DETAILED ACTION
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 Amendment
The §101 rejections are withdrawn in view of the amendment.
Applicant’s arguments regarding the previous art rejections are moot in view of the new rejections below, which were necessitated by amendment.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1-5, 8, 12, and 14-20 are rejected under 35 U.S.C. 103 as being unpatentable over Choudhari et al., US 2021/0029391 in view of Soh et al., US 2024/0155178, further in view of Bekampis et al., US 2020/0204390.
Claims 1 and 14 and 18. Choudhari teaches a computer-implemented method comprising:
a processor and stored instructions [paras. 75-82[:
receiving, from a camera, information about a plurality of users within proximity to a media player [camera captures images of nearby users, Fig. 2A, paras. 30, 32, 36, 55, 57];
determining, based on the information, user profiles associated with the plurality of users, the user profiles each including media interests [individual viewer profile is created, Fig. 2H, paras. 39, 48, 49, 55, 57, 62-64, 113];
generating a group profile that includes the profiles [group profile is created, Fig. 2H, paras. 33, 39, 48, 49, 55, 113];
providing the group profile and a request a to determine an action [recommendations are “based on” the profile, i.e. a request for recommendations; user turning on the system causes a request for recommendations, Fig. 2B, para. 32] for one or more media items as input to a machine-learning model [machine-learning model accounts for group profile, Figs. 2B, 2D, 2D, 2H, paras. 34, 37, 47, 48, 55, 56];
outputting, with the machine-learning model, the one or more media items that satisfy the request based on the media interests and providing a recommendation that includes the one or more media items [Figs. 2B, 2D, 2H, paras. 33, 34, 37, 47, 48, 55, 56, 64].
Choudhari is silent on receiving data from a wireless device about users within proximity. Soh teaches an A/V streaming system including receiving, from a wireless device information about a plurality of users within proximity to a media player [PLS receives preference data for audio devices 314, Figs. 3, 4, paras. 44-47, 53, 54].
Before the effective filing date of the claimed invention, it would have been obvious to one skilled in the art to combine the references, allowing automatic identification of users without manual ID entry or selection of profiles. Usi8ng short range wireless allows the system to recommend only to users who are currently present while excluding any profiles not within wireless range [Choudhari also suggests the utility of wireless connections; paras. 92, 93].
The above references are silent on the profiles including differing viewing preferences in addition to media interests, and compromising between the different viewing preferences.
Bekampis teaches determining user profiles associated with the plurality of users, the user profiles each including media interests and viewing preferences [e.g. light and/or temperature paras. 9, 11, 12, 34, 40, 47],
generating a group profile wherein a different viewing preference of the viewing preferences is provided in at least two of the user profiles [group profile is generated, steps 225, 230, Fig. 2, e.g. using a weighted average of users present, paras. 13-15, 17, 18, 32, 48-50; each user may have differing preferences for lighting, etc., paras. 11, 14, 47];
generating an instruction to perform the action to improve a viewing experience [settings are applied to device, i.e. device is instructed to perform the setting action, step 230, Fig. 2, paras. 49, 50];
and an instruction to perform the action that reflects a compromise between the different viewing preference of the at least two user profiles [steps 225, 230, Fig. 2, e.g. using a weighted average of users present, paras. 13-15, 17, 18, 32, 48-50].
Before the effective filing date of the claimed invention, it would have been obvious to one skilled in the art to incorporate Bekampis, maximizing the satisfaction of the overall group by taking into account their differing preferences, and allowing a group to comfortably view content together in the same room.
2 and 15 and 19. Bekampis teaches the method of claim 1, wherein the viewing preferences relate to a visual viewing aspect of a room [e.g. light and/or temperature, paras. 9, 11, 12, 34, 40, 47], the method further comprising:
responsive to a user of the plurality of users selecting a media item from the one or more media items in the recommendation, instructing the media player to play a selected media item [para. 31, 34, 56, 57, 104]; and
transmitting the instructions to an internet-of-things device for the action to be performed [settings (instructions) are applied, step 230, Fig. 2, paras. 49, 50].
3 and 16 and 20. Bekampis teaches the method of claim 2, wherein: the action is selected from a group of reducing outside light in the room, reducing inside light in the room [e.g. lowering the lights, para. 31].
4 and 17. Soh teaches the method of claim 2, wherein the user is associated with an auditory device [paras. 44-51], the method further comprising: while the selected media is playing and the selected media is in a different language from a user profile language associated with the user profile, translating words from the selected media from the different language to the user profile language; and transmitting the translated words to the auditory device associated with the user [sending a different language version amounts to translating; paras. 38, 48, 51,60, 62].
5. Choudhari teaches the method of claim 1, wherein the user profiles include ranked media interests and the machine-learning model outputs the one or more media items based on selecting top-ranked media interests from the user profiles [media content preferences/historical data (high-ranked media) from profile is used to recommend/output content, paras. 39, 48, 49, 55, 57, 62-64, 113].
8. Soh teaches the method of claim 1, wherein: the wireless device includes a transmitter and a receiver for a wireless protocol selected from a group of Wi-Fi [para. 45], Bluetooth, Radio Frequency Identification, Near Field Communication, wireless mesh, and combinations thereof; and
determining, from the information, a user profile associated with a user includes:
detecting, with the wireless device, that an auditory device or a mobile device associated with the user is within proximity of the media player [detecting Bluetooth connection requires proximity, paras. 44, 45, 63], the auditory device being selected from a group of hearing aids, earbuds, headphones [paras. 45, 48], and combinations thereof;
receiving, with the wireless protocol, the information about the user [e.g. receiving preferences, paras. 46, 53, 54, 56];
extracting an identifier from the information [device ID, Fig. 3, para. 47]; and
identifying a match between the identifier and the user profile [device ID is matched with preferences/profile, Fig. 3, paras. 47, 64, 65].
12. Choudhari teaches the method of claim 1, further comprising: receiving feedback about the recommendation; and modifying the group profile based on the feedback [feedback is used to adjust recommendations, paras. 34, 35, 47, 59, 67].
Claim 6 is rejected under 35 U.S.C. 103 as being unpatentable over Choudhari and Soh and Bekampis as cited above in view of Nishimura, US 2022/0150582.
6. The above references are silent on a questionnaire. Nishimura teaches a recommendation system comprising: registering a user by: providing a questionnaire that includes a request for the media interests and viewing preferences; and generating a user profile that includes the media interests and viewing preferences based on answers from the user [user is presented with questionnaire, i.e. prompted to enter interests, to create profile, para. 53].
It would have been obvious to one skilled in the art before the effective filing date of the claimed invention to incorporate Nishimura, in order to create an accurate profile in the absence of viewing history for a particular user. Profile data generated by the user themselves may be the most reliable method of determining interests.
Claim 7 is rejected under 35 U.S.C. 103 as being unpatentable over Choudhari and Soh and Bekampis as cited above in view of Zhang et al., US 2022/0295144.
7. The above references are silent on a radar system to detect breathing. Zhang teaches a system wherein the wireless device is a radar system and determining a user profile associated with a user includes: determining a breathing pattern of the user; and determining the user profile based on the breathing pattern of the user [RF sensing is used to sense breathing patterns to identify a user; identity is part of a user profile, para. 46].
Before the effective filing date of the claimed invention, it would have been obvious to one skilled in the art to combine the references, using breathing as an identifier since each person in an audience will have a unique breathing pattern, allowing differentiation between audience members. Breathing can also be used as a proxy for a particular individual’s interest in the current content.
Claim 9 is rejected under 35 U.S.C. 103 as being unpatentable over Choudhari and Soh and Bekampis as cited above in view of Christakopoulou et al., US 2022/0394336.
9. Choudhari teaches a machine learning model [Figs. 2B, 2D, 2D, 2H, paras. 34, 37, 47, 48, 55, 56], but the above references are silent on an age-based recommendation. Christakopoulou teaches a method wherein a user of the plurality of users is less than eighteen years old and the one or more media items output are selected based on the user being less than eighteen years old [recommendations based on whether a minor is present in the group, para. 60].
Before the effective filing date of the claimed invention, it would have been obvious to one skilled in the art to combine the references, thereby avoiding the display of mature content to minors, allowing the recommendation system to be used in a family or large group setting.
Claim 10 is rejected under 35 U.S.C. 103 as being unpatentable over Choudhari and Soh and Bekampis as cited above in view of Daymond et al., US 2004/0019691.
10. The above references are silent on logging in to services via profile. Daymond teaches a set top box system comprising: responsive to determining the user profiles, logging a user into one or more services provided by the media player based on the user profile [user profiles store login data in order to access and activate services (i.e. login to services), para. 86].
Before the effective filing date of the claimed invention, it would have been obvious to one skilled in the art to combine the references, allowing the user convenient access to any third-party services, without having to manually enter login information.
Claims 11 and 13 are rejected under 35 U.S.C. 103 as being unpatentable over Choudhari and Soh and Bekampis as cited above in view of Glesinger et al., US 2024/0104305.
11 and 13. The above references are silent on using a LLM to process a query combining viewing history, interests, etc. Glesinger teaches a method wherein the machine-learning model includes a query engine [e.g. adaptive recommendations function, Fig. 1, paras. 24, 40, 58, and a large language model [paras. 96, 260], the method further comprising:
providing the media interests [e.g. interests, preferences, Table 1 (p.7), paras. 38, 85, 94, 100, 108, 117], a viewing history [user-viewed objects, history, Table 1 (p. 7), paras. 36, 38, 176, 179, 231], and a search request from a user that describes features of a media item to the query engine [Figs. 1, 2C, 5A, 8, 15, paras. 41, 96, 100, 315];
combining the search request, the media interests, the viewing history, and a template to form a query and providing the query as input to the large language model [MTAV is a combination of tracked user behavior and usage parameters; MTAV comprising structural, usage, and content aspect is input to adaptive recommendations function (LLM) as a query, Figs. 1, 2C-2C, 8, paras. 27, 57, 58, 62, 96, 97, 100-103, 109, 166-168, 177, 260];
outputting, with the large language model, the media item that corresponds to query [media items or recommendations of media items are output to user, Figs. 1, 5A, 6, paras. 24, 25, 27, 41, 69, 96, 100, 177].
Before the effective filing date of the claimed invention, it would have been obvious to one skilled in the art to incorporate an LLM to implement recommendations as taught in Choudhari, since an LLM can consider a large number of inputs [as in Table 1, p. 7 of Glesinger] and identify patterns that may not be apparent using conventional recommendation algorithms. An LLM having natural language input/output can also be trained in other languages to improve accessibility.
Glesinger teaches providing the media interests, a viewing history, and a search request from a user that describes features of a media item to the query engine as described above, but does not teach combining the profiles of each user.
Bekampis teaches determining user profiles associated with a plurality of users and generating a group profile that combines the content of each user’s profile [Fig. 2, paras. 13-15, 17, 18, 32, 48-50]. The language added by amendment, “providing the [profiles of] each of the users to the query engine” is therefore met by the combination of references. Note that the amendment is supported by the Specification [paras. 94 and 95], which does not explicitly state that each user provides a search request, but supports the concept of combining the search requests of each user as recited.
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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/TIMOTHY R NEWLIN/Examiner, Art Unit 2424
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