DETAILED ACTION
Claims 1-20 are pending.
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 .
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) 1-8, 10-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Shoemake et al. (IS 2014/0359647) in view of Lewis, II et al. (US 2014/0245336).
Claim 1, Shoemake teaches a method comprising:
interfacing a recommendation system (100) to a content management system (CMS) that stores instances of shared files and coordinates user interactions with the shared files (i.e. control server 110 or media content server 150), the user interactions being by and between a plurality of CMS users (i.e. user devices 105) (fig. 1; p. 0087);
identifying two or more address ranges (i.e. portions) that correspond to two or more logical portions of a particular single file (i.e. media content), the two or more address ranges defining locations within the particular single file (i.e. which portions or scenes the viewer consumed) (p. 0130-0132);
storing, onto a computer readable medium, a record of observed user device interactions of a first CMS user over the particular single file (i.e. monitored media on a user profile or control server), wherein at least some of the observed user device interactions correspond to the first CMS user’s interactions with a particular logical portion (i.e. viewing portion or scene) of the particular single file (p. 0087-0089, 0130-0132).
Shoemake is silent regarding a method comprising:
using the record of observed user device interactions to compute a fine-grained recommendation that refers to the particular logical portion of the particular single file; and
communicating, to at least some of the plurality of CMS users, the fine-grained recommendation, wherein the fine-grained recommendation refers to the particular logical portion of the particular single file as well as to the particular single file itself.
Lewis teaches a method comprising:
using the record of observed user device interactions to compute a fine-grained recommendation (i.e. favorite scenes) that refers to the particular logical portion of the particular single file (fig. 9; p 0069-0071); and
communicating, to at least some of the plurality of CMS users, the fine-grained recommendation (i.e. favorite scenes), wherein the fine-grained recommendation refers to the particular logical portion (i.e. scenes) of the particular single file as well as to the particular single file itself (i.e. movie e.g. Armageddon) (fig. 9; p 0069-0071).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the present invention to have provided scene recommendation as taught by Lewis to the system of Shoemake to provide specific favorite scenes to users (p. 0070).
Claim 2, Shoemake teaches the method of claim 1, further comprising:
determining a second CMS user (i.e. other user in group), wherein the second CMS user is associated with the first CMS user (i.e. same demographic group) (p. 0020, 0075); and
providing the fine-grained recommendation to the second CMS user (i.e. generating recommendation to multiple CRDs) (fig. 3; p. 0135-0139, 0173-0174).
Claim 4, Shoemake teaches the method of claim 2, wherein determining the second CMS user comprises comparing at least one attribute of the first CMS user to at least one attribute of the second CMS user (i.e. same demographic group) the at least one attribute of the first CMS user comprises an inferred interest (i.e. determining interest of users) (p. 0020, 0071, 0075).
Claim 5, Shoemake teaches the method of claim 3, wherein determining the second CMS user comprises comparing at least one attribute of the first CMS user to at least one attribute of the second CMS user (i.e. same demographic group) the at least one attribute of the first CMS user comprises one or more user interactions (i.e. monitoring reactions) (p. 0020, 0075, 0130, 0137).
Claim 6, Shoemake teaches the method of claim 2, wherein the first CMS user is associated with the second CMS based at least in part on at least one of, the first CMS user’s interactions or the second CMS user’s interactions (i.e. viewing patterns of each audience member) (p. 0075).
Claim 7, Shoemake teaches the method of claim 1, further comprising:
capturing user interaction events (i.e. selecting the recommended content) of the first CMS user in response to a notification (i.e. recommended content) (p. 0071-0074); and
using the user interaction events of the first CMS user’s response to the notification to train or adjust a machine learning model (i.e. all user interactions are monitored and included for determining trending content including selecting recommended content) (p. 0071-0074).
Claim 8, Shoemake teaches the method of claim 1, further comprising learning an interest of the first CMS user based on the first CMS user’s interactions over the particular portion of the particular single file (i.e. determining user interests) (p. 0131).
Claim 10, Shoemake teaches the method of claim 1, wherein the fine-grained recommendation is a suggestion to a second CMS user to access a particular location of the particular single file (i.e. reactions to a portion), and wherein the second CMS user is selected based on a commonality or association between the first CMS user and the second CMS user (i.e. group similarity) (p. 0117-0118).
Claim 11, Shoemake teaches the method of claim 10, wherein the fine-grained recommendation to the second CMS user is a topic-based recommendation to access the particular single file at a particular location (i.e. trending topic) (p. 0017).
Claim 12, Shoemake teaches the method of claim 11, wherein the topic-based recommendation to access a second single file (i.e. recommendation) at the particular location is based at least in part on one or more user attributes of the first user (i.e. customized to user) (p.0074).
Claim 13, Shoemake teaches the method of claim 1, wherein the fine-grained recommendation that refers to the particular logical portion of the particular single file is based at least in part on one or more user attributes of a second user (i.e. customized to a group of users) (p. 0074).
Claim 14, Shoemake teaches the method of claim 1, further comprising:
initiating an action on behalf of the first user (i.e. clicking link), wherein the action is based on the fine-grained recommendation (i.e. clicking link to recommended program) (figs. 6A-B; p. 0089, 0180-0182).
Claim 15, Shoemake teaches the method of claim 14, wherein the action on behalf of the first user is at least one of, an open operation, or a scrolling action (i.e. scrolling 615 to select link) (fig. 6A).
Claim 16, Shoemake teaches the method of claim 1, wherein particular portion corresponds to at least one of, a section heading, a passage, a chapter, or a timecode (i.e. portion of content) (p. 0020, 0075, 0130).
Claims 17 recites “A non-transitory computer readable medium having stored thereon a sequence of instructions which, when stored in memory and executed by one or more processors causes the one or more processors” to perform the step of claim 1.
Shoemake teaches “A non-transitory computer readable medium having stored thereon a sequence of instructions which, when stored in memory and executed by one or more processors causes the one or more processors” to perform the step of claim 1 (p. 0192).
Claims 18 recites “A non-transitory computer readable medium having stored thereon a sequence of instructions which, when stored in memory and executed by one or more processors causes the one or more processors” to perform the step of claim 2.
Shoemake teaches “A non-transitory computer readable medium having stored thereon a sequence of instructions which, when stored in memory and executed by one or more processors causes the one or more processors” to perform the step of claim 2 (p. 0192).
Claim 19 is analyzed and interpreted as an apparatus of claim 1.
Claim 20 is analyzed and interpreted as an apparatus of claim 2.
Claim(s) 3 is/are rejected under 35 U.S.C. 103 as being unpatentable over Shoemake et al. (IS 2014/0359647) in view of Lewis, II et al. (US 2014/0245336), and further in view of Chawla et al. (US 11451598).
Claim 3, Shoemake is silent regarding the method of claim 2, wherein using the record of observed user device interactions to compute a fine-grained recommendation that refers to the particular logical portion of the particular single file comprises using at least the record of observed user device interactions to train a machine learning model and using the trained machine learning model to generate the fine-grained recommendation.
Lewis teaches the specific feature of:
“wherein using the record of observed user device interactions to compute a fine-grained recommendation (i.e. favorite scenes) that refers to the particular logical portion of the particular single file” (i.e. movie e.g. Armageddon) (fig. 9; p 0069-0071).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the present invention to have provided scene recommendation as taught by Lewis to the system of Shoemake to provide specific favorite scenes to users (p. 0070).
Chawla teaches the specific feature of:
“using at least the record of observed user device interactions (i.e. collecting popularity data) to train a machine learning model and using the trained machine learning model to generate the fine-grained recommendation” (i.e. neural network) (col. 8-9, lines 53-12).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the present invention to have provided machine learning as taught by Chawla to the system of Shoemake to provide automatically generate preidctions of content (col. 8-9, lines 53-12).
Claim(s) 9 is/are rejected under 35 U.S.C. 103 as being unpatentable over Shoemake et al. (IS 2014/0359647) in view of Lewis, II et al. (US 2014/0245336), and further in view of Downing et al. (US 2015/0245084).
Claim 9, Shoemake is silent regarding the method of claim 8, wherein the first CMS user’s interactions over the portion of the particular single file is at least one of, a mouse hover, a rewind command, or a pause command.
Downing teaches the method of claim 8, wherein the first CMS user’s interactions over the portion of the particular single file is at least one of, a mouse hover (i.e. clicking information) (p. 0058) , a rewind command, or a pause command.
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the present invention to have provided clicking information as taught by Downing to the system of Shoemake to allow for a measure of user engagement (p. 0058)
Response to Arguments
Applicant’s arguments with respect to claim(s) 1-20 have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
Claim 1, Applicant argues that
"using the record of observed user device interactions to compute a fine-grained recommendation that refers to the particular logical portion of the particular single file;"
"communicating, to at least some of the plurality of CMS users, the fine-grained recommendation, wherein the fine-grained recommendation refers to the particular logical portion of the particular single file as well as to the particular single file itself"
Applicant respectfully asserts that Shoemake fails to disclose, teach, or suggest "using the record of observed user device interactions to compute a fine-grained recommendation that refers to the particular logical portion of the particular single file", at least because the Office Action at page 3 admits that Shoemake is "silent regarding" the above recited language. Instead, Applicant respectfully asserts that Shoemake only discloses a recommendation to a content object as a whole. However, the indicated language specifically recites a recommendation to a portion of a file, as opposed to a recommendation to a file generally.
In order to remedy the failings of Shoemake, the Office Action at page 3 cites to Lewis, However, Applicant respectfully asserts that Lewis, whether alone or in combination with Shoemake, also fails to disclose, teach, or suggest "using the record of observed user device interactions to compute a fine-grained recommendation that refers to the particular logical portion of the particular single file". In fact, Lewis fails to provide any disclosure of a file, let alone details with regard to any files or arrangements thereof with regard to a recommendation, or to the movies they discuss. In contrast, the present claims expressly recite that the fine-grained recommendation refers to the particular logical portion of the particular single file. Respectfully, there cannot be a disclosure, teaching, or suggestion of a specific relationship (reference to the particular logical portion) between a fine-grained recommendation and the particular single file.
The Office Action at page 3 cites to Lewis 11 [0069]-[0071] for allegedly disclosing, teaching, or suggesting the above recited language. However, the indicated passages, and the remainder of Lewis, fail to disclose, teach, or suggest a single file. Instead, the indicated passage discloses that scenes of a movie may be recommended to users but does not disclose any underlying relationship to any files. As it cannot as not a single file is discussed in Lewis. Instead, Lewis is only concerned with the concept of content, not how that content is stored and arranged.
Thus, Applicant respectfully asserts that Lewis, whether alone or in combination with Shoemake, also fails to disclose, teach, or suggest "using the record of observed user device interactions to compute a fine-grained recommendation that refers to the particular logical portion of the particular single file".
In Response:
The Examiner respectfully disagrees. Lewis clearly discloses programs with portions that have favorite scene tags. Lewis also discloses the tags are stored in a database. The Applicant argues that Lewis doesn’t explicitly disclose “a file” or “logical portion of a file”. One of ordinary skill in the art would recognize that the programs are stored on servers and then transmitted to users are “files”. The “movie” of Lewis is a file stored on a database transmitted to a user. A tagged scene is a logical portion of that file, a particular scene refers to a start and end time of the scene. Thus, Lewis clearly discloses both limitations:
"using the record of observed user device interactions to compute a fine-grained recommendation that refers to the particular logical portion of the particular single file;"
"communicating, to at least some of the plurality of CMS users, the fine-grained recommendation, wherein the fine-grained recommendation refers to the particular logical portion of the particular single file as well as to the particular single file itself"
Conclusion
Claims 1-20 are rejected.
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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MUSHFIKH I. ALAM
Primary Examiner
Art Unit 2426
/MUSHFIKH I ALAM/Primary Examiner, Art Unit 2426 5/28/2026
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