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 .
Response to Arguments
Applicant’s arguments with respect to claim(s) 1, 3, 5 - 10 have been considered but are moot because applicant have amended the application to include new limitations therefore a new ground of rejection will be made which was necessitation by the amendments. Please see below.
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, 5 - 10 is/are rejected under 35 U.S.C. 103 as being unpatentable over Miyazaki (US Publication 2017/0046440) in view of Li (US Publication 2022/0019635).
Regarding claim 1, Miyazaki discloses a management system comprising: a storage medium storing computer-readable instructions (See paragraph [0262]); and one or more processors (See paragraph [0031]) connected to the storage medium (See paragraph [0262]), the processor executing the computer-readable instructions
Miyazaki fails to disclose the processor executing the computer-readable instructions to: manage information about a model for use in a system that recommends content to a user by inputting location information and preference information of the user to the model, and decide on a degree to which the preference information is reflected for each piece of the content when the recommended content is selected in the model; acquire first evaluation information of the user when the content is recommended and played at a first location and second evaluation information of the user when the same content is played at a second location; and update the preference information so that it is determined that the content for which not only the first evaluation information but also the second evaluation information are highly evaluated is a favorite content of the user.
Li discloses the processor executing the computer-readable instructions to: manage information about a model for use in a system that recommends content to a user by inputting location information and preference information of the user to the model (See paragraph [0014], mentions system learns location-based data and has personalized preferences), and decide on a degree to which the preference information is reflected for each piece of the content when the recommended content is selected in the model (See paragraph [0033] mentions weights according to their level of relevance).
acquire first evaluation information (S701 – S713) of the user when the content (thumbnail images) is recommended and played at a first location (S709 – a geographic location (first location – mall/store) is associated with the selected thumbnail image for digital content to be displayed on the application interface) and second evaluation information (S701 – S713) of the user when the same content (based on score of the digital content – ex. Number of likes, shares, viewings, etc.) is played at a second location (s709 – a second geographic location – school/work); and
update the preference information so that it is determined that the content for which not only the first evaluation information but also the second evaluation information are highly evaluated is a favorite content of the user (Paragraph [0033] – teaches that the machine-learning algorithm can be assigned with different weights according to their levels of relevance. The feature with higher relevance can be assigned with a higher weight, and the feature with lower relevance can be assigned with a lower weight; Paragraph [0039] - the weights that are calculated based on some reference indicators. The reference indicators include various activity records, such as the activities of the user changing the geographical location, browsing the digital contents and the time thereof. The reference indicators are referred to for the system to determine the geographic range corresponding to each of the thumbnail images shown on the linking point display area).
Therefore, it would have been obvious to a person having ordinary skills in the art before the effective filing date of the application to have used the teachings of having the processor executing the computer-readable instructions to: manage information about a model for use in a system that recommends content to a user by inputting location information and preference information of the user to the model, and decide on a degree to which the preference information is reflected for each piece of the content when the recommended content is selected in the model in Miyazaki’s invention as taught by Li’s invention.
The motivation for doing this would have been to allow the system to obtain the one or more digital contents that match the user preference (See paragraph [0014]).
Regarding claim 3, Miyazaki discloses a management system comprising: a storage medium storing computer-readable instructions; and one or more processors connected to the storage medium, the processor executing the computer-readable instructions.
Miyazaki fails to disclose the processor executing the computer-readable instructions to: manage information about a model for use in a system that recommends content to a user by inputting time period information and preference information to the model, and decide on a degree to which the preference information is reflected for each piece of the content when the recommended content is selected in the model; acquire first evaluation information of the user when the content is recommended and played at a first location and second evaluation information of the user when the same content is played at a second location; and update the preference information so that it is determined that the content for which not only the first evaluation information but also the second evaluation information are highly evaluated is a favorite content of the user.
Li discloses the processor executing the computer-readable instructions to: manage information about a model for use in a system that recommends content to a user by inputting time period information (See paragraph [0034]) and preference information to the model (See paragraph [0014], mentions system learns location-based data and has personalized preferences), and decide on a degree to which the preference information is reflected for each piece of the content when the recommended content is selected in the model (See paragraph [0033] mentions weights according to their level of relevance).
Therefore, it would have been obvious to a person having ordinary skills in the art before the effective filing date of the application to have used the teachings of having the processor executing the computer-readable instructions to: manage information about a model for use in a system that recommends content to a user by inputting time period information and preference information to the model, and decide on a degree to which the preference information is reflected for each piece of the content when the recommended content is selected in the model in Miyazaki’s invention as taught by Li’s invention.
The motivation for doing this would have been to allow the system to obtain the one or more digital contents that match the user preference (See paragraph [0014]).
Regarding claim 5, Li discloses wherein the processor updates the preference information on the basis of the comparison between the first evaluation information and the second evaluation information with respect to the content for which the first evaluation information is higher than a first reference (See paragraph [0033] mentions assigning weights to data features according to their relevance)
Regarding claim 6, Li discloses wherein the processor lowers the degree to which the preference information is reflected for the content when the second evaluation information is lower than a second reference (See paragraph [0033] mentions weights according to their level of relevance, even can assign lower weights depending on relevance).
Regarding claim 7, Li discloses wherein the processor raises the degree to which the preference information is reflected for the content when the second evaluation information is higher than a third reference (See paragraph [0033] mentions weights according to their level of relevance, even can assign higher weights depending on relevance).
Regarding claim 8, Li discloses wherein the processor updates the preference information on the basis of the comparison between the first evaluation information and the second evaluation information with respect to the content for which the first evaluation information is higher than a first reference (See paragraph [0033] mentions assigning weights to data features according to their relevance).
Regarding claim 9, Li discloses wherein the processor lowers the degree to which the preference information is reflected for the content when the second evaluation information is lower than a second reference (See paragraph [0033] mentions weights according to their level of relevance, even can assign lower weights depending on relevance).
Regarding claim 10, Li discloses wherein the processor raises the degree to which the preference information is reflected for the content when the second evaluation information is higher than a third reference (See paragraph [0033] mentions weights according to their level of relevance, even can assign higher weights depending on relevance).
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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/CAROLYN R EDWARDS/Supervisory Patent Examiner, Art Unit 2692