 10, KIM as modified by Bayer teach all claimed limitations as set forth in rejection of claim 1, further teach: wherein the user input includes text obtained from a user voice input corresponding to a request for the recommended content information (Bayer, paragraph [0038], process 100 can begin by receiving the query from a user, such a query can be received from the user by any suitable approach, such as a voice search or a text search…).
Claims 2-3, and 12-13 are rejected under 35 U.S.C. 103 as being unpatentable over KIM et al. (U.S. Pub. No. 2020/0221179 A1) in view of Bayer et al. (U.S. Pub. No. 2017/0103135 A1), further in view of MANAVOGLU et al. (U.S. Pub. No. 2024/0256757 A1).
Regarding claim 2, KIM as modified by Bayer teach all claimed limitations as set forth in rejection of claim 1, but do not explicitly disclose: obtaining a prompt associated with a request for identification information for at least one content corresponding to the user input based on the use history information, and obtaining the recommended content information through the at least one artificial intelligence model based on data including the prompt.
MANAVOGLU teaches: obtain a prompt associated with a request for identification information for at least one content corresponding to the user input based on the use history information, and obtain the recommended content information through the at least one artificial intelligence model based on data including the prompt (paragraph [0027], the prompt provided to the generative model can include information in addition to input set forth by a user; a prompt provided to the generative model can include, is not limited to including a) user input set forth to the generative model and/or the search engine; 2) a query generated by the generative model based upon the user input; 3) information extracted from search results identified by the search engine based upon the user input and/or the query…; also see paragraph [0036], the prompt optionally includes information from a profile of the user, such as historical information of the user, identified references of the user, etc.; also see paragraph [0038], the generative model generates output based upon the prompt and the output is caused to be presented in the GUI).
It would have been obvious to one of ordinary skill in art before the effective filing date of the claim invention to include obtaining a prompt associated with a request for identification information for at least one content corresponding to the user input based on the use history information, and obtaining the recommended content information through the at least one artificial intelligence model based on data including the prompt into content recommendations of KIM.
Motivation to do so would be to include obtaining a prompt associated with a request for identification information for at least one content corresponding to the user input based on the use history information, and obtaining the recommended content information through the at least one artificial intelligence model based on data including the prompt to provide supplemental content to end users have not been meaningfully integrated with generative models (MANAVOGLU, paragraph [0004], line 9-11).
Regarding claim 3, KIM as modified by Bayer and MANAVOGLU teach all claimed limitations as set forth in rejection of claim 2, further teach: wherein the at least one processor is configured to: obtain the prompt including a preference list for a plurality of metadata types based on metadata included in the use history information (KIM, paragraph [0078], the memory may store information including viewing time, channel, and genre information of content viewed by the user for a certain period; also see paragraph [0091]-[0092], the broadcast signal obtained by the display may include audio and/or video and the additional data corresponding to the content; the additional data may be referred to as additional information or metadata; the additional data may include electronic program guide (EPG); the EPG information may include information about the title, type, subject, genre, etc.; in addition, when the user input as user input for channel selecting to the user interface, the controller may recognize the selected and viewed channel based on the user input; the controller may match and store “information about content including at least one of the content, the genre of the content, the channel, the genre of the channel or genre time information; in combination with the prompt including user profile taught by Bayer, it reads on as claimed).
As per claim 12, this claim is rejected on grounds corresponding to the same rationales given above for rejected claim 2 and are similarly rejected.
As per claim 13, this claim is rejected on grounds corresponding to the same rationales given above for rejected claim 3 and are similarly rejected.
Claims 4-6 and 14 are rejected under 35 U.S.C. 103 as being unpatentable over KIM et al. (U.S. Pub. No. 2020/0221179 A1) in view of Bayer et al. (U.S. Pub. No. 2017/0103135 A1), and MANAVOGLU et al. (U.S. Pub. No. 2024/0256757 A1), further in view of Livington et al. (CA 3143138 A1).
Regarding claim 4, KIM as modified by Bayer and MANAVOGLU teach all claimed limitations as set forth in rejection of claim 3, further teach: wherein the at least one processor is configured to: obtain the preference list including a plurality of keywords […] for the plurality of metadata types, and obtain the prompt including the preference list (KIM, paragraph [0078], the memory may store information including viewing time, channel, and genre information of content viewed by the user for a certain period; also see paragraph [0091]-[0092], the broadcast signal obtained by the display may include audio and/or video and the additional data corresponding to the content; the additional data may be referred to as additional information or metadata; the additional data may include electronic program guide (EPG); the EPG information may include information about the title, type, subject, genre, etc.; in addition, when the user input as user input for channel selecting to the user interface, the controller may recognize the selected and viewed channel based on the user input; the controller may match and store “information about content including at least one of the content, the genre of the content, the channel, the genre of the channel or genre time information; also see paragraph [0105], the viewing history information may be obtained by distinguishing a user input actively selecting content and a user input manually selecting content and applying different weight values; also see paragraph [0122]-[0123], genre may be classified into sports, movies, dramas, music, news, life styles, etc….; noted, “genre may be classified into sports, movies, dramas, music,…” is interpreted a plurality of keywords […] reference for the plurality of metadata types; also see paragraph [0127]-[0128], the recommendation list using a channel that provided content included in the same genre as the content that the user has view a lot as the recommendation channel based on the viewing history information; in combination with the prompt including user profile taught by Bayer, it reads on as claimed).
KIM as modified by Bayer and MANAVOGLU do not explicitly disclose: said keyword sorted in an order of preference.
Livington teaches: said keyword sorted in an order of preference (paragraph [109], the item of content may have one ranking foe one keyword and another ranking for another keyword; also see paragraph [001111], content item ID “68125” has keyword “mining” with keyword rank “6.44” and another keyword “gold” with keyword rank “3.18”, while content item ID 12648 with keyword “wireless sector” with keyword rank “8.98”; noted, keyword associated with the content with higher value, which is an indication of having more weight [preference] comparing to the lower rank value; in combination with the teaching of KIM, paragraph [0105], the viewing history information may be obtained by distinguishing a user input actively selecting content and a user input manually selecting content and applying different weight values; also see paragraph [0122]-[0123], genre may be classified into sports, movies, dramas, music, news, life styles, etc….; noted, “genre may be classified into sports, movies, dramas, music,…; also see paragraph [0128], regarding the viewing history information, when the user views sports content a lot, the controller may generates the recommendation list including at least one channel providing the sport content as the recommendation channel, noted, “sport” keyword is identified as the most preference [more weight/higher value] to the user based on user history than other keyword such as “drama”, “news”, etc. that being used for providing list of recommendations, the combination of KIM and Livington reads on as claimed).
said keyword sorted in an order of preference (paragraph [0222], for a content recommendation system that figured to recommend specific content in response to input from a client or user, the process of evaluating and ranking the content items relative to each other plays an important role in the overall recommendation
It would have been obvious to one of ordinary skill in art before the effective filing date of the claim invention to include said keyword sorted in an order of preference into content recommendations of KIM.
Motivation to do so would be to include said keyword sorted in an order of preference for assigning a plurality of ranks to content items for a plurality different keywords and a plurality of different categories (Livington ,paragraph [0012]).
Regarding claim 5, KIM as modified by Bayer, MANAVOGLU and Livington teach all claimed limitations as set forth in rejection of claim 4, further teach: wherein the at least one processor is configured to: obtain a plurality of content preferences for a plurality of viewing contents from an activity record on the plurality of viewing contents included in the use history information, obtain a plurality of metadata preferences divided by the plurality of metadata types based on the plurality of metadata types corresponding to the plurality of viewing contents and the plurality of content preferences (KIM, paragraph [0078], the memory may store information including viewing time, channel, and genre information of content viewed by the user for a certain period; also see paragraph [0091]-[0092], the broadcast signal obtained by the display may include audio and/or video and the additional data corresponding to the content; the additional data may be referred to as additional information or metadata; the additional data may include electronic program guide (EPG); the EPG information may include information about the title, type, subject, genre, etc.; in addition, when the user input as user input for channel selecting to the user interface, the controller may recognize the selected and viewed channel based on the user input; the controller may match and store “information about content including at least one of the content, the genre of the content, the channel, the genre of the channel or genre time information; also see paragraph [0105], the viewing history information may be obtained by distinguishing a user input actively selecting content and a user input manually selecting content and applying different weight values; also see paragraph [0122]-[0123], genre may be classified into sports, movies, dramas, music, news, life styles, etc….; also see paragraph [0127]-[0128], the recommendation list using a channel that provided content included in the same genre as the content that the user has view a lot as the recommendation channel based on the viewing history information; in combination with the prompt including user profile taught by Bayer, it reads on as claimed), and obtain the preference list including the plurality of keywords sorted in an order of the plurality of metadata preferences (Livington, paragraph [109], the item of content may have one ranking foe one keyword and another ranking for another keyword; also see paragraph [001111], content item ID “68125” has keyword “mining” with keyword rank “6.44” and another keyword “gold” with keyword rank “3.18”, while content item ID 12648 with keyword “wireless sector” with keyword rank “8.98”; noted, keyword associated with the content with higher value, which is an indication of having more weight [preference] comparing to the lower rank value; in combination with the teaching of KIM, paragraph [0105], the viewing history information may be obtained by distinguishing a user input actively selecting content and a user input manually selecting content and applying different weight values; also see paragraph [0122]-[0123], genre may be classified into sports, movies, dramas, music, news, life styles, etc….; noted, “genre may be classified into sports, movies, dramas, music,…; also see paragraph [0128], regarding the viewing history information, when the user views sports content a lot, the controller may generates the recommendation list including at least one channel providing the sport content as the recommendation channel, noted, “sport” keyword is identified as the most preference [more weight/higher value] to the user based on user history than other keyword such as “drama”, “news”, etc. that being used for providing list of recommendations, the combination of KIM and Livington reads on as claimed).
Regarding claim 6, KIM as modified by Bayer, MANAVOGLU and Livington teach all claimed limitations as set forth in rejection of claim 5, further teach: wherein the activity record includes a plurality of values divided by a plurality of activity types, and the at least one processor is configured to: obtain a plurality of weighted sums calculated for the plurality of viewing contents as the plurality of content preferences based on a plurality of weight values corresponding to the plurality of values and the plurality of activity types respectively (KIM, paragraph [0078], the memory may store information including viewing time, channel, and genre information of content viewed by the user for a certain period; also see paragraph [0091]-[0092], the broadcast signal obtained by the display may include audio and/or video and the additional data corresponding to the content; the additional data may be referred to as additional information or metadata; the additional data may include electronic program guide (EPG); the EPG information may include information about the title, type, subject, genre, etc.; in addition, when the user input as user input for channel selecting to the user interface, the controller may recognize the selected and viewed channel based on the user input; the controller may match and store “information about content including at least one of the content, the genre of the content, the channel, the genre of the channel or genre time information; also see paragraph [0105], the viewing history information may be obtained by distinguishing a user input actively selecting content and a user input manually selecting content and applying different weight values; also see paragraph [0122]-[0123], genre may be classified into sports, movies, dramas, music, news, life styles, etc….; also see paragraph [0127]-[0128], the recommendation list using a channel that provided content included in the same genre as the content that the user has view a lot as the recommendation channel based on the viewing history information; also see paragraph [0105], the viewing history information may be obtained by distinguishing a user input actively selecting content and a user input manually selecting content and applying different weight values; also see paragraph [0115]-[0116], the aggressiveness of the suer for viewing the content may be lower than the second type of input or the third type of input, therefore, the fourth weight may have a smaller value than the second weight or the third weight; also see paragraph [0128], regarding the viewing history information, when the user views sports content a lot, the controller may generates the recommendation list including at least one channel providing the sport content as the recommendation channel, noted, “sport” is identified as the most watch by the user based on user history than other genre such as “drama”, “news”, etc. therefore, it weight has more values [accumulating in comparison to other genre] and resulting in recommendation to the user; also see paragraph [0024]).
As per claim 14, this claim is rejected on grounds corresponding to the same rationales given above for rejected claim 4 and are similarly rejected.
Conclusion
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/KEN HOANG/Examiner, Art Unit 2168
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