DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Response to Amendments
The action is responsive to the Applicant’s Amendment filed on 3/11/2026. Claims 1-2, 4-12, and 20 are pending in the application. Claims 1 and 6 are currently amended. Claims 3, and 13-19 are canceled. Claim 20 is new.
Response to Arguments
Applicant’s arguments with respect to the rejections of claims 1-2, 4-12, and 20 have been fully considered. In view of the claim amendment filed, the rejection has been withdrawn. However, upon further consideration, a new ground(s) of rejection is made.
Further, regarding the new limitations recited in claims 1 and 6, it is submitted that they are properly addressed by the new ground of rejection.
Furthermore, it is also submitted that all limitations in pending claims, including those not specifically argued, are properly addressed. The reason is set forth in the rejections. See claim analysis below for detail.
Claim Rejection - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 1-11 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Regarding claims 1 and 6, the term "future format" is not defined by the claim, the specification does not provide a standard for ascertaining the requisite degree, and one of ordinary skill in the art would not be reasonably apprised of the scope of the invention. Claims must particularly point out and distinctly claim the invention, and must have clarity and precision.
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.
Claims 1-2, 4-12, and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Felt et al. (US 20140057661 A1) in view of Kane et al. (US 20150066648 A1) and Hoffman (US 20180268253 A1).
Regarding Claim 1, Felt discloses a system for providing and recommending cultural content (Fig. 1; [0012]: Systems and methods for enhancing a user visit to a site premises are described herein… For example, a site premises may include a museum), comprising:
one or more processors and one or more memory ([0025]: In general, a processor (e.g., a microprocessor) receives instructions, from a computer-readable medium, (e.g., a memory, etc.), and executes those instructions);
an item preservation subsystem configured to manage one or more cultural content items (Fig. 1; [0018]: The site data may be maintained by site data management facility 106 as site data 116 within data storage facility 112… site data may include, without limitation, maps of site premises, proximity maps of portions of site premises, images, text, media content (e.g., virtual tour media content), and any other data associated with site premises), the item preservation subsystem having one or more of:
an item creation module configured to store the one or more cultural content items (Fig. 1; [0018]: Site data management facility 106 may be configured to collect and maintain site data associated with one or more site premises);
a document module configured to associate one or more document items with the one or more cultural content items (Fig. 1; [0018]-[0020]: Site data 116 may include any information descriptive of or otherwise related to one or more site premises. For example, site data 116 may include geographic location data… data descriptive of or otherwise related to a site premises and/or site features within a site premises);
a periodic item review module configured to store one or more item reviews and associate the one or more item reviews with the one or more cultural content items ([0021]: Site data 116 may also be collected from one or more user devices. For example, user feedback such as user ratings of site features within a site premises may be collected and added to site data 116 by site data management facility 106. The collected user ratings may be used to generate data specifying popular and/or unpopular site features within a site premises); and
a data analysis module configured to provide one or more user selectable notifications for the one or more cultural content items ([0022]: Other tools and/or output that may be provided via an interactive user interface may include… notifications of specials within a site premises), wherein the one or more user selectable notifications is associated with one or more metrics ([0021]: The collected user ratings may be used to generate data specifying popular and/or unpopular site features within a site premises);
a media matching subsystem configured to enable sharing of one or more media items ([0060]: FIG. 9 illustrates GUI 500 with a visual indicator 902 indicating a location associated with a match to a premises search displayed therein), the media matching subsystem having one or more of:
a contributed media search module configured to receive one or more first search parameters and to return a list of the one or more media items relevant to the one or more first search parameters (Fig. 5; [0059]-[0060]: When the user of the mobile device selects the "search premises" option, the mobile device may detect the user selection and provide one or more tools to facilitate user input of search terms (e.g., keywords)… Site features that are determined to match search terms may be referred to as search results site features and may be visually indicated within interactive map 502); and
a media matching module configured to compare one or more provided media items to the one or more media items to find one or more similar media items and provide the one or more similar media items (Fig. 9; [0022]: Interactive interface facility 110 may provide… one or more site features within a site premises, site premises route planning tools, and notifications of specials within a site premises; [0048]: If server subsystem 202 determines from the comparison that the mobile device is located within a particular site premises or within a predefined proximity of the site premises, server subsystem 202 may automatically provide site data for the site premises to the mobile device); and
a supplementary media creation module configured to receive one or more descriptions and or one or more original media items in order to create one or more digitally derived media items relevant to the one or more descriptions and or the one or original more media items ([0059]-[0060]: The mobile device may receive and use the search terms to initiate a search of site data associated with the site premises. As an example… a location that serves a particular type of food (e.g., a hamburger) or sells a particular product (e.g., a souvenir). The mobile device may provide search results to the user);
a content-visitor subsystem configured to provide one or more items to a user ([0075]: As another example of output configured to enhance a visit to a site premises, the mobile device may be configured to provide a virtual tour of the site premises and/or one or more site features within the site premises), the content-visitor subsystem having one or more of:
a compilation module configured to compile one or more multi-media items associated with the one or more cultural content items to form one or more multimedia compilations ([0075]: The virtual tour may include a presentation of media content in any suitable format, including, without limitation, audio, video, photos, slideshows, and multimedia presentations about the site premises and/or one or more site features within site premises).
a user search module configured to receive one or more second parameters, and to return a list of multimedia compilations relevant to the one or more second parameters ([0061]: Next, the user may select a second site feature within interactive map 502 to be the next site feature along the route. The mobile device may detect the selections and identify a proposed route connecting the starting location to the location of the next feature);
However, Felt does not explicitly teach “wherein each of the one or more multimedia compilations is associated with a geographic location; a visitor forecast module configured to determine one or more visitor traffic forecasts to happen in the future for one or more locations associated with the one or more multimedia compilations; a user search module configured to receive one or more second parameters, and to return a list of multimedia compilations relevant to the one or more second parameters a content recommendation module configured to recommend one or more multimedia compilations to a user; a rating module configured to apply one or more ratings to one or more of: one or more multimedia compilations in the list of multimedia compilations; or one or more of the multimedia compilations recommended to the user, wherein one or more of the item preservation subsystem, the media matching subsystem, or the content-visitor subsystem are stored in the one or more memory and executed by the one or more processors”.
On the other hand, in the same field of endeavor, Kane teaches
wherein each of the one or more multimedia compilations is associated with a geographic location ([0097]: Recommendations may also be provided based on real-time location information, such as that provided by smart-phone GPS data; [Nonfunctional descriptive material describing the compilations]);
a visitor forecast module configured to determine one or more visitor traffic forecasts to happen in the future for one or more locations associated with the one or more multimedia compilations ([0016]-[0017]: metrics pertaining to how and where users interact with all or part of individual content items may be collected, aggregated, and reported… These metrics provide insights into what content items, or portions thereof, were accessed at a given location. These insights may benefit users by providing recommendations for future items).
a content recommendation module configured to recommend one or more multimedia compilations to a user ([0004]: FIG. 1 is an illustrative architecture for collecting content access events and generating recommendations for media content items based on location),
wherein the content recommendation module recommends at least one future format of the one or more multimedia compilations based at least in part on the one or more visitor traffic forecasts associated with the geographic location of each of the one or more multimedia compilations (Figs. 10-11; [0129]: Various combinations of content item information 402, content access information 502, user access profile data 602, and parameter information 702 may be used by recommendation module 334 to generate recommendations 1002; [0146]: At 1106, potential content items are ranked. Rankings may be based on any number of different parameters, such as relevance, distance, proximity, completion metrics, usage patterns, abandonment statistics, popularity, user reviews, user preference, user behavior, past viewing history, past purchase history, and so on),
wherein said at least one future format comprising at least one of a multimedia presentation or a multimedia experience based at least in part on said one or more visitor traffic forecasts (Figs. 10-11; [0129]-[0147]: From this information and data, the recommendation module 334 computes a wide variety of recommendations… If this list of content items becomes large, the list can be filtered according to various factors, such as ratings, popularity, sales rank, and so forth; [0147]-[0149]: At 1108, the set of potential content items may be filtered. Various filters may be optionally applied to narrow the list of potential content items [visitor traffic forecasts correspond to a filter]).
Additionally Hoffman teaches a rating module configured to apply one or more ratings to one or more of: one or more multimedia compilations in the list of multimedia compilations ([0089]-[0094]: FIG. 11 shows the feed for a user. Note that the items in the feed are documents, presentations, images, Web pages, and other information… In FIG. 11, there are three numbers reported for each item… Run a computation (i.e., a subroutine) called ItemRanker (described below) on the candidate set to assign an overall rank to each item);
or one or more of the multimedia compilations recommended to the user ([0099]-[0102]: When the ranked search results are presented, the system presents the single item with the highest rank), wherein one or more of the item preservation subsystem, the media matching subsystem, or the content-visitor subsystem are stored in the one or more memory and executed by the one or more processors ([0287]-[0290]: The following discussion provides a brief, general description of a suitable computing environment in which the invention can be implemented).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the teaching of Felt to incorporate the teachings of Kane and Hoffman to include a content recommendation module and a rating module for multimedia compilations recommended to the user.
The motivation for doing so would be to improve searching among content items, as recognized by Hoffman ([Abstract] of Hoffman: The disclosed techniques provide improved methods for searching among content items, organizing content items into categories, and pruning redundant content) and to apply filters to the list of recommended content times, as recognized by Kane ([0147] of Kane: At 1108, the set of potential content items may be filtered).
Regarding Claim 2, the combined teachings of Felt, Kane, and Hoffman disclose the system of claim 1.
Hoffman further teaches wherein the data analysis module is further configured to:
calculate one or more statistics of the one or more metrics([0023]: FIG. 19 is a display page illustrating how item and collection statistics are presented); and
provide the one or more user selectable notifications in response to the one or more metrics meeting one or more thresholds ([0091]: A push notification can be triggered automatically when a sufficient number of relevant items are found, or at a user specified interval).
Regarding Claim 4, the combined teachings of Felt, Kane, and Hoffman disclose the system of claim 1.
Felt further teaches wherein the one or more first search parameters include one or more of: an entity name, a time period, a radius from a current location, or a user selected location (Fig. 1; [0047] The user of the mobile device may desire to visit a particular site premises such as an amusement park. The user may utilize the mobile device to initiate a search of site data 116 maintained by server subsystem 202 for site data associated with the amusement park).
Regarding Claim 5, the combined teachings of Felt, Kane, and Hoffman disclose the system of claim 1.
Hoffman further teaches wherein the content recommendation module further comprises: a similarity detection module configured to determine one or more similar users to the user and to calculate at least one rating probability of the one or more multimedia compilations based on the one or more similar users, wherein the one or more multimedia compilations are recommended based at least in part on the at least one rating probability ([0008]: FIG. 5 is a flow diagram illustrating the processing of a similarity component in accordance with some examples of the disclosed technology[0035]-0036]: Any or all these features can be used to determine similarity).
Regarding Claim 6, Felt discloses the computer-implemented method, executed by one more processors, of recommending cultural content, comprising:
receiving one or more search parameters from a user, wherein the one or more search parameters include at least a location (Fig. 1; [0047] The user of the mobile device may desire to visit a particular site premises such as an amusement park. The user may utilize the mobile device to initiate a search of site data 116 maintained by server subsystem 202 for site data associated with the amusement park);
filtering one or more content items using the one or more search parameters to form a first subset of content items (Fig. 5; [0054]: Alternatively, interactive map 502 may be filtered to show only popular attractions 504-5 and 504-8.);
However, Felt does not explicitly teach “determining, using one or more statistical models, a first rating probability and a second rating probability for each content item in the first subset of content items; calculating, for each content item in the first subset of content items, a ranking using at least the first rating probability and the second rating probability; ordering the first subset of content items using the ranking to form a ranked first subset of content items; and outputting the ranked first subset of content items using the ranked first subset of content items based in part on said at least one future format of claim 1.”
On the other hand, in the same field of endeavor, Hoffman teaches
determining, using one or more statistical models, a first rating probability and a second rating probability for each content item in the first subset of content items (Figs. 19-20; [0194]- [0196]: FIG. 19 is a display page 1900 illustrating statistics for a collection);
calculating, for each content item in the first subset of content items, a ranking using at least the first rating probability and the second rating probability ([0092]: Compute a textual rank for each candidate item (i.e., an item in the candidate set) based on a similarity algorithm. This rank is based on the textual contents of the items and on the search phrase. [0095] 3. Run a computation (i.e., a subroutine) called ItemRanker (described below) on the candidate set to assign an overall rank to each item);
Additionally, Kane teaches ordering the first subset of content items using the ranking to form a ranked first subset of content items ([0149]: The recommendations may be visually organized according to the rankings applied at 1106) based in part on said at least one future format of claim 1 (Fig. 4; [0064]-[0071]: The report generation module 324 is configured to transform location information and recommendations into user selected formats and representations… Type or content item format 406… (e.g., different formats or imprints of the same title)); and
outputting the ranked first subset of content items using the ranked first subset of content items (Fig. 11; [0149]: At 1112, the recommendations are presented to the user).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the teaching of Felt to incorporate the teachings of Hoffman and Kane to include a content recommendation module and a rating module for multimedia compilations recommended to the user.
The motivation for doing so would be to improve searching among content items, as recognized by Hoffman ([Abstract] of Hoffman: The disclosed techniques provide improved methods for searching among content items, organizing content items into categories, and pruning redundant content) and to apply filters to the list of recommended content times, as recognized by Kane ([0147] of Kane: At 1108, the set of potential content items may be filtered).
Regarding Claim 7, the combined teachings of Felt, Kane, and Hoffman disclose the computer-implemented method of claim 6.
Hoffman further teaches, further comprising: providing one or more additional parameters after outputting the first ranked subset of content items ([0107]: Similarly, the system might rank the pages of a document or the slides in a presentation in terms of which are most likely to interest the user based on the extent to which users have viewed or interacted with portions thereof…); and
re-ordering the first ranked subset of content items in response to selection of one or more of the one or more additional parameters ([0111]: Often items are related but not identical. For example, a slide deck that contains some of the same slides as another, but reordered or intermixed with others); and
outputting the re-ordered first ranked subset of content items ([0251]-[0253]: When the system displays a set of values to the user, it invokes one of the ranking computations… The system uses the ranking computations to produce output that users can see… The goal is to identify the items that are most likely to interest the user).
Regarding Claim 8, the combined teachings of Felt, Kane, and Hoffman disclose the method of claim 6.
Hoffman further teaches wherein determining the first rating probability for each content item further comprises: dividing a number of times each content item was ranked above a threshold value by a total number of times each content item was ranked ([0087]: By running a series of computations over the information gathered from users, the system computes data structures that are used for a variety of ranking or search operations).
Regarding Claim 9, the combined teachings of Felt, Kane, and Hoffman disclose the method of claim 6.
Hoffman further teaches wherein determining the second rating probability for each content item further comprises:
determining one or more users similar to the user ([0088]-[0089]: For example, the system can track the information that users have viewed in the past, and find items that are similar or that were viewed by other people who looked at the same or similar information as the current user); and
dividing a number of times each content item was ranked above a threshold by the one or more users similar to the user by a total number of the one or more users similar to the user ([0087]: By running a series of computations over the information gathered from users, the system computes data structures that are used for a variety of ranking or search operations).
Regarding Claim 10, the combined teachings of Felt, Kane, and Hoffman disclose the method of claim 9.
Hoffman further teaches wherein the one or more users similar to the user is determined by using nearest neighbor machine learning classifiers ([0075]: With this definition of similarity, the nearest neighbors above a certain threshold are computed for each item. References to the nearest neighbors are stored with each item for later retrieval).
Regarding Claim 11, the combined teachings of Felt, Kane, and Hoffman disclose the computer-implemented method of claim 6.
Felt further teaches wherein the one or more search parameters include one or more of: a person, an entity, one or more keywords, or a time period ([0059] When the user of the mobile device selects the "search premises" option, the mobile device may detect the user selection and provide one or more tools to facilitate user input of search terms (e.g., keywords)).
Regarding Claim 12, the combined teachings of Felt, Kane, and Hoffman disclose the computer-implemented method of claim 6.
Hoffman further teaches wherein the filtering is performed using at least one named entity recognition model ([0108]: The system may perform image recognition and search for the names or characteristics of objects and people that have been recognized).
Regarding Claim 20, the combined teachings of Felt, Kane, and Hoffman disclose the system of claim 1.
Kane further teaches further comprising a notification to a user interface, via the user search module, wherein the notification presents on said user interface the one or more visitor traffic forecasts and the at least one future format ([0145]-[0149]: Once sample users are identified, content items accessed by the sample users who are similar to the accessing user and which are associated with the location can be identified to form a set of potential content items… At 1106, potential content items are ranked. Rankings may be based on any number of different parameters, such as relevance, distance, proximity, completion metrics, usage patterns, abandonment statistics, popularity, user reviews, user preference, user behavior, past viewing history, past purchase history, and so on… At 1112, the recommendations are presented to the user).
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any extension fee 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.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to SHIRLEY D. HICKS whose telephone number is (571)272-3304. The examiner can normally be reached Mon - Fri 7:30 - 4:00.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Charles Rones can be reached on (571) 272-4085. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/S D H/Examiner, Art Unit 2168
/CHARLES RONES/Supervisory Patent Examiner, Art Unit 2168
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