Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claim Rejections - 35 USC § 103
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.
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.
Claim 1-2 and 4-6 are rejected under 35 U.S.C. 103 as being unpatentable over You et al. (US 2022/0245047 A1, hereinafter You) in view of Burkard et al. (US 9443197 B1, hereinafter Burkard).
Regarding Claim 1
You teaches:
A web prediction system, comprising: (You [0050] “In some embodiments, the techniques described herein can provide for an improved GUI that executes commands faster by predicting an intent of a user session and displaying GUI elements directed toward accomplishing that intent.”; You [0054] “In many embodiments, method 400 can comprise an activity 401 of receiving in-session user activity. In various embodiments, in-session user activity can comprise interactions with a GUI that occur during a user session. For example, in-session user activity can comprise interactions with a web site during a browsing session on the website. As another example, in-session user activity can comprise interactions with a computer program that occur beginning from when the program is opened to when the program is closed. It will be understood that while many user sessions end when a GUI is closed (e.g., by navigating away from a website or closing a program), user sessions can persist after closure of the GUI.”; Examiner’s Note (EN): “GUI” reads on “web … system”)
a memory that stores data associated with a user; (You [0083] “Memory storage module 601 can be referred to as user activity receiving module 601.”)
an interface configured to interact with the user during a user session; and (You [0042] GUI 350, 351, 352 can comprise text and/or graphics (images) based user interfaces.”)
one or more processors communicatively coupled to the memory and the interface, (You Fig 2, CPU 210, Memory ROM/RAM 208, Monitor 106)
wherein the one or more processors are configured to: receive, during the user session, user activity data from the interface; (You [0054] “… activity 401 of receiving in-session user activity. In various embodiments, in-session user activity can comprise interactions with a GUI that occur during a user session. For example, in-session user activity can comprise interactions with a web site during a browsing session on the website.”).
a machine learning model trained to identify one or more web interface interactions corresponding to user activity data; (You [0060] “… activity 405 of predicting one or more intents of a user. In some embodiments, an intent of a user can be predicted using a first set of predictive algorithms. In these or other embodiments, an input for a predictive algorithm can comprise in-session activity and/or historical activity”, and “a first set of predictive algorithms can comprise one or more machine learning algorithms”; (EN): “in-session activity and/or historical activity” reads on “user activity data”).
While You teaches predicting the intent of the user, it does not distinctly disclose:
predict a likely next action that the user will take by applying the received user activity data
fetch web interface data corresponding to the likely next action; and
load the fetched web interface data into the memory prior to detecting an actual next action of the user.
However, Burkard teaches:
-predict a likely next action that the user will take by applying the received user activity data (Burkard [col. 8 ln 43-46] “Navigational intent may be any action that would tend to indicate that the user will generate a particular network request, such as a request for a particular web page.” and [col. 8 ln 58-62] “At stage 304, after receiving the indicator of navigational intent, the computing device 200 attempts to predict the most likely navigation event. In short, the computing device 200 makes a best guess of to where the user is likely to navigate next, based upon the indicator.”; (EN): “likely navigation event” reads on “likely next action”)
-fetch web interface data corresponding to the likely next action; and (Burkard [col. 7 ln 35-39 ] “ … a prerender module 210 to perform fetching of a next web page as identified by the navigation prediction module 208. The prerender module 210 sends a network request for the web page identified to be the likely next navigation destination that the user will select.)
-load the fetched web interface data into the memory prior to detecting an actual next action of the user. (Burkard [col. 8 ln 65-67, col. 9 ln 1-2] At stage 306, the computing device 200 prerenders the content from the predicted next navigation event as determined at stage 304. The prerendering process may include storing a prerendered web page within a browser, such as the prerendered web page 218.; (EN): “prerenders” reads on “prior to detecting an actual next action” )
Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art to combine the system of You for altering a graphical user interface optimized based on predicted intent, with the teachings of Burkard for predicting next user navigation event and prerendering the corresponding event data in order to minimize delay in accessing web content. (Burkard [col. 8 ln 28-35] Aspects of the method 300 operate to identify one or more likely navigation destinations from a set of navigation indicators, and then prerender the identified navigation destinations. The method 300 may be performed by a computing device, such as the computing device 200, to eliminate delays in the user web browsing experience by prerendering web pages that are identified as likely navigation targets by the user.)
Regarding Claim 2
The combination of You and Burkard teaches all of the limitations of claim 1 and You further teaches:
-wherein the user activity data includes behavioral data of the user. (You [0060] “an input for a predictive algorithm can comprise in-session activity”; (EN): “in-session activity” reads on “behavioral data”).
Regarding Claim 4
The combination of You and Burkard teaches all of the limitations of claim 1.
However, You does not distinctly disclose:
wherein the one or more processors are configured to predict a plurality of likely next actions, each ranked according to a confidence score.
Burkard further teaches:
wherein the one or more processors are configured to predict a plurality of likely next actions, each ranked according to a confidence score. (Burkard [col. 9 ln 41-43] “At stage 404, the computing device 200 determines a most likely navigation event or events based upon the user navigation history.” and [col. 9 ln 55-60] “The computing device 200 may associate each navigation event with a particular confidence value, indicating the likelihood that the user will select each navigation event. These confidence values may then be used to sort the navigation events to determine the most likely navigation event.”
Regarding Claim 5
The combination of You and Burkard teaches all of the limitations of claim 1 and You further teaches:
-wherein the user activity data includes one of behavioral data or historical data relating to the user depending on a phase of the user session. (You [0054-5] “In many embodiments, in-session user activity can be continually streamed to a database and/or a cache for storage and further processing. This cached in-session user activity can then be quickly accessed on demand to create a GUI customized to the specific user session. In various embodiments, user activity can be entered into an initial GUI. As described herein, an “initial GUI” need not be restricted to a GUI displayed when a user initially opens the GUI. “Initial GUI” is merely used to differentiate the initial GUI from subsequent GUIs (e.g., an altered GUI described in activity 411 below). For example, an initial GUI can be displayed in the middle and/or at the end of a user session. As another example, an altered GUI (as described in activity 411 below) can become an initial GUI when it is further altered according to the techniques described herein… In some embodiments, in-session user activity can be selectively aggregated with historical user activity. In various embodiments, historical user activity can comprise in-person user activity and/or the interactions with GUIs described above.”; (EN): “initial GUI”, “subsequent GUIs”, and “altered GUI” read on “phase[s] of user session”, “in-session user activity” reads on “behavioral data”)
Regarding Claim 6
The combination of You and Burkard teaches all of the limitations of claim 1 and You further teaches:
-wherein the user activity includes the historical data during an initial phase of the user session, and includes the behavioral data during an ongoing phase of the user session. (You [0054-5] “In many embodiments, in-session user activity can be continually streamed to a database and/or a cache for storage and further processing. This cached in-session user activity can then be quickly accessed on demand to create a GUI customized to the specific user session. In various embodiments, user activity can be entered into an initial GUI. As described herein, an “initial GUI” need not be restricted to a GUI displayed when a user initially opens the GUI. “Initial GUI” is merely used to differentiate the initial GUI from subsequent GUIs (e.g., an altered GUI described in activity 411 below). For example, an initial GUI can be displayed in the middle and/or at the end of a user session. As another example, an altered GUI (as described in activity 411 below) can become an initial GUI when it is further altered according to the techniques described herein… In some embodiments, in-session user activity can be selectively aggregated with historical user activity. In various embodiments, historical user activity can comprise in-person user activity and/or the interactions with GUIs described above.”; [0060] “an input for a predictive algorithm can comprise in-session activity and/or historical activity.”); (EN): “initial GUI”, “subsequent GUIs”, and “altered GUI” read on “phase[s] of user session”, “in-session user activity” reads on “behavioral data”)
Claims 3 is rejected under 35 U.S.C. 103 as being unpatentable over You in view of Burkard as set forth above and further in view of Koukoumidis et al. (US 11086858 B1, hereinafter Koukoumidis).
Regarding Claim 3
The combination of You and Burkard teaches all of the limitations of claim 1 and You further teaches:
wherein the user activity data includes at least one of historical … data relating to the user. (You [0060] an input for a predictive algorithm can comprise in-session activity and/or historical activity. (EN): “historical activity” reads on “historical data”)
You does not distinctly disclose:
environmental data relating to the user.
However, Koukoumidis teaches:
environmental data relating to the user. (Koukoumidis [col 11 ln 14-21] “The user context engine 225 may store the user profile of the user. The user profile of the user may comprise user-profile data including demographic information, social information, and contextual information associated with the user. The user-profile data may also include user interests and preferences on a plurality of topics, aggregated through conversations on news feed, search logs, messaging platform 205, etc.”, and [col 9 ln 18-24] “A user profile may include, for example, biographic information, demographic information, behavioral information, social information, or other types of descriptive information, such as work experience, educational history, hobbies or preferences, interests, affinities, or location. Interest information may include interests related to one or more categories.”; (EN): “demographic information, social information, and contextual information associated with the user” read as “environmental data relating to the user”)
Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art to combine the web prediction system of You and Burkard, with the predictive machine learning model of Koukoumidis that uses environmental data, to assist a user to better predict the user’s likely next action using external context. (Koukoumidis [col 15 ln 29-35] “In particular embodiments, the CU composer 270 may retrieve a user profile from the user context engine 225 when generating the communication content and determining the modality of the communication content. As a result, the communication content may be more natural, personalized, and context-aware for the user.” )
Claims 7 is rejected under 35 U.S.C. 103 as being unpatentable over You in view of Burkard as set forth above and further in view of Zhang (US 10560546 B2, hereinafter Zhang).
Regarding Claim 7
The combination of You and Burkard teaches all of the limitations of claim 1, but does not distinctly disclose:
- wherein the one or more processors are further configured to: detect the actual next action of the user;
- determine whether the detected actual next action of the user corresponds to a likely next action loaded into the memory; and
- generate the interface or perform a new fetch depending on the determining.
However, Zhang teaches:
- wherein the one or more processors are further configured to: detect the actual next action of the user; (Zhang [col 15 ln 4-7] “The application can monitor for user interactions with the user interface and perform actions, e.g., update data, navigate to a different tab, etc., in response to detecting the user interactions.”)
- determine whether the detected actual next action of the user corresponds to a likely next action loaded into the memory; and (Zhang [col 15 ln 64-67, col 16 ln 1-2] “The system provides the next action data to the client device for storage in a cache of the client device (408). The client device can store the next action data in the cache. If the client device detects that the predicted next action was performed, the client device can obtain the data from the cache and present the data to the user.”)
- generate the interface or perform a new fetch depending on the determining. (Zhang [col 14 ln 59-67] “The system stores the next action data in a cache of the system (310). For example, the system may include a high speed cache or other memory storage local to the system in which the system stores the next action data. By storing the next action data in local memory, the next action data can be presented more quickly if the user actually performs the predicted next action as the system would not have to wait for the request to traverse the network and the next action data to arrive.”; [col 15 ln 13-14] “The system updates the user interface to present the next action data (316).” )
Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art to combine the web prediction system of You and Burkard, with the teachings of Zhang to pre-cache data for predicted future action in order to reduce latency. (Zhang [col 2 ln 41-57] “The latency in obtaining and presenting data to a user can be reduced by pre-caching data that is likely to be requested in response to the user's next action, e.g., the user's next user interaction with an application. The latency in loading a web application can be reduced by identifying a web application that will be used to present data requested in response to the user's next action and obtaining the code, e.g., scripts, used to load the web application. Similarly, the latency in loading a mobile application can be reduced by identifying a mobile application that will be used to present data requested in response to the user's next action and loading the application in the background of the user's client device. Pre-caching data also allows the client device to present the data quicker as the client device does not have to wait for a request to traverse the network, the server to identify the requested data, and the requested data to make its way to the client device.”)
Regarding Claim 8
Claim 8 recites substantially similar limitations for claim 1 and is therefore rejected on the same basis.
Regarding Claim 9
Claim 9 recites substantially similar limitations for claim 2 and is therefore rejected on the same basis.
Regarding Claim 10
Claim 10 recites substantially similar limitations for claim 3 and is therefore rejected on the same basis.
Regarding Claim 11
Claim 11 recites substantially similar limitations for claim 4 and is therefore rejected on the same basis.
Regarding Claim 12
Claim 12 recites substantially similar limitations for claim 5 and is therefore rejected on the same basis.
Regarding Claim 13
Claim 13 recites substantially similar limitations for claim 6 and is therefore rejected on the same basis.
Regarding Claim 14
Claim 14 recites substantially similar limitations for claim 7 and is therefore rejected on the same basis.
Claims 15-19 is rejected under 35 U.S.C. 103 as being unpatentable over You in view of Koukoumidis in view of Burkard.
Regarding Claim 15
You teaches:
A non-transitory computer-readable storage medium storing instructions thereon that when read executed by one or more processors cause the one or more processors to execute functions, comprising: (You Fig 2)
receiving a user identity of a user; (You [0046] “interactions can be tied to a unique identifier (e.g., an IP address, an advertising ID, device ID, etc.) and/or a user account. In embodiments where a user 340, 341 interacts with GUIs 350, 351 before logging into a user account, data stored in the one or more database that is associated with a unique identifier can be merged with and/or associated with data associated with the user account.” (EN): “unique identifier” and “user account” reads on “user identity”)
retrieving historical … data associated with the user based on the user identity; calculating an initial next action prediction by applying the historical … data to a machine learning model trained to identify one or more web interface interactions corresponding to historical … data; (You [0060] “In many embodiments, method 400 can comprise an activity 405 of predicting one or more intents of a user. In some embodiments, an intent of a user can be predicted using a first set of predictive algorithms. In these or other embodiments, an input for a predictive algorithm can comprise in-session activity and/or historical activity.” and [0023] “selectively aggregating the in-session user activity of the user with historical activity data of the user” (EN): “retrieving” reads on “aggregating” and “next action prediction” reads on “an intent of a user can be predicted”)
receiving user activity data of the user; (You [0054] “… activity 401 of receiving in-session user activity. In various embodiments, in-session user activity can comprise interactions with a GUI that occur during a user session”).
calculating a subsequent next action prediction based on the user activity data; (You [0067] “In various embodiments, an altered GUI can become an initial GUI and the techniques described herein can be repeated using the altered GUI as the initial GUI” and [0060] “In many embodiments, method 400 can comprise an activity 405 of predicting one or more intents of a user. In some embodiments, an intent of a user can be predicted using a first set of predictive algorithms. In these or other embodiments, an input for a predictive algorithm can comprise in-session activity and/or historical activity.” (EN): “an altered GUI can become an initial GUI” reads on “subsequent next action” which differentiates it from “initial next action”)
You does not distinctly disclose:
retrieving … environmental data associated with the user based on the user identity;
calculating an initial next action prediction by applying the … environmental data to a machine learning model trained to identify one or more web interface interactions corresponding to … environmental data;
However, Koukoumidis teaches:
retrieving … environmental data associated with the user based on the user identity; [col 11 ln 14-21] “The user context engine 225 may store the user profile of the user. The user profile of the user may comprise user-profile data including demographic information, social information, and contextual information associated with the user. The user-profile data may also include user interests and preferences on a plurality of topics, aggregated through conversations on news feed, search logs, messaging platform 205, etc.”, [col 9 ln 18-24] “A user profile may include, for example, biographic information, demographic information, behavioral information, social information, or other types of descriptive information, such as work experience, educational history, hobbies or preferences, interests, affinities, or location. Interest information may include interests related to one or more categories.” [col 30 ln 33-38] “the predictive model 425 may receive a user profile from the user context engine 225 (EN): “receive” reads on “retrieve” and “demographic information, social information, and contextual information associated” read on “environmental data”)
calculating an initial next action prediction by applying the … environmental data to a machine learning model trained to identify one or more web interface interactions corresponding to … environmental data; (Koukoumidis [col 30 ln 33-38] “the predictive model 425 may receive a user profile from the user context engine 225 that may comprise previous user queries and generate speculative queries 508 based on the previous user queries and assign a confidence score to the generated speculative queries 508”, [col 28 ln 57-61]“ In particular embodiments, the predictive model 425 may generate one or more speculative queries based on the textual input 410 and send a response 430 to the request predictor 415 that comprises any generated speculative query.”, and [col 31 ln 13-16] “In particular embodiments, the predictive model 425 may use a machine-learning algorithm to refine the model to reduce the excessive processing of speculative queries”; (EN): “speculative quer[y]” reads on “next action”, “user profile” contains “environmental data” as set forth above)
Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art to combine the web prediction system of You that uses historical data, with the predictive machine learning model of Koukoumidis that uses environmental data, to assist a user to better predict the user’s likely next action using external context. (Koukoumidis [col 15 ln 29-35] “In particular embodiments, the CU composer 270 may retrieve a user profile from the user context engine 225 when generating the communication content and determining the modality of the communication content. As a result, the communication content may be more natural, personalized, and context-aware for the user.”)
You and Koukoumidis does not distinctly disclose:
fetching initial web interface data corresponding to the initial next action;
preloading the fetched initial web interface data into memory prior to detecting an actual next action of the user;
fetching subsequent data needed to carry out the subsequent next action;
and preloading the fetched subsequent data into the memory prior to determining an actual next action of the user.
However, Burkard teaches:
fetching initial web interface data corresponding to the initial next action; (Burkard [col. 7 ln 35-39 ] “ … a prerender module 210 to perform fetching of a next web page as identified by the navigation prediction module 208. The prerender module 210 sends a network request for the web page identified to be the likely next navigation destination that the user will select.” (EN): ”, “predicted next navigation event” reads on “initial next action”)
preloading the fetched initial web interface data into memory prior to detecting an actual next action of the user; (Burkard [col. 8 ln 65-67, col. 9 ln 1-2] “At stage 306, the computing device 200 prerenders the content from the predicted next navigation event as determined at stage 304. The prerendering process may include storing a prerendered web page within a browser, such as the prerendered web page 218.” (EN): “prerenders” reads on “prior to detecting an actual next action”)
fetching subsequent data needed to carry out the subsequent next action; (Burkard [col. 7 ln 35-39 ] “ … a prerender module 210 to perform fetching of a next web page as identified by the navigation prediction module 208. The prerender module 210 sends a network request for the web page identified to be the likely next navigation destination that the user will select.” (EN): “predicted next navigation event” reads on “subsequent next action”)
and preloading the fetched subsequent data into the memory prior to determining an actual next action of the user. (Burkard [col. 8 ln 65-67, col. 9 ln 1-2] “At stage 306, the computing device 200 prerenders the content from the predicted next navigation event as determined at stage 304. The prerendering process may include storing a prerendered web page within a browser, such as the prerendered web page 218.” (EN): “prerenders” reads on “prior to detecting an actual next action”)
Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art to combine the web prediction system of You and Koukoumidis that uses historical and environmental data, with the method of Burkard for prerendering the next predicted event data in order to minimize delay in accessing web content. (Burkard [col. 8 ln 28-35] Aspects of the method 300 operate to identify one or more likely navigation destinations from a set of navigation indicators, and then prerender the identified navigation destinations. The method 300 may be performed by a computing device, such as the computing device 200, to eliminate delays in the user web browsing experience by prerendering web pages that are identified as likely navigation targets by the user.)
Regarding Claim 16
The combination of You, Koukoumidis, and Burkard teaches all of the limitations of claim 15 and You further teaches:
wherein the user activity data includes behavioral data of the user. (You [0060] “an input for a predictive algorithm can comprise in-session activity”; (EN): “in-session activity” reads on “behavioral data”)
Regarding Claim 17
The combination of You, Koukoumidis, and Burkard teaches all of the limitations of claim 15.
You and Koukoumidis does not distinctly disclose:
wherein each of the initial next action prediction and the subsequent next action prediction predict a plurality of likely next actions, each ranked according to a confidence score.
However, Burkard further teaches:
wherein each of the initial next action prediction and the subsequent next action prediction predict a plurality of likely next actions, each ranked according to a confidence score. (Burkard [col. 9 ln 41-43] “At stage 404, the computing device 200 determines a most likely navigation event or events based upon the user navigation history.” and [col. 9 ln 55-60] “The computing device 200 may associate each navigation event with a particular confidence value, indicating the likelihood that the user will select each navigation event. These confidence values may then be used to sort the navigation events to determine the most likely navigation event.” (EN): “likely navigation event” reads on “the initial next action prediction” and “subsequent next action prediction”)
Regarding Claim 18
The combination of You, Koukoumidis, and Burkard teaches all of the limitations of claim 15 and You further teaches:
wherein the initial next action prediction or the subsequent next action is performed based on a phase of a user session. (You [0067] “In various embodiments, an altered GUI can become an initial GUI and the techniques described herein can be repeated using the altered GUI as the initial GUI” and [0060] “In many embodiments, method 400 can comprise an activity 405 of predicting one or more intents of a user. In some embodiments, an intent of a user can be predicted using a first set of predictive algorithms. In these or other embodiments, an input for a predictive algorithm can comprise in-session activity and/or historical activity.” (EN): “an altered GUI can become an initial GUI” reads on the method to predict the “subsequent next action” being synonymous with one for “initial next action”)
Regarding Claim 19
The combination of You, Koukoumidis, and Burkard teaches all of the limitations of claim 18 and You further teaches:
wherein the initial next action prediction is performed during an initial phase of the user session and wherein the subsequent next action prediction is performed during an ongoing phase of the user session. (You [0067] “In various embodiments, an altered GUI can become an initial GUI and the techniques described herein can be repeated using the altered GUI as the initial GUI” and [0060] “In many embodiments, method 400 can comprise an activity 405 of predicting one or more intents of a user. In some embodiments, an intent of a user can be predicted using a first set of predictive algorithms. In these or other embodiments, an input for a predictive algorithm can comprise in-session activity and/or historical activity.” (EN): “initial GUI” reads on “initial phase” and “altered GUI” reads on “ongoing phase”)
Claims 20 is rejected under 35 U.S.C. 103 as being unpatentable over You in view of Koukoumidis in view of Burkard in view of Zhang.
Regarding Claim 20
The combination of You, Koukoumidis, and Burkard teaches all of the limitations in claim 15.
However, You does not distinctly disclose:
generating a user interface from the data preloaded into the memory.
Burkard further teaches:
generating a user interface from the data preloaded into the memory. Burkard [col. 8 ln 65-67, col. 9 ln 1-2] At stage 306, the computing device 200 prerenders the content from the predicted next navigation event as determined at stage 304. The prerendering process may include storing a prerendered web page within a browser, such as the prerendered web page 218.; (EN): this paragraph denotes updating the web page/ GUI using the preloaded data)
However, the combination of You, Koukoumidis, and Burkard does not distinctly disclose:
wherein the functions further comprise: detecting the actual next action of the user;
determining that the actual next action corresponds to the predicted next action; and
However, Zhang teaches:
wherein the functions further comprise: detecting the actual next action of the user; (Zhang [col 15 ln 4-7] “The application can monitor for user interactions with the user interface and perform actions, e.g., update data, navigate to a different tab, etc., in response to detecting the user interactions.”)
determining that the actual next action corresponds to the predicted next action; and (Zhang [col 15 ln 1-2] “The system detects an occurrence of the predicted next action at the user interface (312)” and [col 15 ln 66-67, col 16 ln 1-2] “If the client device detects that the predicted next action was performed, the client device can obtain the data from the cache and present the data to the user.” EN: these passages denote the ability for the system of Zhang to be able to determine whether the next action is the predicted one)
Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art to combine the web prediction system of You, Koukoumidis, and Burkard, with the method of Zhang to pre-cache data for predicted future action in order to reduce latency. (Zhang [col 2 ln 41-57] “The latency in obtaining and presenting data to a user can be reduced by pre-caching data that is likely to be requested in response to the user's next action, e.g., the user's next user interaction with an application. The latency in loading a web application can be reduced by identifying a web application that will be used to present data requested in response to the user's next action and obtaining the code, e.g., scripts, used to load the web application. Similarly, the latency in loading a mobile application can be reduced by identifying a mobile application that will be used to present data requested in response to the user's next action and loading the application in the background of the user's client device. Pre-caching data also allows the client device to present the data quicker as the client device does not have to wait for a request to traverse the network, the server to identify the requested data, and the requested data to make its way to the client device.”)
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to JIAHE NIU whose telephone number is (571)270-0152. The examiner can normally be reached 8am-5pm.
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/JIAHE NIU/Examiner, Art Unit 2128
/OMAR F FERNANDEZ RIVAS/Supervisory Patent Examiner, Art Unit 2128
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