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 .
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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 2-3-2025 has been entered.
Response to Arguments
Applicant’s arguments, see pgs. 10-12, filed 10-28-2025, with respect to the 112 rejection(s) have been fully considered and are persuasive. Therefore, the rejection has been withdrawn.
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 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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 18, 21, 29, 40, 42, 50, 54-57, 63-64, 67-71, 73-79 are rejected under 35 U.S.C. 103 as being unpatentable over Fishman et al (2012/0059825) in view of LaFreniere et al (US 2011/0126251 A1) and Angiolillo et al (US 20090007179 A1), Kent JR. et al (US 20100131987 A1) and further in view of Jeon et al (US 20090055385 A1)
Claim 29. Fishman disclose a method comprising:
prior to a scheduled time of an episode the historical social media events (See e.g. [0015] on obtaining content-related information/event such as currently viewed, being recorded, scheduled, etc. See [0032]-[0033] on identifying those content items that appears to be of heightened interest to viewers based on…the number of times the video program has been reference in microblogs or on-line social network news. Examiner note: video program that has been referenced in social media indicated social media events occurred in the past), a user device schedule to an episode recording (See e.g. [0014] on determining a list of popular content items based on collected information; See e.g. [0015] on obtaining content-related information/event such as currently viewed, being recorded, scheduled, etc.);
receiving information indicating a plurality of social media events occurring during the scheduled time of the episode (See e.g. [0015] on obtaining content-related information/event such as currently viewed, being recorded, scheduled, etc. See [0032]-[0033] on identifying those content items that appears to be of heightened interest to viewers based on…the number of times the video program has been reference in microblogs or on-line social network news);
generating, based on the plurality of social media events occurring during the scheduled time of the episode, a plurality of input scores for the episode, wherein each input score corresponds to a different type of social media eventcurrently viewed, being recorded, scheduled, etc. See [0032]-[0033] on identifying those content items that appears to be of heightened interest to viewers based on…the number of times the video program has been reference in microblogs or on-line social network news. [0033] Content-related data, which may include viewership information, changes in viewership (e.g., a sudden spike in the number of users trending about a video program or a dramatic increase in the number of viewers watching or recording a video program), ratings of content, references to content items in on-line publications, rental and purchasing information, etc., may be processed by the analytics module 314 to identify those content items that appear to be of heightened interest to viewers. An indication of the heightened interest (also referred to as popularity) may be expressed in terms of a popularity value, which may be calculated for a content item (e.g., a video program) based on, cumulatively, the total number of viewers currently watching or recording the video program being above a predetermined threshold value, the total number of viewers currently watching or recording the video program having increased by a certain percent as compared to the earlier measurement, the number of times the video program has been referenced in microblogs or on-line social network news feeds, etc. The recommendation engine 310 may be configured to generate a list of popular content items, where a popular item is associated with a popularity value above a certain threshold value, customize the lists respectively for viewers associated with viewer devices 340 and 350, and provide the customized lists to the viewer devices 340 and 350. Customization process is described in further detail with reference to FIG. 4 below. Examiner Note: popularity value of a content reads on “input scores” since input score is not further defined in any shape or form. The broadest reasonable interpretation of “input score” is any score/value that is inputted/used. Also note: the inputted score is not further defined to indicate what is it being inputted to. [0038] In one embodiment, a customized playlist is generated by generating a score for each item from the list of popular content items and including items into in the customized playlist based on respective scores of the items from the list of popular content items. The scoring may be based on the viewer's preferences identified in the viewer's profile, based on data from the viewer's personal bucket and the viewer's social bucket. A content item from a category that is not indicated in the viewer's profile as being of interest to the viewer and that is not considered as being of interest to the viewer based on the viewing history of the viewer may still be assigned a high score by the customization module 440 based on the information from the viewers social bucket. For example, the customization module 440 may be configured to weigh heavily an indication that a certain content item is of high interest to a great number of the viewer's social contacts. [0017] In one example embodiment, in addition to determining a personalized hot list of content items, a smart playlist system may trigger recording of a certain program as soon as the program has been identified as a live program and of high relevance to the viewer. For example, a viewer may not be tuned into a channel broadcasting a particular live sports event. If the smart playlist system determined that the live sports event is of high relevance to the viewer, the smart playlist system may trigger the recording of the live broadcast of the sports event on the viewer's client device (e.g., a set top box, a desktop computer, etc.) and also alerts the user to the fact that she may be interested in the event being currently broadcast on a certain channel. The viewer may then ignore the alert. If the viewer, instead, tunes to the suggested channel the viewer would not have missed the beginning of the broadcast because the recording of the program has been automatically triggered by an instruction provided to the viewer's client device from the smart playlist system. In one example, the high relevancy of the live broadcast may have been determined based on the fact that all of the viewer's social network contacts have either tuned into the associated channel or have scheduled the recording of the broadcast. In another example, the high relevancy of the live broadcast may have been determined based on the viewer's profile or on the viewer's viewing history. An example smart playlist system may be implemented within architecture illustrated in FIG. 1. Examiner Note: social network contacts “tuned in” and “scheduled” are different social network events).
causing, based on the plurality of input score, the user device to schedule a second recording (See e.g. [0014] on determining a list of popular content items based on collected information; See e.g. [0015] on obtaining content-related information/event such as currently viewed, being recorded, scheduled, etc. [0017] In one example embodiment, in addition to determining a personalized hot list of content items, a smart playlist system may trigger recording of a certain program as soon as the program has been identified as a live program and of high relevance to the viewer. For example, a viewer may not be tuned into a channel broadcasting a particular live sports event. If the smart playlist system determined that the live sports event is of high relevance to the viewer, the smart playlist system may trigger the recording of the live broadcast of the sports event on the viewer's client device (e.g., a set top box, a desktop computer, etc.) and also alerts the user to the fact that she may be interested in the event being currently broadcast on a certain channel. The viewer may then ignore the alert. If the viewer, instead, tunes to the suggested channel the viewer would not have missed the beginning of the broadcast because the recording of the program has been automatically triggered by an instruction provided to the viewer's client device from the smart playlist system. In one example, the high relevancy of the live broadcast may have been determined based on the fact that all of the viewer's social network contacts have either tuned into the associated channel or have scheduled the recording of the broadcast. In another example, the high relevancy of the live broadcast may have been determined based on the viewer's profile or on the viewer's viewing history. An example smart playlist system may be implemented within architecture illustrated in FIG. 1.Examiner Note: “second recording” is not further defined, reads on any second recording under broadest reasonable interpretation. That include recording of existing or new program/content based on viewing history).
While Fishman disclose trigger recording on new program based on viewing history (which could include a series of episodes including the episode and subsequent episode), Fishman fails to explicitly disclose a subsequent episode of the series of episodes (in the event that “second” recording” means a subsequent episode of the series of episodes).
LaFreniere disclose set-top box (including PCR) and using social network recommendation (See e.g. abstract, [0019]-[0021])(thereby in the same field of endeavor), and explicitly disclose using past episodes view status to record latest/missing/subsequent episodes of a program (See [0065]).
Specifically, LaFreniere disclose prior to a scheduled time of an episode of a series of episodes, causing, by one or more computing devices
causing, based on the plurality of that a contact has viewed all episodes of season two of Lost except for episode five. The user may automatically be informed of the status when the desired episode is airing or the contact may manually send a request for the content to be recorded. See also [0065] Many content providers may allow such clipping as a way of enabling buzz or Internet marketing to build. The portions of media content may be posted to a social network or sent directly to one or more contacts. The recording options of section 510 may also allow a user to specify programs or content to be recorded from a remote location on an associated home DVR. A user may display on a home page of one or more social networks currently recorded content or a list of upcoming recording selections);
Angiolillo disclose set-top box (including PCR) and using social network for program sorting (See e.g. abstract, [0019]-[0024]), broadcasting information and recording option including previous and next episode ([0037]-[0038]), rating for each episode ([0033]))(thereby in the same field of endeavor).
Specifically, Angiolillo disclose causing, based on the plurality of input score, the user device to schedule a recording of a subsequent episode of the series of episodes (See e.g. [0037]-[0038]. [0037] FIG. 4B depicts a Broadcast Information screen 440 for the user to view the program data associated with the selected Broadcast Information option from the program information menu 430, in accordance with an exemplary embodiment of the present disclosure. The Broadcast Information screen 440 may include a variety of information for the user to view. These may include when the show or program was first aired, when the show or program was previously aired (if not the same as the first airing), any future air times, next sequential episodes, previous episodes, episode/program blocking features, recording features, and other broadcasting information. [0038]…In addition, the "Broadcast Information" screen 440 may indicate that the next sequential episode of U.S. Idol is #99 and that the previous episode was #97. In one embodiment, the next sequential episode may also include a Record option for recording, for example, on a personal video recorder (PVR) or digital video recorder (DVR), when it is schedule to air. [0019]… In another embodiment, the server 136 may also include additional processing logic to sort and index the one or more recorded and stored program data by at least one of the following: program information, broadcast (or rebroadcast) information, reviews, social network information, event/show statistics, category, advertisement information, initial broadcast time, ratings, popularity, closed-captioning information, etc.).
Kent JR. disclose causing, based on the plurality of input score, the user device to change the episode recording to series recording. ([0021] After selection of a particular season premier, pilot, or premier of a series program, recorded media 132 associated with the selected program may be presented at the display device 106. After presentation of at least a portion of the recorded media 132 associated with the program, the GUI module 130 may present an additional user interface display. For example, when a user stops playback of the recorded media 132 associated with the program, the user may be presented with a user interface display that includes a selectable option to control the recording of future episodes of the particular program. In a particular illustrative embodiment, the user interface display includes a selectable record future episodes option. The record future episodes option may cause future episodes of the particular series programs to be scheduled for recording by the scheduling module 122. To illustrate, after having watched at least a portion of the pilot, season premier or premier episode of a particular series program, the user may determine that he or she enjoys the program and may select the record future episodes option in order to ensure that future episodes of the particular series program are recorded. The record future episodes option may be associated with other user configuration settings 134 such as do not record reruns, record programs during a particular time slot, options for how long the program is stored at the recorded media 132, or other recording options. [0027] After an initial episode of a series program has been recorded, the method may include, at 232, generating a graphical user interface (GUI) including information about recorded programs. The information about the recorded programs may include information distinguishing the recorded initial episode in the GUI as an initial episode of a series program. For example, the information identifying the recorded initial episode may be highlighted, may be associated with the flag 229, or may be associated with a separate menu or list of programs to indicate that it is an initial episode of a series program. The method may also include, at 234, presenting a display, including at least a portion of the recorded initial episode. After presentation of at least a portion of the recorded initial episode, the method may include, at 236, presenting a GUI including a selectable option to record future episodes of the particular series program. For example, after presenting at least a portion of the recorded initial episode, the user may be presented with a GUI and given the option to record future episodes of the same series. In a particular embodiment, after presenting at least a portion of the recorded initial episode, the method includes, at 238, presenting a GUI including a selectable option to blacklist future episode of the particular series program. Blacklisting future episodes may prohibit the future episodes of the series from being recorded at the media recording device.).
The artisan of ordinary skill, starting with the method of determining popularity of content (including historical social media reference) and triggering of recording of Fishman, would have appreciated the benefit of recoding latest/missing/subsequent episode based on past viewing history of other episodes as proposed by LaFreniere, Angiolillo and Kent JR. The ordinarily-skilled artisan would readily see the benefits of providing relevant contents to the user, which would provide the well-known, predictable, and expected results of personalized entertainment recommendation. The artisan of ordinary skill would have been motivated to combine Fishman with LaFreniere, Angiolillo and Kent JR, as proposed above, at least because both are directed to a popularity of entertainment items.
Therefore, a person having ordinary skill in the art before the effective filing date of the invention would have found it obvious to combine the method of determining popularity of content of Fishman with the recoding subsequent episode of Angiolillo together with schedule a recording during the episode of LaFrenier and the change to series recording of Kent JR to achieve the well-known and expected benefit of personalized entertainment/hotlist recommendation.
Jeon disclose media-based recommendation (See [0034])(thereby in the same field of endeavor). Specifically, Jeon disclose training, based on historical social media events corresponding to a plurality of series of episodes, a machine learning model configured to determine popularities for episodes ([0029] Recommendations can be made, for example, using a recommendation engine as part of a computer-based system, such as a system using central servers to provide a number of different services, such as search, maps, shopping, and other such services. Two example categories of approaches can include "collaborative filtering" and "content-based recommendation." Collaborative filtering can also be referred to as "behavioral data-based recommendation" because it uses input from many users to "train" the recommendation engine. The content-based recommendation approach generally involves analyzing the content itself to determine similarity of items. [0034] One model used for the content-based recommendation approach is to make recommendations based on a combination of genre data (e.g., from an electronic program guide (EPG) provider) and ratings-based popularity data (e.g., for stations and/or programs). The model can use genre field values to find sets of similar programs. The sets may then be ranked by popularity of the station (e.g., where the program airs) or popularity of program itself (e.g., if such data is available). Programs that are series may be treated either at the series level or the episode level, or both. For example, when a new episode for an existing series airs, it may be assigned popularity data reflective of the series as a whole, but the data may transition over time into a reflection of the popularity of the episode, or some blend of the episode and the series (particularly when it is difficult to separate actions by users that indicate popularity of a series from popularity of an episode). Also, programs, such as in the form of episodes, may be organized into common clusters other than a group of episodes in a series. Moreover, although relatively implicit indications of popularity (e.g., clicking and ratings) are discussed here, more explicit indications, such as "5 star" ratings systems or other user ratings may be used. [0036] A third model used for the content-based recommendation approach is to apply extra data to a filter process to produce better clustering. For example, a system may apply machine learning techniques to analyze the content of a document to determine one or more concepts of the document, in order to be able to locate a relevant document. In a media search setting, such analysis may be performed on various data relating to a program. Such extra data may include, for example, closed caption data, blogs, or some web site content that extensively describes the program. In this sense, this third model is an extension of the second model. One advantage of using this model is that it can offset the lack of program description available in the source data from most EPG data providers. The lack of adequate descriptions can negatively affect the quality of filter-based recommendations because the filter performs clustering using keywords (e.g., using keywords available from program descriptions). Using keywords derived from closed captions, blogs, or web sites can improve recommendations when program descriptions are inadequate or unavailable. [0044] Profiles can be generated for a user and updated over time based on input explicitly provided by the user and selections made by the user (e.g., web sites visited, user clicks on those web sites, etc.). Profiles may be, for example, maintained in social networking or other sites. If a user subsequently enters a search term that in some way is related to user profile information, the recommendation engine can automatically combine the information to generate recommendations that may interest the user. [0096] Over time, user activity on the TV client 302 collected by the TV front end 304 (via arrow 7) may be provided to the user click history 306 via arrow 8. Such user activity may include user actions that can be used later, for example, in formulating recommendations. For example, the user actions may include inputs by the user on the Internet that relate to particular TV programs or stations. In this way, the recommendations can be based, at least in part, on the collaborative filtering approach described above or other similar approaches. Moreover, when more than one user is involved, recommendations using the collaborative filtering approach are "behavioral data-based recommendations" because they can use input from many users to "train" the recommendation engine.);
The artisan of ordinary skill, starting with the method of determining popularity of content and triggering of recording of Fishman, would have appreciated the benefit of using machine learning to determine popularities of episodes based on past viewing history of other episodes, training, based on historical social media events corresponding to a plurality of series of episodes, a machine learning model configured to determine popularities for episodes as proposed by Jeon. The ordinarily-skilled artisan would readily see the benefits of using machine learning model (trained) to provide relevant contents to the user, which would provide the well-known, predictable, and expected results of personalized entertainment recommendation. The artisan of ordinary skill would have been motivated to combine Fishman with LaFreniere, Angiolillo and Jeon, as proposed above, at least because both are directed to a popularity of entertainment items.
Therefore, a person having ordinary skill in the art before the effective filing date of the invention would have found it obvious to combine the method of determining popularity of content of Fishman with the machine learning of Jeon and the recoding subsequent episode of Angiolillo together with schedule a recording during the episode of LaFrenier to achieve the well-known and expected benefit of personalized entertainment/hotlist recommendation.
Furthermore, recording of past/current/subsequent/another/future episode of the series are non functional descriptive materials. The method of scheduling remains the same regardless of recoding choice. That is, the recording of past/current/subsequent/another/future episode of the series does not functionally change the schedule recording method of claim 29.
In re Curry, the Board held that in a computer-implemented method of providing "wellness-related services," "the 'wellness-related data in the databases.., does not functionally change either the data storage system or communication system used in the method of claim 81. Nonfunctional descriptive material cannot render nonobvious an invention that would have otherwise been obvious." See Ex parte Curry, 84 USPQ2d 1272 (BPAI 2005), aff'd (Fed. Cir. Appeal No. 2006-1003, aff'd Rule 36 June 12, 2006) MPEP 2106.01.
In re John, the Board held that the descriptive material (i.e., "control information" and "request" comprising a description of a development environment) recited in claim 1 is non-functional descriptive material because each of the "control information" and "request" does not functionally affect the process of managing a development environment. Rather, the control information is merely information that is used for "managing said first request" by a computer program and the request is data that is received ("receiving a first request") and processed ("processing said first request") by the system. In each case, the data (i.e., "control information" and "request") do not affect how the method of the prior art is performed on a computer system. In other words, the method of receiving and processing the request and reviewing the request "in accordance with control information" is carried out in the same way regardless of the nature of the request or control information. See Ex parte John F. Bisceglia, Appeal 2007-3447.
Claim 18. Kent JR disclose the method of claim 29, further comprising: causing the user device to output an indication of a second scheduled time that a subsequent episode, of the series of episode, is to be sent to the user device ([0021] After selection of a particular season premier, pilot, or premier of a series program, recorded media 132 associated with the selected program may be presented at the display device 106. After presentation of at least a portion of the recorded media 132 associated with the program, the GUI module 130 may present an additional user interface display. For example, when a user stops playback of the recorded media 132 associated with the program, the user may be presented with a user interface display that includes a selectable option to control the recording of future episodes of the particular program. In a particular illustrative embodiment, the user interface display includes a selectable record future episodes option. The record future episodes option may cause future episodes of the particular series programs to be scheduled for recording by the scheduling module 122. To illustrate, after having watched at least a portion of the pilot, season premier or premier episode of a particular series program, the user may determine that he or she enjoys the program and may select the record future episodes option in order to ensure that future episodes of the particular series program are recorded. The record future episodes option may be associated with other user configuration settings 134 such as do not record reruns, record programs during a particular time slot, options for how long the program is stored at the recorded media 132, or other recording options. [0027] After an initial episode of a series program has been recorded, the method may include, at 232, generating a graphical user interface (GUI) including information about recorded programs. The information about the recorded programs may include information distinguishing the recorded initial episode in the GUI as an initial episode of a series program. For example, the information identifying the recorded initial episode may be highlighted, may be associated with the flag 229, or may be associated with a separate menu or list of programs to indicate that it is an initial episode of a series program. The method may also include, at 234, presenting a display, including at least a portion of the recorded initial episode. After presentation of at least a portion of the recorded initial episode, the method may include, at 236, presenting a GUI including a selectable option to record future episodes of the particular series program. For example, after presenting at least a portion of the recorded initial episode, the user may be presented with a GUI and given the option to record future episodes of the same series. In a particular embodiment, after presenting at least a portion of the recorded initial episode, the method includes, at 238, presenting a GUI including a selectable option to blacklist future episode of the particular series program. Blacklisting future episodes may prohibit the future episodes of the series from being recorded at the media recording device).
Claim 21. LaFreniere disclose the method of claim 29, wherein causing the user device to schedule the second recording is further based on information indicating that a plurality of other user devices also outputted the episode during the schedule time. (See e.g. [0065] on recoding all of a portion during viewing based on social network currently recording (i.e. other user devices)).
Kent JR disclose causing the user device to change to episode recording (See e.g. [0021] and [0027] on recording future episodes of the particular program).
The combined teaching discloses the method of claim 29, wherein causing the user device to change to episode recording is further based on information indicating that a plurality of other user devices also outputted the episode during the schedule time.
Claim 40. LaFreniere disclose the method of claim 29, wherein the causing the user device to schedule the second recording of the another episode is based on a change in a count of scheduled recordings of the series of episodes (See e.g. [0065] on upcoming recoding selections. Examiner Note: it implied a change in count of scheduled recordings).
Kent JR disclose causing the user device to change to episode recording (See e.g. [0021] and [0027] on recording future episodes of the particular program).
The combined teaching discloses the method of claim 29, wherein the causing the user device to change to episode recording is further based on a change in a count of scheduled recordings of the series of episodes.
Claim 42. LaFreniere disclose the method of claim 29, wherein the causing the user device to schedule the second recording of the another episode is further based on remote control commands associated with the first episode or the series of episodes (See e.g. [0027]-[0028] on remote control command).
Kent JR disclose causing the user device to change to episode recording (See e.g. [0021] and [0027] on recording future episodes of the particular program).
The combined teaching discloses the method of claim 29, wherein the causing the user device to change to episode recording is further based on remote control commands associated with the episode or the series of episodes.
Claim 50. LaFreniere disclose the method of claim 29, wherein the causing the user device to schedule the second recording of the another episode is further based on a change in tuning behavior of a service associated with the series of episodes (See e.g. [0021] on tuning. See also media content currently being viewed, recommendations, highest rated content. Examiner Note: current being viewed implied tuning behavior).
Kent JR disclose causing the user device to change to episode recording (See e.g. [0021] and [0027] on recording future episodes of the particular program).
The combined teaching discloses the method of claim 29, wherein the causing the user device to change to episode recording is further based on a change in tuning behavior of a service associated with the series of episodes.
Claim 54-55. Fishman disclose the method of claim 29, wherein the causing to schedule the second recording is based on levels of likes/dislikes associated an episode, via one or more social media platform (See [0032]-[0033] on identifying those content items that appears to be of heightened interest to viewers based on…the number of times the video program has been reference in microblogs or on-line social network news. Examiner note: the number of times referencing a content that appears to be of heightened interest indicated symbolic feedback, level of likes (and implied level of dislike) of the content).
Kent JR disclose causing the user device to change to episode recording (See e.g. [0021] and [0027] on recording future episodes of the particular program).
The combined teaching disclose the method of claim 29, wherein the causing to to change to episode recording is based on levels of likes/dislikes received, via one or more social media platforms, for the episode during the scheduled time of the an episode.
Furthermore, levels of likes/dislikes are non functional descriptive materials. The method of scheduling remain the same regardless of social media likes/dislikes/mentions. That is, the symbolic feedback, levels of likes/dislikes does not functionally change the schedule recording method of claim 29.
In re Curry, the Board held that in a computer-implemented method of providing "wellness-related services," "the 'wellness-related data in the databases.., does not functionally change either the data storage system or communication system used in the method of claim 81. Nonfunctional descriptive material cannot render nonobvious an invention that would have otherwise been obvious." See Ex parte Curry, 84 USPQ2d 1272 (BPAI 2005), aff'd (Fed. Cir. Appeal No. 2006-1003, aff'd Rule 36 June 12, 2006) MPEP 2106.01.
In re John, the Board held that the descriptive material (i.e., "control information" and "request" comprising a description of a development environment) recited in claim 1 is non-functional descriptive material because each of the "control information" and "request" does not functionally affect the process of managing a development environment. Rather, the control information is merely information that is used for "managing said first request" by a computer program and the request is data that is received ("receiving a first request") and processed ("processing said first request") by the system. In each case, the data (i.e., "control information" and "request") do not affect how the method of the prior art is performed on a computer system. In other words, the method of receiving and processing the request and reviewing the request "in accordance with control information" is carried out in the same way regardless of the nature of the request or control information. See Ex parte John F. Bisceglia, Appeal 2007-3447.
Claim 56. Angiolillo disclose the method of claim 29, wherein at least one subsequent episode comprises a new video program in a series of video program (Examiner Note: “new” video program is not further defined in any shape or form, reads on any video program such as broadcast/rebroadcast of next/subsequent episode that is “new” with respect to the current episode. Furthermore, first airing of an episode is within the broadest reasonable interpretation of new video program. See e.g. [0037]-[0038]. [0037] FIG. 4B depicts a Broadcast Information screen 440 for the user to view the program data associated with the selected Broadcast Information option from the program information menu 430, in accordance with an exemplary embodiment of the present disclosure. The Broadcast Information screen 440 may include a variety of information for the user to view. These may include when the show or program was first aired, when the show or program was previously aired (if not the same as the first airing), any future air times, next sequential episodes, previous episodes, episode/program blocking features, recording features, and other broadcasting information. [0038]…In addition, the "Broadcast Information" screen 440 may indicate that the next sequential episode of U.S. Idol is #99 and that the previous episode was #97. In one embodiment, the next sequential episode may also include a Record option for recording, for example, on a personal video recorder (PVR) or digital video recorder (DVR), when it is schedule to air. [0026] Referring back to FIG. 2C, the program information menu 230 may be interactive and may provide a variety of categories for a user to choose from. These may include program information, broadcast (or rebroadcast) information, reviews, social network information, scores and/or statistics of events within a program, and advertisements and advertisement information. Other program data may also be considered and provided. In one embodiment, the program title, episode number, and/or first airing information may also be displayed at the program information menu 230 for convenient user viewing).
Kent JR also disclose the method of claim 29, wherein at least one subsequent episode comprises a new video program in a series of video program (See e.g. [0021] and [0027] on recording future episodes of the particular program).
Claim 57. Fishman disclose using a particular channel of a programming guide for recording (See [0017] on a channel broadcasting a particular live sports event).
LaFreniere disclose using title of the series of episode as preference (See [0065] on “Lost” and [0064] on “The Office”.
Angiolillo disclose using category/genre of a content for recording (See [0019] on category, advertisement information, initial broadcast time, ratings, popularity, closed-captioning information, etc.).
Kent JR disclose causing the user device to change to episode recording (See e.g. [0021] and [0027] on recording future episodes of the particular program).
The combined teaching discloses the method of claim 29, wherein the causing the user device to schedule the second recording is further based on determining that one or more characteristics of the second recording satisfies one of the following preferences of a user associated with the user device: a genre of content, a title of the series of episodes, a particular actor, a particular director, a maturity rating, or a particular channel of a programming guide.
63. Jeon disclose the method of claim 29, wherein the training the machine learning model is further based on: viewer behavior information corresponding to the plurality of series of episodes, and ratings information corresponding to the plurality of series of episodes ([0034] One model used for the content-based recommendation approach is to make recommendations based on a combination of genre data (e.g., from an electronic program guide (EPG) provider) and ratings-based popularity data (e.g., for stations and/or programs). The model can use genre field values to find sets of similar programs. The sets may then be ranked by popularity of the station (e.g., where the program airs) or popularity of program itself (e.g., if such data is available). Programs that are series may be treated either at the series level or the episode level, or both. For example, when a new episode for an existing series airs, it may be assigned popularity data reflective of the series as a whole, but the data may transition over time into a reflection of the popularity of the episode, or some blend of the episode and the series (particularly when it is difficult to separate actions by users that indicate popularity of a series from popularity of an episode). Also, programs, such as in the form of episodes, may be organized into common clusters other than a group of episodes in a series. Moreover, although relatively implicit indications of popularity (e.g., clicking and ratings) are discussed here, more explicit indications, such as "5 star" ratings systems or other user ratings may be used. [0085] A recommendation generator 308 can use data from the user-click history 306 to generate recommendations based on various attributes. In this sense, the recommendation generator 308 can serve as the "recommendation engine" described above. The recommendation generator 308 can also use audience measurement data 310 in generating recommendations. For example, the audience measurement data 310 may represent viewer popularity collected over time, such as by Nielson ratings. Moreover, the recommendation generator may also draw upon corpus data 309, which may include information such as blogs, media-directed web sites, and other such media-directed web content. Such information may likewise be used to discern relationships between and among particular programs for purposes of determining whether two programs are sufficiently related that a recommendation can be made for one based on a determination that a user is interested in the other. Examiner Note: “viewer behavior information” is not further defined, reads on any viewer behavior information such as user click, ratings, or audience measurement data, etc.).
64. Fishman disclose the method of claim 29, further comprising: adjusting, based on the plurality of social media events and during the episode, an advertising rate corresponding to the series of episodes ([0017] In one example embodiment, in addition to determining a personalized hot list of content items, a smart playlist system may trigger recording of a certain program as soon as the program has been identified as a live program and of high relevance to the viewer. For example, a viewer may not be tuned into a channel broadcasting a particular live sports event. If the smart playlist system determined that the live sports event is of high relevance to the viewer, the smart playlist system may trigger the recording of the live broadcast of the sports event on the viewer's client device (e.g., a set top box, a desktop computer, etc.) and also alerts the user to the fact that she may be interested in the event being currently broadcast on a certain channel. The viewer may then ignore the alert. If the viewer, instead, tunes to the suggested channel the viewer would not have missed the beginning of the broadcast because the recording of the program has been automatically triggered by an instruction provided to the viewer's client device from the smart playlist system. In one example, the high relevancy of the live broadcast may have been determined based on the fact that all of the viewer's social network contacts have either tuned into the associated channel or have scheduled the recording of the broadcast. In another example, the high relevancy of the live broadcast may have been determined based on the viewer's profile or on the viewer's viewing history. An example smart playlist system may be implemented within architecture illustrated in FIG. 1.” Examiner Note: “advertising rate” is not further defined, reads on any advertising/recommendation rate of the episode. Thus, recommending/triggering recording of such live program indicated adjusting the “advertising rate” of the program)
67. (Currently Amended) Kent JR disclose the method of claim 29, wherein the scheduled time of the episode of the series is a season premiere of the episode of the series ([0017] In a particular embodiment, the user configuration settings 134 include a record initial episodes options 136. When the record initial episodes option is selected in the user configuration settings 134, the scheduling module 122 may search the EPG information 126 to identify initial episodes of series programs. When an initial episode of a series program is identified, the initial episode may be scheduled for recording. For example, when the user configuration settings 134 include the record initial episodes options 136, the scheduling module 122 may search the EPG information 126 to determine whether a series premier, a series pilot, or a season premier of a series program is identified within the EPG information 126. Series premiers, series pilots and series season premiers are collectively referred to herein as initial episodes of series programs. If an initial episode of a series program is identified in the EPG information 126, the scheduling module 122 may schedule the initial episode for recording. [0018] In a particular embodiment, when the record initial episodes option 136 is selected, the scheduling module 122 searches the EPG information 126 each time the EPG information is updated in order to identify initial episodes of series programs in the updated EPG information. For example, the scheduling module 122 may search the EPG information 126 using a keyword search. To illustrate, the scheduling module 122 may search for keywords within the EPG information 126, such as "pilot," "premier," or "season premier". In another example, the EPG information 126 may include a premier flag associated with one or more programs. The premier flag may indicate that the associated program is an initial episode of a series program. The scheduling module 122 may search the EPG information 126 to identify programs associated with premier flags and schedule such programs for recording. In another example, the scheduling module 122 may compare the updated EPG information with EPG information that was previously received to determine whether one or more programs that were not identified in the previously received EPG information are identified in the updated EPG information. In various embodiments, the scheduling module 122 searches the EPG information 126 using another technique or combination of techniques. For example, the scheduling module may use a combination of a keyword search, a search for premier flags, or a comparison of previously received EPG information to updated EPG information to identify the initial episodes of the series programs).
68. Kent JR disclose causing the user device to change to episode recording (See e.g. [0021] and [0027] on recording future episodes of the particular program).
LaFreniere disclose the method of claim 29, wherein the causing the user device to change the episode recording to the series recording comprises: storing a profile comprising a plurality of rules for scheduling future recording at the user device (See e.g. [0027] on user preferences rules. Examiner note: any programmed/scheduled event indicated related rule is being applied/satisfied. See also [0027] … The user preferences may include rules, permissions, stipulations, and other preferences that manage the respective device.”[0046] … For example, the user preferences may specify that viewing information for a particular television series is uploaded to one or more social networks whereas viewing information related to a separate program is not updated to the social networks. The user preferences may identify actions to be taken or not taken with regard to a number activities and events taken by the user or the user's contacts. [0061] Section 504 may display the status of the user according to user preferences. A user may set status information to update automatically according to the current viewing selection. For example, a user may set section 504 to detail the current programming selection the user is viewing. The user may also limit the number of friends or contacts that receive these status updates through the user preferences. A user may set section 504 to be automatically sent only to those who are viewing the same program or who have joined a certain viewing group or who have similar programming preferences, recorded as viewing statistics. For example, a user may join the group, "Fans of The Office," and select status information to be viewable to anyone with the same preferences, even those who have not been added as friends or contacts).
Angiolillo disclose causing, based on a determination that one or more characteristics of the series of episodes satisfy one or more of the plurality of rules, the user device to change the episode recording to the series recording (See e.g. [0037]-[0038]).
The combined teaching disclose the method of claim 29, wherein the causing the user device to change the episode recording to the series recording comprises: storing a profile comprising a plurality of rules for scheduling future recording at the user device; and causing, based on a determination that one or more characteristics of the series of episodes satisfy one or more of the plurality of rules, the user device to change the episode recording to the series recording.
69. Kent JR disclose The method of claim 29, wherein the causing the user device to schedule the second recording comprises causing the user device to: schedule a separate recording for each new episode of the series of episodes; and exclude, from the second recording of the series of episodes, repeat episodes of the series of episodes ([0021] After selection of a particular season premier, pilot, or premier of a series program, recorded media 132 associated with the selected program may be presented at the display device 106. After presentation of at least a portion of the recorded media 132 associated with the program, the GUI module 130 may present an additional user interface display. For example, when a user stops playback of the recorded media 132 associated with the program, the user may be presented with a user interface display that includes a selectable option to control the recording of future episodes of the particular program. In a particular illustrative embodiment, the user interface display includes a selectable record future episodes option. The record future episodes option may cause future episodes of the particular series programs to be scheduled for recording by the scheduling module 122. To illustrate, after having watched at least a portion of the pilot, season premier or premier episode of a particular series program, the user may determine that he or she enjoys the program and may select the record future episodes option in order to ensure that future episodes of the particular series program are recorded. The record future episodes option may be associated with other user configuration settings 134 such as do not record reruns, record programs during a particular time slot, options for how long the program is stored at the recorded media 132, or other recording options. [0027] After an initial episode of a series program has been recorded, the method may include, at 232, generating a graphical user interface (GUI) including information about recorded programs. The information about the recorded programs may include information distinguishing the recorded initial episode in the GUI as an initial episode of a series program. For example, the information identifying the recorded initial episode may be highlighted, may be associated with the flag 229, or may be associated with a separate menu or list of programs to indicate that it is an initial episode of a series program. The method may also include, at 234, presenting a display, including at least a portion of the recorded initial episode. After presentation of at least a portion of the recorded initial episode, the method may include, at 236, presenting a GUI including a selectable option to record future episodes of the particular series program. For example, after presenting at least a portion of the recorded initial episode, the user may be presented with a GUI and given the option to record future episodes of the same series. In a particular embodiment, after presenting at least a portion of the recorded initial episode, the method includes, at 238, presenting a GUI including a selectable option to blacklist future episode of the particular series program. Blacklisting future episodes may prohibit the future episodes of the series from being recorded at the media recording device).
70. (Currently Amended) Fishman disclose the method of claim 29, wherein generating the plurality of popularity input scores comprises:
extracting, from the plurality of social media events, statistics corresponding to different types of social media events [0038] In one embodiment, a customized playlist is generated by generating a score for each item from the list of popular content items and including items into in the customized playlist based on respective scores of the items from the list of popular content items. The scoring may be based on the viewer's preferences identified in the viewer's profile, based on data from the viewer's personal bucket and the viewer's social bucket. A content item from a category that is not indicated in the viewer's profile as being of interest to the viewer and that is not considered as being of interest to the viewer based on the viewing history of the viewer may still be assigned a high score by the customization module 440 based on the information from the viewers social bucket. For example, the customization module 440 may be configured to weigh heavily an indication that a certain content item is of high interest to a great number of the viewer's social contacts. [0017] In one example embodiment, in addition to determining a personalized hot list of content items, a smart playlist system may trigger recording of a certain program as soon as the program has been identified as a live program and of high relevance to the viewer. For example, a viewer may not be tuned into a channel broadcasting a particular live sports event. If the smart playlist system determined that the live sports event is of high relevance to the viewer, the smart playlist system may trigger the recording of the live broadcast of the sports event on the viewer's client device (e.g., a set top box, a desktop computer, etc.) and also alerts the user to the fact that she may be interested in the event being currently broadcast on a certain channel. The viewer may then ignore the alert. If the viewer, instead, tunes to the suggested channel the viewer would not have missed the beginning of the broadcast because the recording of the program has been automatically triggered by an instruction provided to the viewer's client device from the smart playlist system. In one example, the high relevancy of the live broadcast may have been determined based on the fact that all of the viewer's social network contacts have either tuned into the associated channel or have scheduled the recording of the broadcast. In another example, the high relevancy of the live broadcast may have been determined based on the viewer's profile or on the viewer's viewing history. An example smart playlist system may be implemented within architecture illustrated in FIG. 1. Examiner Note: social network contacts “tuned in” and “scheduled” are different social network events, the numbers are the statistics); and
wherein the causing the user device to automatically schedule the second recording is further based on the predicted popularity of the episode (See e.g. [0015] on obtaining content-related information/event such as currently viewed, being recorded, scheduled, etc. See [0032]-[0033] on identifying those content items that appears to be of heightened interest to viewers based on…the number of times the video program has been reference in microblogs or on-line social network news. [0033] Content-related data, which may include viewership information, changes in viewership (e.g., a sudden spike in the number of users trending about a video program or a dramatic increase in the number of viewers watching or recording a video program), ratings of content, references to content items in on-line publications, rental and purchasing information, etc., may be processed by the analytics module 314 to identify those content items that appear to be of heightened interest to viewers. An indication of the heightened interest (also referred to as popularity) may be expressed in terms of a popularity value, which may be calculated for a content item (e.g., a video program) based on, cumulatively, the total number of viewers currently watching or recording the video program being above a predetermined threshold value, the total number of viewers currently watching or recording the video program having increased by a certain percent as compared to the earlier measurement, the number of times the video program has been referenced in microblogs or on-line social network news feeds, etc. The recommendation engine 310 may be configured to generate a list of popular content items, where a popular item is associated with a popularity value above a certain threshold value, customize the lists respectively for viewers associated with viewer devices 340 and 350, and provide the customized lists to the viewer devices 340 and 350. Customization process is described in further detail with reference to FIG. 4 below.).
Joen disclose predicting, using the machine learning model and based on the statistics, a popularity of the episode (([0029] Recommendations can be made, for example, using a recommendation engine as part of a computer-based system, such as a system using central servers to provide a number of different services, such as search, maps, shopping, and other such services. Two example categories of approaches can include "collaborative filtering" and "content-based recommendation." Collaborative filtering can also be referred to as "behavioral data-based recommendation" because it uses input from many users to "train" the recommendation engine. The content-based recommendation approach generally involves analyzing the content itself to determine similarity of items. [0034] One model used for the content-based recommendation approach is to make recommendations based on a combination of genre data (e.g., from an electronic program guide (EPG) provider) and ratings-based popularity data (e.g., for stations and/or programs). The model can use genre field values to find sets of similar programs. The sets may then be ranked by popularity of the station (e.g., where the program airs) or popularity of program itself (e.g., if such data is available). Programs that are series may be treated either at the series level or the episode level, or both. For example, when a new episode for an existing series airs, it may be assigned popularity data reflective of the series as a whole, but the data may transition over time into a reflection of the popularity of the episode, or some blend of the episode and the series (particularly when it is difficult to separate actions by users that indicate popularity of a series from popularity of an episode). Also, programs, such as in the form of episodes, may be organized into common clusters other than a group of episodes in a series. Moreover, although relatively implicit indications of popularity (e.g., clicking and ratings) are discussed here, more explicit indications, such as "5 star" ratings systems or other user ratings may be used. [0036] A third model used for the content-based recommendation approach is to apply extra data to a filter process to produce better clustering. For example, a system may apply machine learning techniques to analyze the content of a document to determine one or more concepts of the document, in order to be able to locate a relevant document. In a media search setting, such analysis may be performed on various data relating to a program. Such extra data may include, for example, closed caption data, blogs, or some web site content that extensively describes the program. In this sense, this third model is an extension of the second model. One advantage of using this model is that it can offset the lack of program description available in the source data from most EPG data providers. The lack of adequate descriptions can negatively affect the quality of filter-based recommendations because the filter performs clustering using keywords (e.g., using keywords available from program descriptions). Using keywords derived from closed captions, blogs, or web sites can improve recommendations when program descriptions are inadequate or unavailable. [0044] Profiles can be generated for a user and updated over time based on input explicitly provided by the user and selections made by the user (e.g., web sites visited, user clicks on those web sites, etc.). Profiles may be, for example, maintained in social networking or other sites. If a user subsequently enters a search term that in some way is related to user profile information, the recommendation engine can automatically combine the information to generate recommendations that may interest the user. [0096] Over time, user activity on the TV client 302 collected by the TV front end 304 (via arrow 7) may be provided to the user click history 306 via arrow 8. Such user activity may include user actions that can be used later, for example, in formulating recommendations. For example, the user actions may include inputs by the user on the Internet that relate to particular TV programs or stations. In this way, the recommendations can be based, at least in part, on the collaborative filtering approach described above or other similar approaches. Moreover, when more than one user is involved, recommendations using the collaborative filtering approach are "behavioral data-based recommendations" because they can use input from many users to "train" the recommendation engine).
Claims 71 and 76 are drawn to claim 29 and are rejected under same rationale. Note for apparatus and CRM. See Fishman claim 20-21, [0043]-[0044].
Claims 73 and 77 are drawn to claim 64 and are rejected under same rationale.
Claims 74 and 78 are drawn to claim 69 and are rejected under same rationale.
Claims 75 and 79 are drawn to claim 70 and are rejected under same rationale.
Claim Rejections - 35 USC § 103
Claim 7 is rejected under 35 U.S.C. 103 as being unpatentable over Fishman et al (2012/0059825) in view of LaFreniere et al (US 2011/0126251 A1), Angiolillo et al (US 20090007179 A1), Jeon et al (US 20090055385 A1) Kent JR. et al (US 20100131987 A1), and further in view of Ketkar (US 2012/0030587 A1)
Claim 7: Fishman disclose content including free program or video on demand, but fails to explicitly disclose free content is being “linearly scheduled”.
Ketkar disclose social media program recommendation and DVR (See [0034]). Ketkar also disclose one can access linearly scheduled program ([0064]) in addition to non-linear type such as video on demand.
As such, one of ordinary skill in the art would have understand the free video being broadcasted is linearly scheduled program.
As such, it would have been obvious to one having ordinary skill in the art, before the effective filing date of the claimed invention that 1) the free video being broadcasted is linearly scheduled program; 2) to modify the access to free video of Fishman to incorporate linearly scheduled episodes of Ketkar. Given the fact programs can be accessed in linear/non-linear (video on demand), one having ordinary skill in the art would have been motivated to make this obvious modification with predictable result of wherein the series of episodes comprises linearly-scheduled episodes.
Claim Rejections - 35 USC § 103
Claims 51 and 72 are rejected under 35 U.S.C. 103 as being unpatentable over Fishman et al (2012/0059825) in view of LaFreniere et al (US 2011/0126251 A1), Angiolillo et al (US 20090007179 A1), Jeon et al (US 20090055385 A1), Kent JR. et al (US 20100131987 A1), and further in view of Reneris (2010/0272414).
Claims 51 and 72: Fishman do not teach automatically deleting stored episode. Reneris teaches “automatically deleting at least one subsequent episode of the series of episodes from the user device after the at least one subsequent episode has been stored at the user device for a threshold quantity of time.” (Reneris [0035], “For example, the standalone PVR 116 may perform some tasks autonomously while the standalone computer 110 has the ability to communicate with the PVR using an appropriate protocol and provide commands such as to schedule a recording and delete a recording”).
The artisan of ordinary skill, starting with the method of determining popularity of content of Fishman, would have appreciated the benefit of automatically deleting stored episode as proposed by Reneris. The ordinarily-skilled artisan would readily see the benefits of deleting a recording to save space, which would provide the well-known, predictable, and expected results.
Therefore, a person having ordinary skill in the art before the effective filing date of the invention would have found it obvious to combine the method of determining popularity of content of Fishman with the automatically deleting stored episode as proposed by Reneris to achieve the well-known and expected benefit of space saving.
Claim 66 is rejected under 35 U.S.C. 103 as being unpatentable over Fishman et al (2012/0059825) in view of LaFreniere et al (US 2011/0126251 A1) and Angiolillo et al (US 20090007179 A1), Jeon et al (US 20090055385 A1), Kent JR. et al (US 20100131987 A1) and further in view of Allard et al (US 20090113480 A1)
Claim 66, While Jeon disclose using machine learning determining content popularities, Jeon do not teach wherein the machine learning model comprises a support vector machine.
However, Allard disclose social network based recording (thereby in the same field of endeavor) and further disclose wherein the machine learning model comprises a support vector machine. (Allard “[0046] With reference now to FIG. 5, a system 500 that can intelligently configure a content schedule and/or a content channel is provided. Generally, the system 500 can include the contacts component 108 that can populate the content schedule 110 based upon a social network 112, as described herein. For example, in many cases, the contacts component 108 can populate the content schedule 110 based upon express input from a user 114 of the device 106 and/or from a manager of the content channel 102. However, in other cases, the contacts component 108 can employ machine learning techniques to provide for determinations or inference that relate to, e.g., selecting or filtering items supplied to the content schedule 110, as will be explained in more detail infra. ..[0051] Such inference can result in the construction of new events or actions from a set of observed events and/or stored event data, whether or not the events are correlated in close temporal proximity, and whether the events and data come from one or several event and data sources. Various classification (explicitly and/or implicitly trained) schemes and/or systems (e.g. support vector machines, neural networks, expert systems, Bayesian belief networks, fuzzy logic, data fusion engines . . . ) can be employed in connection with performing automatic and/or inferred action in connection with the claimed subject matter.”
The artisan of ordinary skill, starting with the method of determining popularity of content of Fishman+LaFreniere+Angiolillo+Jeon, would have appreciated the fact that various classification system (including SVM) can be employed for popularity determination and content scheduling as disclosed by Allard. Therefore, a person having ordinary skill in the art before the effective filing date of the invention would have found it obvious to combine the method of determining popularity of content of Fishman+LaFreniere+Angiolillo+Jeon with the various classification system (including SVM) can be employed for popularity determination and content scheduling as disclosed by Allard to achieve the well-known and expected result.
Furthermore, the kind of machine learning model being used is non functional descriptive materials. The method of popularity determination and scheduling remains the same regardless of the model being used. That is, the kind of ML model (only high level recitation without any specific details) does not functionally change the schedule recording method of claim 33.
In re Curry, the Board held that in a computer-implemented method of providing "wellness-related services," "the 'wellness-related data in the databases.., does not functionally change either the data storage system or communication system used in the method of claim 81. Nonfunctional descriptive material cannot render nonobvious an invention that would have otherwise been obvious." See Ex parte Curry, 84 USPQ2d 1272 (BPAI 2005), aff'd (Fed. Cir. Appeal No. 2006-1003, aff'd Rule 36 June 12, 2006) MPEP 2106.01.
In re John, the Board held that the descriptive material (i.e., "control information" and "request" comprising a description of a development environment) recited in claim 1 is non-functional descriptive material because each of the "control information" and "request" does not functionally affect the process of managing a development environment. Rather, the control information is merely information that is used for "managing said first request" by a computer program and the request is data that is received ("receiving a first request") and processed ("processing said first request") by the system. In each case, the data (i.e., "control information" and "request") do not affect how the method of the prior art is performed on a computer system. In other words, the method of receiving and processing the request and reviewing the request "in accordance with control information" is carried out in the same way regardless of the nature of the request or control information. See Ex parte John F. Bisceglia, Appeal 2007-3447.
Conclusion
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/LUT WONG/Primary Examiner, Art Unit 2127
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