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 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.
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 1, 5, 9, 10, 14 and 18 are rejected under 35 U.S.C. 103 as being unpatentable over Zhang W0 2018/125276 (cited by Applicant)in further view of Hu (US 20170344338 A1).
(Claim 1) A method implemented by one or more processors, the method comprising:
(Claim 10) A system comprising: memory storing instructions; one or more processors operable to execute the instructions to ([0101] Processors suitable for the execution of a computer program include, by way of example, both general and special purpose microprocessors, and any one or more processors of any kind of digital computer. Generally, a processor will receive instructions and data from a read only memory or a random access memory or both.):
transmitting, to a third-party server device, an action advancement data request for one or more predicted actions of a user (Zhang Fig.1 shows server);
receiving, from the third-party server device (Zhang Fig.1 shows server)and in response to transmitting the action advancement data request, action advancement data for the one or more predicted actions of the user (¶[0003]The data processing apparatus can receive, over the data communication network, the next action data and store the next action data in the cache. Other implementations of this aspect include corresponding apparatus, methods, systems, and computer programs, configured to perform the actions of the methods, encoded on computer storage devices.);
caching the action advancement data ([0011] Particular embodiments of the subject matter described in this specification can be implemented so as to realize one or more of the following advantages. 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);
subsequent to caching the action advancement data:
receiving [[natural language]] input from the user ()[0007] In some aspects, the data processing apparatus of the client device can detect a trigger event for determining the predicted next action. The predicted next action can be determined in response to detecting the trigger event.;
processing the [[natural language]] input to determine that the natural language input requests an automated assistant ([0101]Moreover, a computer can be embedded in another device, e.g., a mobile telephone, a personal digital assistant (PDA),) to perform an action of the one or more predicted actions (¶[0003]The data processing apparatus can receive, over the data communication network, the next action data and store the next action data in the cache. Other implementations of this aspect include corresponding apparatus, methods, systems, and computer programs, configured to perform the actions of the methods, encoded on computer storage devices, ¶[0074] The system determines a predicted user action based on the user interface context (304). The predicted user action may be an action that a user of the application is likely to perform at the user interface based on the user interface context. For example, the predicted user action may be an action having at least a threshold probability of being performed by the user. In another example, the predicted user action may be a user action having a highest probability among multiple possible user actions that can be performed at the user interface.)
in response to determining that the natural language input requests the automated assistant to perform the action of the one or more predicted actions: using the cached action advancement data in performing the action responsive to the request, wherein the cached action advancement data is used in performing the action responsive to the action advancement data being cached and being for the one or more predicted actions of the user ([0074] The system determines a predicted user action based on the user interface context (304). The predicted user action may be an action that a user of the application is likely to perform at the user interface based on the user interface context. For example, the predicted user action may be an action having at least a threshold probability of being performed by the user. In another example, the predicted user action may be a user action having a highest probability among multiple possible user actions that can be performed at the user interface.)
Zhang does not explicitly disclose however Hu teaches natural language input ([0009] Embodiments of the present invention provide a system for identifying user preferences and changing settings of a device (such as a mobile phone, tablet, and laptop) based on natural language processing. As a user is engaged in normal use of the device, the system applies user preferences to all applications on the device. Any natural language input (such as speech and texting) from a user of the device can be captured in real time by the system on the device. With existing technologies of natural language processing (NLP) and semantic analysis, the system extracts user's preferences.)
It would have been obvious to one of ordinary skill in the art prior to the effective filing date of the invention to modify action advancement of Zhang to include the natural language of of Hu in order to interact in real-time ([0009], Hu);
With respect to claims 5 and 14 Zhang teaches prior to transmitting the action advancement data request: determining a probability of the user requesting performance of the one or more predicted actions ([0074] For example, the predicted user action may be an action having at least a threshold probability of being performed by the user. In another example, the predicted user action may be a user action having a highest probability among multiple possible user actions that can be performed at the user interface.) ;
wherein transmitting the action advancement data request is based on the probability ([0078] In some implementations, the system may select multiple predicted next actions for which to pre-cache data. For example, multiple predicted next actions may have at least a threshold probability of being performed by the user. In another example, the system may select a specified number of the actions that have the highest probabilities. In this way, the system is more likely to obtain and pre- cache the data for the user before the user performs the action.)
With respect to claims 9 and 18, Hu further teaches wherein the one or more processors are of a client computing device via which the natural language input is received ([0005] In yet another aspect, a computer system for identifying user preferences and changing settings of a device based on natural language processing is provided. The computer system comprises one or more processors, one or more computer readable tangible storage devices, and program instructions stored on at least one of the one or more computer readable tangible storage devices for execution by at least one of the one or more processors. The program instructions are executable to capture, by one or more programs running in background on the device, an input of natural language from a user of the device.)
It would have been obvious to one of ordinary skill in the art prior to the effective filing date of the invention to modify action advancement of Zhang to include the natural language of Hu in order to interact in real-time ([0009], Hu);
Claims 2 and 11 are rejected under 35 U.S.C. 103 as being unpatentable over Zhang, Hu in further view of Brickell (US 20180007140 A1).
With respect to claims 2 and 11, Zhang, Hu do not explicitly disclose however Brickell teaches wherein the action includes interaction with an Internet of Things (IoT) device and wherein the action advancement data includes data for establishing a network connection with the IoT device (¶[0096] Example 24 is an IoT device comprising: means for reading a temporary network configuration from a memory of the first device, the temporary network configuration stored in the memory of the first device prior to delivery of the first device to a customer; means for searching for a temporary network created by a second device and corresponding to the temporary network configuration; means for connecting to the temporary network responsive to finding the temporary network; means for authenticating with a remote configuration server; means for receiving a configuration from the remote configuration server responsive to authenticating with the remote configuration server; and means for applying the configuration.)
It would have been obvious to one of ordinary skill in the art prior to the effective filing date of the invention to modify action advancement of Zhang in view of the natural language of Hu to include IoT devices of Brickell in order to anticipate control of IoT devices;
Claims 3 and 12 are rejected under 35 U.S.C. 103 as being unpatentable over Zhang, Hu, Brickell in further view of Wolfson (US 20170199749 A1).
With respect to claims 3 and 12, Zhang, Hu and Brickell do not explicitly disclose however Wolfson teaches wherein the action includes rendering graphical data and wherein the action advancement data includes data includes data utilized in generating the graphical data ([0004] In some implementations, a computing device can present dynamic graphical user interface previews of an application on a display of the computing device. For example, cached graphical user interface (GUI) content can be dynamically generated in response to receiving application data update information for the corresponding application.)
It would have been obvious to one of ordinary skill in the art prior to the effective filing date of the invention to modify action advancement of Zhang in view of the natural language of Hu in view of IoT of Brickell to include graphical data of Wolfson in order to improve the user experience (Wolfson, [0003]);
Claims 4 and 13 are rejected under 35 U.S.C. 103 as being unpatentable over Zhang, Hu, in further view of Wolfson (US 20170199749 A1).
With respect to claims 4 and 13, Zhang, Hu do not explicitly disclose however Wolfson teaches wherein the action includes rendering graphical data and wherein the action advancement data includes data includes data utilized in generating the graphical data ([0004] In some implementations, a computing device can present dynamic graphical user interface previews of an application on a display of the computing device. For example, cached graphical user interface (GUI) content can be dynamically generated in response to receiving application data update information for the corresponding application.)
It would have been obvious to one of ordinary skill in the art prior to the effective filing date of the invention to modify action advancement of Zhang in view of the natural language of Hu to include graphical data of Wolfson in order to improve the user experience (Wolfson, [0003]);
Claims 6 and 15 are rejected under 35 U.S.C. 103 as being unpatentable over Zhang, Hu, in further view of Chen (US 20190102784 A1).
With respect to claims 6 and 15 Zhang and Hu do not explicitly disclose however Chen teaches wherein determining the probability of the user requesting performance of the one or more predicted actions comprises processing one or more attributes, that are specific to the user, using a trained machine learning model to generate the probability ([0007] First, user characteristics of the user involved in the impression opportunity are applied to a set of models associated with the content campaign to predict a set of user actions after viewing the content item, each model predicting a likelihood of the user performing a respective user action in the set of user actions, ¶[0053] The model training module 302 uses supervised machine learning to train the models, with the feature vectors for impression events serving as the inputs. Different machine learning techniques—such as linear support vector machine)
It would have been obvious to one of ordinary skill in the art prior to the effective filing date of the invention to modify action advancement of Zhang in view of the natural language of Hu to include machine learning model of Chen in order to better predict user actions based (Chen, [0015]);
Claims 7 and 16 are rejected under 35 U.S.C. 103 as being unpatentable over Zhang, Hu, in further view of Begeja (US 20170280434 A1).
With respect to claims 7 and 16 Zhang, Hu do not explicitly disclose however Begeja teaches wherein caching the action advancement data comprises determining an amount of time for caching the action advancement data and caching the action advancement data for the amount of time (¶0043] The pre-caching engine 101 may determine that the user device pre-cache indicator 161 indicates that the particular media data 133 is to be pre-cached at the first user device 107, ¶[0054] The pre-caching engine 101 may generate the retention duration threshold 164 indicating that the particular media data 133 is to be retained in the memory 132 until at least the second request time 194. In a particular aspect, the retention duration threshold 164 may indicate that the particular media data 133 is to be retained in the memory 132 until a second time (e.g., Monday at 8:15 AM) that is subsequent to the second request time 194. The second time may be based on the second request time 194 and an error margin.)
It would have been obvious to one of ordinary skill in the art prior to the effective filing date of the invention to modify action advancement of Zhang in view of the natural language of Hu to include caching time of Begeja in order to reduce a latency in providing data(Begeja, [0055]);
Claims 8 and 17 are rejected under 35 U.S.C. 103 as being unpatentable over Zhang, Hu, Begeja in further view of Fukami (US 20190297142 A1).
With respect to claims 8 and 17, none of Zhang, Hu and Begeja explicitly disclose, however Fukami teaches wherein determining the amount of time is based on predicted computational obligations (¶[0034]When the cache valid time 505 has elapsed, the client terminal 10 discards the local record and performs name resolution again. The load prediction/control unit 303 can dynamically set the cache valid time 505 according to the load situation of each server 40.)
It would have been obvious to one of ordinary skill in the art prior to the effective filing date of the invention to modify action advancement of Zhang in view of the natural language of Hu in view of caching times of Begeja to include computational obligations of Fukami in order to optimally set cache valid time (Fukami, [0085]);
Conclusion
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/ATHAR N PASHA/ Primary Examiner, Art Unit 2657