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 .
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
Response to Arguments
Applicant's arguments filed 01/23/26 have been fully considered but they are not persuasive.
On pages 9-10, applicant argues that Sato discloses a user characteristics vector derived from user attributes, and not “viewership history”.
In response, it is first noted that Des Jardin’s was relied upon for the specific teachings of “a user’s viewership history, the viewership history comprising content viewing and interaction data of the user” (historical data including content viewing and interaction; see Des Jardins at paragraph 29-32, 52, 64). Thus, the combination would meet the current claim limitations.
Second, Sato explicitly discloses “a user’s viewership history” as a user behavior history ([0059] The user behavior history may include, for example, a book purchase history, a video viewing history, or a restaurant visit history.)
Thus, while the example behavior history data including user attributes, shown in Fig. 12 of Sato, may be a “behavior history in a document browsing system in a company” (see Sato at paragraph 106-108), Sato explicitly discloses wherein the user behavior history may instead include a video viewing history (as described in paragraph 59). Thus, the vectors are derived from attributes described within and taken from user behavior history comprising a video viewing history.
While applicant’s claim generally recites “a matrix factorization that combines the viewership history and the real-time weather information”, there is no further description or requirements regarding to what forms of data are being combined.
Thus as the user behavior history of Sato (shown in Fig. 12) is a video viewing history, all of the attributes contained within and selectable for combining as vectors constitute “viewership history”.
As seen in Fig. 14 and 19, different attribute vectors are combined to create characteristic vectors (such as the context attribute vectors combined to create a context characteristic vector; paragraph 120, 145-146).
Thus, a “weather” context attribute vector (paragraph 54) is combined with a different user behavior context attribute vector (such as time, day or day of the week; Fig. 12, paragraph 54, 107) to create a “context characteristic vector” (Fig. 19, paragraph 145-146).
Therefore, applicant’s arguments are not convincing, as Sato clearly discloses a specific example of creating a “user-weather combined” vector, as the combined user context characteristic vector includes multiple items of content data corresponding to the user, including weather.
The current claims do not recite any limitation or requirements as to what form the “viewership history” data takes. As described by Sato, the viewership history data to be combined with the weather vector is a day or time vector. Both of these values comprise “viewership history” data as they represent the days and times the user viewed the video programming.
On page 10-11, applicant argues that “The Office Action does not identify any specific data structure in Sato that constitutes the claimed "user-weather combined vector." Applicant respectfully submits that no such combined vector exists in Sato.”
In response, see above regarding the creation of the “context characteristic vector” as disclosed by Sato.
The current claims do not include any specific requirements as to what form the “viewership history” or “real-time weather information” takes or what form the combined vector takes and what specific information is included within that vector.
For example, it is noted that applicant’s specification describes combining the user's viewership history and the weather data to create a “user’s modified preference” and then deriving a user-weather combined vector from the “user’s modified preference”. (see paragraph 68 of applicant’s specification).
Thus, the rejection under Sato discloses a user behavior history combining plural forms of information about the user, including time and weather they performed different actions (Fig. 12, paragraph 54, 59, 106-108).
Matrix factorization is applied to different attribute vectors to create a “combined” vector.
On page 11, of applicant’s response, applicant argues that “Sato Does Not Teach a Vector "Representing a Modified Preference of the User Under
the Weather Condition."”
In response, as indicated above, Sato discloses creating a context characteristic vector combining both weather and another viewing context, such as day or time. This represents a “modified preference of the user under the weather condition” as it is a combined vector modifying the weather viewing preference with a “time of day” or “day of the week” preference, thus indicating the user viewing preferences on specific days and time under different weather conditions.
This context characteristic vector is used with other vectors, such as the item characteristic vector, to determine the viewing probability for the content item for suggestion purposes. The resulting probability itself is not the “vector” relied upon the meet the current claim limitations.
While applicant’s specification discloses a “combined vector” comprising much more viewership information than that disclosed by Sato, this is not required by the current claim language. Amendments specifically capturing the plural different forms of viewership history combined within the vector, such as disclosed in paragraph 58, 66, of applicant’s specification, would overcome the rejections.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 18-20 are rejected under 35 U.S.C. 103 as being unpatentable over Des Jardins et al. (Des Jardins) (US 2015/0370818) (of record) in view of Sato et al. (Sato) (US 2025/0021848) (of record).
As to claim 18, while Des Jardins discloses a system (Fig. 1, 3) comprising:
one or more processors (paragraph 20); and
one or more memories storing instructions that, when executed by the one or more processors (paragraph 20), cause the system to perform operations comprising:
obtaining a user’s viewership history, the viewership history comprising content viewing and interaction data of the user (historical data including content viewing and interaction; paragraph 29-32, 52, 64);
obtaining real-time weather information relevant to a weather condition at a geographical location of the user (paragraph 43, 57, 62, 72-74); and
dynamically generating, for presentation, a content recommendation using a recommendation algorithm and based on the viewership history and the real-time weather information (content recommendations based upon historical viewing and current weather conditions; Fig. 4-5; paragraph 37, 43, 45, 57, 62, 74-72, 100), wherein the recommendation algorithm includes a machine learning model that is trained to adapt recommendations based on patterns identified in viewership information and corresponding weather information (machine learning algorithm to analyze historical information to determine viewing patterns; paragraph 29, 37, 41, 57, 61, 73, 98-100),
and the machine learning model is configured to produce data comprising:
an identification of at least one weather themed content item that is thematically related to the weather condition for inclusion in the content recommendation (snow, winter and Christmas content on a snowy day; paragraph 74, 89),
they fail to specifically disclose an identification of latent features representing interactions between the viewership history and the weather condition based upon a matrix factorization that combines the viewership history and the real-time weather information to create a user-weather combined vector representing a modified preference of the user under the weather condition, and inclusion of the content item in the content recommendation based on the matrix factorization.
Additionally, in an analogous art, Sato discloses a system for personalizing content for presentation to a user (paragraph 53-56, 188, 199) wherein a machine learning model will produce an identification of latent features representing interactions between viewership history and real-time weather information based upon a matrix factorization that combines the viewership history, the viewership history comprising content viewing and interaction data of the user (paragraph 59) and the real-time weather information (paragraph 54) to create a user-weather combined vector representing a modified preference of the user under the weather condition (matrix factorization of user behavior vector and context vector, including weather status to identify probable user behaviors; Fig. 1, 12, 18-19; paragraph 54-60, 106-111, 142-153) and inclusion of the content item in a content recommendation based on the matrix factorization (used in suggestion model to for probability the user will desire the item; paragraph 53-56, 153, 188, 199) so as to improve the performance of the suggestion system and the realization of the performance evaluation with high reliability (paragraph 198-199).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Des Jardins’ system to include an identification of latent features representing interactions between the viewership history and the weather condition based upon a matrix factorization that combines the viewership history and the real-time weather information to create a user-weather combined vector representing a modified preference of the user under the weather condition, and inclusion of the content item in the content recommendation based on the matrix factorization, as taught in combination with Sato, for the typical benefit of improving the performance of the suggestion system and the realization of the performance evaluation with high reliability (paragraph 198-199).
As to claim 19, while Des Jardins discloses a system (Fig. 1, 3) comprising:
a data storage device for storing users' viewership histories, the viewership history comprising content viewing and interaction data of the user (historical data including content viewing and interaction; paragraph 29-32, 52, 64);
one or more processors (paragraph 20) equipped with a machine learning algorithm (paragraph 57), wherein the one or more processors are configured to perform, based on the machine learning model, operations including:
fetching weather information in real-time indicative to a weather condition at a geographical location of a user (paragraph 38, 43, 57, 62, 72-74, 94),
identifying at least one weather themed content item that is thematically related to the weather condition (snow, winter and Christmas content on a snowy day; paragraph 74, 89),
dynamically generating a content recommendation including the at least one weather theme related content item (content recommendations based upon historical viewing and current weather conditions; Fig. 4-5; paragraph 37, 43, 45, 57, 62, 74-74, 89, 100); and
a transmission device configured to transmit the generated content recommendation to a user terminal device (Fig. 4-5; paragraph 37, 43, 45, 57, 62, 74-72, 100), they fail to specifically disclose
they fail to specifically disclose an identification of latent features representing interactions between the viewership history and the weather condition based upon a matrix factorization that combines the viewership history and the real-time weather information to create a user-weather combined vector representing a modified preference of the user under the weather condition, and inclusion of the content item in the content recommendation based on the matrix factorization.
Additionally, in an analogous art, Sato discloses a system for personalizing content for presentation to a user (paragraph 53-56, 188, 199) wherein a machine learning model will produce an identification of latent features representing interactions between viewership history and real-time weather information based upon a matrix factorization that combines the viewership history, the viewership history comprising content viewing and interaction data of the user (paragraph 59) and the real-time weather information (paragraph 54) to create a user-weather combined vector representing a modified preference of the user under the weather condition (matrix factorization of user behavior vector and context vector, including weather status to identify probable user behaviors; Fig. 1, 12, 18-19; paragraph 54-60, 106-111, 142-153) and inclusion of the content item in a content recommendation based on the matrix factorization (used in suggestion model to for probability the user will desire the item; paragraph 53-56, 153, 188, 199) so as to improve the performance of the suggestion system and the realization of the performance evaluation with high reliability (paragraph 198-199).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Des Jardins’ system to include an identification of latent features representing interactions between the viewership history and the weather condition based upon a matrix factorization that combines the viewership history and the real-time weather information to create a user-weather combined vector representing a modified preference of the user under the weather condition, and inclusion of the content item in the content recommendation based on the matrix factorization, as taught in combination with Sato, for the typical benefit of improving the performance of the suggestion system and the realization of the performance evaluation with high reliability (paragraph 198-199).
As to claim 20, Des Jardins and Sato disclose wherein: the transmission device is configured to communicate, via a receiving device, with the user terminal device (see Des Jardins at Fig. 2, paragraph 22, 26), and the operations further include:
determining the geographical location of the user based on information of the receiving device (see Des Jardins at paragraph 38).
Claims 1-8, 10-14, 16-17 are rejected under 35 U.S.C. 103 as being unpatentable over Des Jardins in view of Ding et al. (Ding) (US 2020/0288205) (of record) and Sato.
As to claim 1, while Des Jardins discloses a computer-implemented method for dynamic content recommendation (see Fig. 1, 3; paragraph 3-5), comprising
accessing a user's viewership history stored in non-transitory memory, the viewership history comprising content viewing and interaction data of the user (historical data including content viewing and interaction; paragraph 29-32, 52, 64);
obtaining real-time weather information relevant to a weather condition at a geographical location of the user (paragraph 43, 57, 62, 72-74); and
dynamically generating, for presentation, a content recommendation using a recommendation algorithm and based on the viewership history and the real-time weather information (content recommendations based upon historical viewing and current weather conditions; Fig. 4-5; paragraph 37, 43, 45, 57, 62, 74-72, 100), wherein the recommendation algorithm includes a machine learning based collaborative filtering algorithm that is trained to adapt recommendations based on patterns identified in viewership information and corresponding weather information (machine learning algorithm to analyze historical information to determine viewing patterns; paragraph 29, 37, 41, 57, 61, 73, 98-100), and the collaborative filtering algorithm is configured to produce data comprising:
an identification of at least one weather themed content item that is thematically related to the weather condition for inclusion in the content recommendation (snow, winter and Christmas content on a snowy day; paragraph 74, 89),
Des Jardins fails to specifically disclose a neural network based collaborative filtering algorithm, an identification of latent features representing interactions between the viewership history and the weather condition based upon a matrix factorization that combines the viewership history and the real-time weather information to create a user-weather combined vector representing a modified preference of the user under the weather condition, and inclusion of the content item in the content recommendation based on the matrix factorization.
In an analogous art, Ding discloses a system for providing improved recommendations for multimedia content (paragraph 4, 56; Fig. 2) wherein the recommendation algorithm includes a neural network based collaborative filtering algorithm that is trained to adapt recommendations based on patterns identified in viewership information and corresponding weather information (Fig. 6, paragraph 49, 85, 137, 131) so as to provide improved recommendation accuracy and performance and eliminate problems with “cold start” of newly added resources (paragraph 65, 133-134, 142).
Additionally, in an analogous art, Sato discloses a system for personalizing content for presentation to a user (paragraph 53-56, 188, 199) wherein a machine learning model will produce an identification of latent features representing interactions between viewership history and real-time weather information based upon a matrix factorization that combines the viewership history, the viewership history comprising content viewing and interaction data of the user (paragraph 59) and the real-time weather information (paragraph 54) to create a user-weather combined vector representing a modified preference of the user under the weather condition (matrix factorization of user behavior vector and context vector, including weather status to identify probable user behaviors; Fig. 1, 12, 18-19; paragraph 54-60, 106-111, 142-153) and inclusion of the content item in a content recommendation based on the matrix factorization (used in suggestion model to for probability the user will desire the item; paragraph 53-56, 153, 188, 199) so as to improve the performance of the suggestion system and the realization of the performance evaluation with high reliability (paragraph 198-199).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Des Jardins’ system to include a neural network based collaborative filtering algorithm, as taught in combination with Ding, for the typical benefit of providing improved recommendation accuracy and performance and eliminate problems with “cold start” of newly added resources.
Additionally, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Des Jardins and Ding’s system to include an identification of latent features representing interactions between the viewership history and the weather condition based upon a matrix factorization that combines the viewership history and the real-time weather information to create a user-weather combined vector representing a modified preference of the user under the weather condition, and inclusion of the content item in the content recommendation based on the matrix factorization, as taught in combination with Sato, for the typical benefit of improving the performance of the suggestion system and the realization of the performance evaluation with high reliability (paragraph 198-199).
As to claim 2, Des Jardins, Ding and Sato disclose wherein the real-time weather information is obtained via an automated data retrieval system interfacing with external weather data sources (see Des Jardins at paragraph 62).
As to claim 3, Des Jardins, Ding and Sato disclose receiving user feedback through a user interface (see Des Jardins at paragraph 99); and
updating the content recommendation based on the user feedback (see Des Jardins at paragraph 99-100).
As to claim 4, Des Jardins, Ding and Sato disclose wherein the content recommendation is specific to digital media (digital content sources, such as via Internet; see Des Jardins at paragraph 26, 28).
As to claim 5, Des Jardins, Ding and Sato disclose identifying content available at the geographical location (see Des Jardins at paragraph 35, 81-82); and
evaluating the identified content against the user's viewership history and real-time weather information, wherein the generated content recommendation comprises content to watch in real-time or within a time window based on the evaluation (see Des Jardins at paragraph 45-46, 48-51, 62, 74, 80, 89, 94).
As to claim 6, Des Jardins, Ding and Sato disclose notifying the user of an upcoming content that aligns with the user’s viewership history and the weather information (see Des Jardins at paragraph 45-46, 48-51, 62, 74, 80, 89, 94).
As to claim 7, Des Jardins, Ding and Sato disclose wherein the generated content recommendation comprises a plurality of content items, the method further comprising ranking the plurality of content items based on at least one of their respective relevance to the weather information or the user's viewership history (see Des Jardins at paragraph 83, 89-91).
As to claim 8, Des Jardins, Ding and Sato disclose wherein the neural network based collaborative filtering algorithm is configured to produce the identification of the at least one weather themed content item by:
applying a similarity metric to compare the user-weather combined vector with factorized attributes of candidate content items (see Sato at Fig. 18-19, paragraph 142-153 and Des Jardins at paragraph 45-46, 48-51, 62, 74, 80, 89, 94); and
selecting the at least one weather-themed content item for inclusion in the content recommendation based on the similarity metric indicating alignment with the user-weather combined vector (see Des Jardins at paragraph 74, 89).
As to claim 10, Des Jardins, Ding and Sato disclose integrating the real-time weather information into the content recommendation (content recommendations about the particular weather type, such as snowy day programming and “storm preparation”; see Des Jardins at paragraph 62, 74, 94-95).
As to claim 11, Des Jardins, Ding and Sato disclose wherein the content recommendation includes at least one content item of linear content, on-demand content, or streaming content (see Des Jardins at paragraph 82).
As to claim 12, Des Jardins, Ding and Sato disclose recommending a set of weather-appropriate activities to the user based on the real-time weather information, the user's viewership history, and/or the user's geographical location (see Des Jardins at paragraph 62, 74).
As to claim 13, Des Jardins, Ding and Sato disclose providing an educational content or advice related to weather preparedness based on the real-time weather information, the user's viewership history, and/or the user's geographical location (programming related to storm preparation and melting ice with salt; see Des Jardins at paragraph 74).
As to claim 14, Des Jardins, Ding and Sato disclose retrieving the geographical location of the user based on a location of a receiving device through which the generated content recommendation is received for presentation to the user (see Des Jardins at paragraph 38).
As to claim 16, Des Jardins, Ding and Sato disclose monitoring the real-time weather information with respect to a change in the weather condition (see Des Jardins at paragraph 62, 94); and adjusting the content recommendation based on a change in the weather condition that is detected during the monitoring (adjusting the recommendation time period and content durations based upon weather conditions; see Des Jardins at paragraph 62, 94).
As to claim 17, Des Jardins, Ding and Sato disclose receiving authentication information from the user (user device registration via account; see Des Jardins at paragraph 69); and
retrieving the user viewership history based on the authentication information (retrieving user profile corresponding to registered user device; see Des Jardins at paragraph 69).
Claim 9 is rejected under 35 U.S.C. 103 as being unpatentable over Des Jardins, Ding and Sato and further in view of Philbrick et al. (Philbrick) (US 2016/0335725) (of record).
As to claim 9, while Des Jardins, Ding and Sato disclose providing the real-time weather information, including content related to storm preparation (see Des Jardins at paragraph 74), Des Jardins fails to specifically disclose generating a weather-related alert to the user based on the real-time weather information, wherein the weather-related alert comprises a notification about at least one of a severe weather condition, a weather forecast change, or a safety warning.
In an analogous art, Philbrick discloses a system for providing content (Fig. 1-2; paragraph 11-14) which will determine the user location (paragraph 46) and generate a weather-related alert to the user based on real-time weather information (paragraph 48-50), wherein the weather-related alert comprises a notification about at least one of a severe weather condition, a weather forecast change, or a safety warning (providing an alert indicating the type of weather condition and safety actions to take for the weather; paragraph 48-52) so as to provide an enhancement to typical weather information through customized weather preparedness tips and/or advice to customer and/or subscribers (paragraph 13).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Des Jardins’ system to include generating a weather-related alert to the user based on the real-time weather information, wherein the weather-related alert comprises a notification about at least one of a severe weather condition, a weather forecast change, or a safety warning, as taught in combination with Philbrick, for the typical benefit of providing an enhancement to typical weather information through customized weather preparedness tips and/or advice to customer and/or subscribers (paragraph 13).
Claim 15 is rejected under 35 U.S.C. 103 as being unpatentable over Des Jardins, Ding and Sato and further in view of Gonzalez et al. (Gonzalez) (US 2018/0053121) (of record).
As to claim 15, while Des Jardins discloses activities based upon the current weather (staying home vs. travel and travel times based upon the weather; see Des Jardins at paragraph 74, 94),
they fail to specifically disclose providing a real-time alert to the user regarding a weather change relevant to a planned activity; and generating a suggestion including alternative activities or content based on these weather changes.
In an analogous art, Gonzalez discloses a system (Fig. 1) which will monitor weather updates to provide a real-time alert to the user regarding a weather change relevant to a planned activity (paragraph 28, 30) and generate a suggestion including alternative activities or content based on these weather changes (paragraph 28, 30-31, 40) so as to provide information in real time such that the user may travel with the least disruption by provided updated information based upon weather changes (paragraph 28, 30).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Des Jardins’ system to providing a real-time alert to the user regarding a weather change relevant to a planned activity; and generating a suggestion including alternative activities or content based on these weather changes, as taught in combination with Gonzalez, for the typical benefit of providing information in real time such that the user may travel with the least disruption by provided updated information based upon weather changes (paragraph 28, 30).
Conclusion
THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to James R Sheleheda whose telephone number is (571)272-7357. The examiner can normally be reached M-F 8 am-5 pm CST.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Benjamin Bruckart can be reached at (571) 272-3982. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/James R Sheleheda/ Primary Examiner, Art Unit 2424