DETAILED ACTION
The following is a non-final office action upon examination of application number 18/772957.
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 .
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 3/26/2026 has been entered.
Response to Amendment
Claims 1, 8, and 15 have been amended.
Claims 1-20 are pending in the application and have been examined on the merits discussed 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.
Claims 1, 6, 8, 13, and 15 are rejected under 35 U.S.C. 103 as being unpatentable over US 2015/0339596 (Ioannidis); in view of US 2015/0026708 (Ahmed); in view of US 2015/0347823 (Monnerat).
As per claim 1, Ioannidis teaches: a computer-implemented method for optimal selection of a media file for members of a viewing group, including: receiving, by a device processor, one or more available content items for display; ([0050] …the application may interface with an online database of restaurants, movies, or other activities. Then, based on the information specified by the organizer, including but not limited to type of event, location, as well as which friends have been invited, the application makes suggestions selected from this database that best fit the interests of the group [0081] …The choices suggested by the system may be obtained from an online database, such as shown in step 2125).
determining … a member profile for each of the viewing members; ([0005] Fandango provides a service for single users to find movie ticketing information and buy tickets online. Users can login through the account registered by Fandango or their Facebook account. Fandango maintains a profile of the user and stores his purchase records. [0062] One embodiment of a method 700 for constructing a profile of a user. [Abstract] …The methods and apparatus allow a user profile to be constructed based on ratings that a user provides to items in a database. [0071] …The system also maintains a profile of users, including gender, occupation, activities (likes, comments), text status (tweets), for example. [0048] …logs in by providing her credentials, for example. In another example, she does not login, but provides contact information that uniquely identifies her, such as an email address, Facebook identification, phone number, etc.).
for each member profile, determining, by the device processor, a score for each of the one or more available content items; ([Abstract] …allow prediction of a rating that an individual with a particular user profile may give to a similar item in a database. [0013] …The method further comprises predicting a rating that the user would assign to each of the plurality of possible items in the database based on a profile of the user and on features of each of the plurality of possible items)
determining, by the device processor, a group score for each of the one or more available content items based on the score for each of the of one or more available content items determined for each member profile; ([Abstract] … predict the best choice for a group activity by considering the ratings of all users within a group…the system selects recommendations for the users, comprising elements such as movie title … The system sends notifications to the users. [0015] …The method also comprises steps of assigning a weight to the rating of each user for the plurality of possible items, and of determining a score for each of the plurality of possible items based on the assigned weights for each user and on ratings of the plurality of possible items from each user in the group of users. [0059] Each item in the filtered list may then receive a score, which is a weighted average of the ratings the users in the group give to the items)
selecting a predetermined number of content items from the one or more available content items based on the group score; and displaying, by the device processor, the selected content items … ([0064] …The prediction is based on a profile of the user and on features of each of the plurality of possible items. The method further comprises a step 930 for selecting an item from the plurality of possible items selected in step 910, based on the predicted ratings. [0066] Control is then passed to step 1150 for selecting an item from the plurality of possible items based on the determined score from step 1140. Control is then passed to step 1160 to send electronic notification to each user in the group of users identifying the selected item. [Abstract] … predict the best choice for a group activity by considering the ratings of all users within a group…the system selects recommendations for the users, comprising elements such as movie title … The system sends notifications to the users)
Although not explicitly taught by Ioannidis, Ahmed teaches: receiving, by the device processor, a request for streamed electronic content; ([Abstract] … media content--including, without limitation, movies, television programs, music, video games, and/or the like--may be presented to a user(s) via a presence detection device ("PDD"), and information regarding such presented media content may be monitored and sent to a server for analysis [0080] By analyzing what users are viewing, the various systems are able to determine what programs and television stations are of particular interest to particular users).
capturing data, by a camera, of viewing members to view the streamed electronic content, the data comprising one or more images and audio information; ([0054] …presence information can include raw image, video, or audio data [0094] According to some embodiments, inline cameras (which in some cases can be a stand-alone device or a device embodied in another suitable device including, but not limited to, a PDD as discussed above, or a video calling device, and/or the like) can also contain cameras, microphones, and other sensors. These sensors, in conjunction with the internal processing capability of the device allow the device to know when someone is in room. Additionally, the devices can also recognize who is in the room.)
analyzing, by the device processor, the captured data using facial and voice recognition algorithms to determine demographic information and member attributes for each of the viewing members, the voice recognition algorithms based on … each of the viewing members; [0054] … facial characteristics, and/or voice detection can be used to uniquely identify a person [0092] In some embodiments, the PDD may also interface with (or have integrated therein) a camera or other video/image capture device. The camera may be used to determine the number of people in the room, as well as the mood, gender, age, identity, etc., of each person. [0142] … gender of each audience member, age of each audience member, demographic group to which each audience member belongs (i.e., other than age or gender, which might include, without limitation, at least one of ethnicity, culture, language-proficiency, location, socio-economic grouping, income, political leanings, and/or the like) [Claim 27] …audience-based information is monitored using one or more of: facial recognition techniques; facial expression recognition techniques; mood recognition techniques; emotion recognition techniques; voice recognition techniques; vocal tone recognition techniques).
determining, by the device processor, a member profile of each of the viewing members based on the demographic information and member attributes ([0016] … cameras can determine not only that someone is in the room, but they can also determine who is in the room, using their sensors and facial recognition capability, or the like. This information allows for much more targeted type of advertising, because a particular user's profile and personal preferences can be used for advertising. [0051] … Additionally, through means such as facial recognition and voice detection, or the like, the devices also can automatically recognize who is in the room. More specifically, such devices can detect the presence of a particular individual. [0054] … facial characteristics, and/or voice detection can be used to uniquely identify a person [0092] In some embodiments, the PDD may also interface with (or have integrated therein) a camera or other video/image capture device. The camera may be used to determine the number of people in the room, as well as the mood, gender, age, identity, etc., of each person. As discussed above, these characteristics may be used to determine (or in some cases, optimize determinations of) advertisements. [0121] … Merely by way of example, the images might be analyzed with facial recognition software, which can be used to determine the number of people in the room with the inline camera and/or to identify any of such people (e.g., by determining a name, an age range, a gender, and/or or other identifying or demographic information about a user, based on the output of the facial recognition software)).
…the score based on analyzing data network traffic; ([0094] … determine whether advertising is effective. Inline cameras can use this to customize advertising for a particular user and to gauge the effectiveness of ads for advertisers. [0098] …This capability can be used to determine whether or not a user is watching or has watched a particular advertisement that is being displayed. This type of feedback is invaluable to advertisers, as it provides information that can be gathered and analyzed. The statistics can be given or sold back to advertisers who can then gauge the effectiveness of their ad [0123] …can collect and/or provide feedback to the advertiser (or another third party). Merely by way of example, the inline camera might capture audio and/or video while the advertisement is being displayed, which can indicate user reaction to the advertisement. This audio/video can be used to infer user acceptance (or rejection) of the advertisement [0167] Method 300 might include determining commonalities among the first through M.sup.th advertisements (block 380), determining differences in preferences of the first through N.sup.th users (block 385), and determining group demographics for the first through N.sup.th users (block 390). If there are conflicting determinations of advertisements, it may be determined which one or more of the first through M.sup.th advertisements would likely be accepted by, or relevant to, the first through N.sup.th users. For example, in some cases, likes, dislikes, or indifferences of each user with respect to particular advertisements or type of advertisements might be taken into account when determine which one or more of the first through M.sup.th advertisements would likely be accepted by, or relevant to, the first through N.sup.th users. Viewing patterns of each user (either alone or in particular groups with one or more of the present users of the group) might also be taken into account.)
displaying, by the device processor, the selected content items while streaming the electronic content ([0024] …presenting the at least one advertisement to the user might comprise presenting the at least one advertisement to the user by inserting the at least one advertisement into a video stream. In some cases, inserting the at least one advertisement into a video stream might comprise overlaying the video stream with the at least one advertisement [0117] … provide advertisements to be inserted in a video stream, can interact with advertisers to provide presence information and/or receive advertisements (and/or advertising instructions) from the advertisers [0122] …insert the advertisement directly into the video stream provided to the STB, based on presence information collected by the inline device…).
It would have been obvious, before the effective filing date of the claimed invention, for one of ordinary skill in the art to have modified the teachings of Ioannidis with the aforementioned teachings of Ahmed with the motivation of optimizing the identification of content/advertisements (Ahmed [0092]). Further, one of ordinary skill in the art would have recognized that applying the teachings of Ahmed to the system of Ioannidis would have yielded predictable results and doing so would have been recognized by those of ordinary skill in the art as resulting in an improved system that would allow for the use of cameras to identify users viewing content.
Although not explicitly taught by Ioannidis, Monnerat teaches: the voice recognition algorithms based at least on a pitch, intonation, or timbre …. ([0070] … may use speech recognition to find and analyze one or more characteristics of the person's voice. The speech recognition software may use audio characteristics such the frequency, pitch, tone, timbre, amplitude, duration, and the like. [0071] … characteristics such as the frequency, pitch, tone, timbre, amplitude, may be used).
It would have been obvious, before the effective filing date of the claimed invention, for one of ordinary skill in the art to have modified the teachings of Ioannidis with the aforementioned teachings of Monnerat with the motivation of analyzing characteristics of a person’s voice (Monnerat [0070]). Further, one of ordinary skill in the art would have recognized that applying the teachings of Monnerat to the system of Ioannidis would have yielded predictable results and doing so would have been recognized by those of ordinary skill in the art as resulting in an improved system that would allow for analysis of various audio characteristics.
As per claim 6, Ioannidis teaches: determining the group score based on a weighted average of the score for each of the one or more available content items for each member profile ([0015] …The method also comprises steps of assigning a weight to the rating of each user for the plurality of possible items, and of determining a score for each of the plurality of possible items based on the assigned weights for each user and on ratings of the plurality of possible items from each user in the group of users).
As per claim 8 and 15, these claims recite limitations substantially similar to those addressed by the rejection of claim 1, above; therefore, the same rejection applies.
As per claim 13, this claim recites limitations substantially similar to those addressed by the rejection of claim 6, above; therefore, the same rejection applies.
Claims 2, 9, and 16 are rejected under 35 U.S.C. 103 as being unpatentable over US 2015/0339596 (Ioannidis); in view of US 2015/0026708 (Ahmed); in view of US 2015/0347823 (Monnerat); in view of US 2014/0067828 (Archibong).
As per claim 2, although not explicitly taught by Ioannidis, Archibong teaches: determining, by the device processor, at least one of a first classification for each of the viewing members based on a corresponding viewing device for each of the viewing members ([0048] …environment 100 may include multiple users 101 [0213] At step 2220, a second display device of the user is determined. For example, social TV dongle 810 may determine that the user is interacting with a smartphone while viewing the content on the first display device. As another example, social TV dongle 810 may determine that the user is interacting with a tablet computer while viewing the content on the first display device. [0261] …where identities of a first and second user are determined. This may be accomplished by, for example, analyzing MAC addresses of the users' mobile devices).
It would have been obvious, before the effective filing date of the claimed invention, for one of ordinary skill in the art to have modified the teachings of Ioannidis with the aforementioned teachings of Archibong with the motivation of identifying content to provide/recommend to users (Archibong [Abstract]). Further, one of ordinary skill in the art would have recognized that applying the teachings of Archibong to the system of Ioannidis would have yielded predictable results and doing so would have been recognized by those of ordinary skill in the art as resulting in an improved system that would allow for the identification of users based on their devices.
As per claim 9 and 16, these claims recite limitations substantially similar to those addressed by the rejection of claim 2, above; therefore, the same rejection applies.
Claims 3, 4, 7, 10, 11, 14, 17, 18, and 20 are rejected under 35 U.S.C. 103 as being unpatentable over US 2015/0339596 (Ioannidis); in view of US 2015/0026708 (Ahmed); in view of US 2015/0347823 (Monnerat); in view of US 2014/0067828 (Archibong); in view of US 2015/0178781 (Luk).
As per claim 3, Ioannidis teaches: determining, by the device processor, an available identification mechanism for each of the corresponding viewing devices, wherein the available identification mechanism includes one of device usage ([0048] …logs in by providing her credentials, for example. In another example, she does not login, but provides contact information that uniquely identifies her, such as an email address, Facebook identification, phone number, etc.; identification through device usage as the user provides credentials through the device).
Although not explicitly taught by Ioannidis, Archibong teaches: wherein the available identification mechanism includes … facial recognition; wherein facial recognition includes determining member attributes based on facial recognition information ([0172] … For example, facial recognition or body-size recognition may be utilized to compare captured images of users 101 with images associated with users 101 on social networking system 160 (e.g., a profile picture). [0173] … a facial recognition process, in order to determine an identity of user 101. Any appropriate action may then be performed according to the identity of user 101. [0174] … The captured image may then be analyzed using facial recognition in order to determine the identity of user 101. [0175] … The captured image may then be analyzed using facial recognition in order to determine the identity of user 101. Once the identity of user 101 is determined, a customized social programming guide for the identified user may be presented; identification through facial recognition)
One of ordinary skill in the art would have recognized that applying the teachings of Archibong to the system of Ioannidis would have yielded predictable results and doing so would have been recognized by those of ordinary skill in the art as resulting in an improved system that would allow for the use of facial recognition for the identification of users.
Although not explicitly taught by Ioannidis, Luk teaches: wherein device usage includes determining i) a number of applications installed and ii) a usage frequency of the installed applications; and ([0065] In a third example, a correlation group may include data related to application usage. According to various embodiments, application usage data may include, but is not limited to: information describing applications installed on the device, information describing the genres of applications installed on the device, web usage statistics, native application usage statistics, and installed application usage statistics).
One of ordinary skill in the art would have recognized that applying the teachings of Luk to the system of Ioannidis would have yielded predictable results and doing so would have been recognized by those of ordinary skill in the art as resulting in an improved system that would allow for the collection of usage data related to applications on a device.
As per claim 4, Ioannidis teaches: wherein the member profile for each of the viewing members includes a gender ([0071] …The system connects to existing social networks, for example, Facebook, Google plus, Twitter or similar sites. The system also maintains a profile of users, including gender, occupation, activities (likes, comments), text status (tweets), for example).
Although not explicitly taught by Ioannidis, Archibong teaches: wherein the member profile for each of the viewing members includes a gender and an age group ([0054] … Demographic data typically includes data about the user, such as age, gender, location, etc., e.g., as included in the user's profile).
One of ordinary skill in the art would have recognized that applying the teachings of Archibong to the system of Ioannidis would have yielded predictable results and doing so would have been recognized by those of ordinary skill in the art as resulting in an improved system that would allow for the collection of profile information including age data.
As per claim 7, Ioannidis teaches: wherein determining the group score further includes factoring one of … a media file identifier ([0015] … The method comprises a step for selecting a plurality of possible items from a database. The method further comprises a step of determining a rating for each of the plurality of possible items from the database for each user in the group of users. The method also comprises steps of assigning a weight to the rating of each user for the plurality of possible items, and of determining a score for each of the plurality of possible items based on the assigned weights for each user and on ratings of the plurality of possible items from each user in the group of users; group score is determined based on a media identifier (unique vector for media item in database). [0056] In one embodiment, each item in the database is associated with a unique vector v describing the item's features). Examiner notes that the present claims do not set forth how the media file identifier is used in determining a group score; therefore, any involvement of a media file identifier in determining a group score satisfies the claimed feature.
Although not explicitly taught by Ioannidis, Archibong teaches: wherein determining the group score further includes factoring one of a geographic identifier ([0059] … The relevance and ranking engine 225 includes logic for calculating a relevance score for content objects (including both user-generated content objects and third-party content objects) relative to a user, for ranking the content objects by their relevance scores, and for selecting content objects for sending to users as notifications or as responses to user requests. To calculate the relevance score, the relevance and ranking engine 225 determines a location value by comparing the content object location and a current location for the user device 210 … Then, the relevance and ranking engine 225 combines the location value, interest value, connection value, and time value to determine the relevance score for the content object with respect to the user).
It would have been obvious, before the effective filing date of the claimed invention, for one of ordinary skill in the art to have modified the teachings of Ioannidis with the aforementioned teachings of Archibong with the motivation of scoring using location relevance (Archibong [0059]). Further, one of ordinary skill in the art would have recognized that applying the teachings of Archibong to the system of Ioannidis would have yielded predictable results and doing so would have been recognized by those of ordinary skill in the art as resulting in an improved system that would allow for the use of geographic location in determining a score.
As per claim 10 and 17, these claims recite limitations substantially similar to those addressed by the rejection of claim 3, above; therefore, the same rejection applies.
As per claim 11 and 18, these claims recite limitations substantially similar to those addressed by the rejection of claim 4, above; therefore, the same rejection applies.
As per claim 14 and 20, these claims recite limitations substantially similar to those addressed by the rejection of claim 7, above; therefore, the same rejection applies.
Response to Arguments
Applicant's arguments filed 2/26/2026 have been fully considered but they are not persuasive.
With respect to the rejection under 35 USC 103, Applicant argues that the art of record does not disclose the claimed features.
Examiner respectfully disagrees. The Applicant’s arguments are directed to newly amended features; additional search has been conducted and the rejection has been updated to address said amendments. See updated Claim Rejections - 35 USC § 103 above.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
US 2007/0198261 (Chen) – discloses a system that performs voice recognition and uses pitch to determine user features.
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/ALAN TORRICO-LOPEZ/Primary Examiner, Art Unit 3625
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