DETAILED ACTION
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 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.
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 of this title, 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 1-4, 7, 15-16 and 18 are rejected under 35 U.S.C. 103 as being unpatentable over Izquierdo Domenech et al. (U.S. 20210142368) in view of Bennett (U.S. 20090287683).
For claim 1, Izquierdo Domenech et al. disclose a computer-implemented method comprising:
obtaining, on behalf of each user of a plurality of users in an operational environment, information related to a plurality of different performance aspects of the user in the operational environment from a plurality of information sources (at least [0011]-[0012], [0026] and claim 7. Obtaining browsing data from users, using one or more of: cookies, urls, sessions, login, HSTS supercookies, browser fingerprint systems.);
integrating information obtained from the plurality of information sources (at least [0003]. Digital marketing uses data management platforms (DMPs) that allow advertisers to create targeted audiences to target, based on a combination of data from different sources);
tracking user events associated with each of the plurality of users to build a user profile in a data storage for each of the plurality of users based on information obtained from the plurality of information sources and the user events, the user events involving interactions users have with information, equipment and software related to the plurality of different performance aspects in the operational environment (at least [0011]-[0012], [0026] and claim 7. Obtaining browsing data from users, using one or more of: cookies, urls, sessions, login, HSTS supercookies, browser fingerprint systems, network footprints and geolocation, to create a personal profile of each user, or of a group of users);
in response to a request, analyzing the user profile of a particular user of the plurality of users, to derive one or more applicable information modules for the particular user (at least [0011]-[0012], [0026] and claims 7-9. Receiving user data through a data management platform (DMP) and, once identified, either in a new access and/or when they continue browsing for a certain time, serving the video file to the user's browser for its downloading and display in a space enabled for purchase or promotion of products or services of the promoter. The video with customised audio is sent to the media demand-side platform (DSP) when a request is received from the media demand management system (DMP) to display advertising from the promoting company and this user, or group of users, already has a customised video created by the system.); and presenting the one or more applicable information modules to the particular user (at least [0011]-[0012], [0026] and claims 7-9. Receiving user data through a data management platform (DMP) and, once identified, either in a new access and/or when they continue browsing for a certain time, serving the video file to the user's browser for its downloading and display in a space enabled for purchase or promotion of products or services of the promoter. The video with customised audio is sent to the media demand-side platform (DSP) when a request is received from the media demand management system (DMP) to display advertising from the promoting company and this user, or group of users, already has a customised video created by the system.) However, Izquierdo Domenech et al. do not disclose updating the user profile over time based on the tracking or interaction with the information.
In the same field of endeavor, Bennett discloses updating the user profile over time based on the tracking or interaction with the information (at least [0018]-[0019] and [0032]. The client may contain an application, a part of the browser, or extra software that monitors user use patterns of the client over time to augment, update, or change the user profile information as time goes on.)
Therefore, it would have been obvious to one of ordinary skill in the art at the time the invention was made to modify the invention of Izquierdo Domenech et al. as taught by Bennett for purpose of selecting the search results for placement in the search result list based upon a user profile.
For claim 2, the combination of Izquierdo Domenech et al. and Bennett disclose the method of claim 1. Izquierdo Domenech et al. disclose the one or more applicable information modules include one or more of: a video module, an image module, a media module, a training module, a certification module, a data module and/or an application module (at least [0011]-[0012], [0026] and claims 7-9. Receiving user data through a data management platform (DMP) and, once identified, either in a new access and/or when they continue browsing for a certain time, serving the video file to the user's browser for its downloading and display in a space enabled for purchase or promotion of products or services of the promoter. The video with customised audio is sent to the media demand-side platform (DSP) when a request is received from the media demand management system (DMP) to display advertising from the promoting company and this user, or group of users, already has a customised video created by the system.)
For claim 3, the combination of Izquierdo Domenech et al. and Bennett disclose the method of claim 1. Bennett discloses the one or more applicable information modules include a widget, a functional code block, an applet, a scripting module, and a coded module configured to deliver a specific type of information (at least Fig. 1 and [0019]-[0022]. The page of FIG. 2 may be assembled by the server and/or by the client with information from the server and provided via a graphical display screen to the user. Specifically, the exemplary snap shot illustrated in FIG. 2 shows the search engine server's search interface web page 205 delivered to the client device's web browser 295 to facilitate user searching, to allow for the sending browser activity information, and favorite list and related metadata, and to activate user profiling (that is, to begin to collect user information, browser activity information and favorite list, process the information, etc., and send (or allow access to) the resulting latest user profile via the search engine server periodically or during searching). The search engine server's search interface web page 205 may contain a page title such as `Search Engine's web page (www.Search_Engine.com)` 221, and a `search` button 239. Along with `search` button 239, the search interface web page 205 also contains a `search within favorites` radio button 229, a `search using browser activity` radio button 233, a `search using trends` radio button 235, and an `include user profile` radio button 237 (or some other GUI or user interaction form of user selection other than a radio button) that help refine the search, in accordance with the teachings herein.)
For claim 4, the combination of Izquierdo Domenech et al. and Bennett disclose the method of claim 1. Izquierdo Domenech et al. disclose removing or adding applicable information modules based on a context, the context including a location of the user, role of the user, experience of the user, and/or current task of the user in the operational environment (at least [0004]. The data added by third parties come from sources external to advertisers and are common user segmentation data used by companies, including, for example, age, gender, socio-professional category, or geographical location.)
For claim 7, the combination of Izquierdo Domenech et al. and Bennett disclose the method of claim 1. Izquierdo Domenech et al. disclose presenting includes presenting content related to interactions of the particular user with equipment, software, or an operational process (at least [0011]-[0012], [0026] and claims 7-9. Receiving user data through a data management platform (DMP) and, once identified, either in a new access and/or when they continue browsing for a certain time, serving the video file to the user's browser for its downloading and display in a space enabled for purchase or promotion of products or services of the promoter. The video with customised audio is sent to the media demand-side platform (DSP) when a request is received from the media demand management system (DMP) to display advertising from the promoting company and this user, or group of users, already has a customised video created by the system.)
For claims 15-16, the claims have features similar to claims 1-2. Therefore, the claims are also rejected for the same reasons in claims 1-2.
For claim 18, the claim has features similar to claim 1. Therefore, the claim is also rejected for the same reasons in claim 1.
Claim 5, 17 and 19 are rejected under 35 U.S.C. 103 as being unpatentable over Izquierdo Domenech et al. (U.S. 20210142368) in view of Bennett (U.S. 20090287683) and further in view of Bose et al. (U.S. 11232484).
For claim 5, the combination of Izquierdo Domenech et al. and Bennett do not disclose the method of claim 4, wherein analyzing includes predicting information related to a context for a user of the plurality of users, the context including a location of the user, role of the user, experience of the user, and/or current task of the user in the operational environment.
In the same field of endeavor, Bose et al. disclose predicting information related to a context for a user of the plurality of users, the context including a location of the user, role of the user, experience of the user, and/or current task of the user in the operational environment (at least col. 11, line 57 to col. 12, line 5. Mart content may be used to make predictions about a customer's behavior interests by looking at their browser behavior in order of tasks and actions.)
Therefore, it would have been obvious to one of ordinary skill in the art at the time the invention was made to modify the invention of Izquierdo Domenech et al. as taught by Bennett for purpose of generating an output comprising the customized content for display on the website for the user in real-time.
For claims 17 and 19, the claims have features similar to claim 5. Therefore, the claims are also rejected for the same reasons in claim 5.
Claim 8 is rejected under 35 U.S.C. 103 as being unpatentable over Izquierdo Domenech et al. (U.S. 20210142368) in view of Bennett (U.S. 20090287683) and further in view of Swanson et al. (U.S. 11635889).
For claim 8, the combination of Izquierdo Domenech et al. and Bennett do not disclose the method of claim 4, generating data to level up skills and/or performance of the particular user in the operational environment
In the same field of endeavor, Swanson et al. disclose generating data to level up skills and/or performance of the particular user in the operational environment (at least col. 1, line 49-62. The interactive display dynamically presents, in real-time, a status or output of the second mobile application in response to the one or more user actions performed on the web-based interface.)
Therefore, it would have been obvious to one of ordinary skill in the art at the time the invention was made to modify the invention of Izquierdo Domenech et al. as taught by Swanson et al. for purpose of predicting outcome of the one or more user actions in the second mobile application.
Claim 9 is rejected under 35 U.S.C. 103 as being unpatentable over Izquierdo Domenech et al. (U.S. 20210142368) in view of Bennett (U.S. 20090287683) and further in view of Momchilov et al. (U.S. 20200336514).
For claim 9, the combination of Izquierdo Domenech et al. and Bennett do not disclose the method of claim 1, applying one or more machine learning or artificial intelligence models that identify patterns based on the user events, the patterns including patterns for a single user of the plurality of users or groups of users of the plurality of users.
In the same field of endeavor, Momchilov et al. disclose applying one or more machine learning or artificial intelligence models that identify patterns based on the user events, the patterns including patterns for a single user of the plurality of users or groups of users of the plurality of users (at least [0047]. The embedded browser 34 (or the server 31) may utilize machine learning or a matching algorithm to determine usage patterns in which a user(s) typically opens a second SaaS application after providing a given input or series of inputs in a first SaaS application.)
Therefore, it would have been obvious to one of ordinary skill in the art at the time the invention was made to modify the invention of Izquierdo Domenech et al. as taught by Momchilov et al. for purpose of pre-launching a second SaaS application from the server.
Claims 10-14 are rejected under 35 U.S.C. 103 as being unpatentable over Izquierdo Domenech et al. (U.S. 20210142368) in view of Bennett (U.S. 20090287683) and further in view of McCormack et al. (U.S. 20200128106).
For claim 10, the combination of Izquierdo Domenech et al. and Bennett do not disclose the method of claim 1, analyzing includes applying machine learning or artificial intelligence to identify one or more gaps in performance of operational procedures and training in the operational environment.
In the same field of endeavor, McCormack et al. disclose analyzing includes applying machine learning or artificial intelligence to identify one or more gaps in performance of operational procedures and training in the operational environment (at least [0158]. The System 100 may include an Intelligent Tutor (ITutor) component using the ML Module 109. The ITutor component captures and logs data related to each training repetition using the End-user Application Module 101, and sends that data to the ML Module 109, which detects the user's events and scores based on real-time analytics. Using a basic statistics approach, in which key variables are manually identified to serve as performance indicators (e.g. mortality rate, lethality, weather/lighting conditions, position of adversary, etc.), the ITutor component detects the user's deficiencies or strengths (such as poor performance with a pistol at night in the rain, or excellent use of cover in all conditions). The ITutor develops and maintains a baseline average for the population (all users) for each key variable. Compared to the baseline, each user's skills will be automatically and ongoingly assessed based on their performance. The simulations are then altered accordingly to challenge the user appropriately by modifying the intensity of the variables correlated with the performance indicator. For example, expanding on the example above, for the user performing poorly with a pistol at night in the rain, after identifying this user's deficiency, the System 100 will autonomously create and deliver more training exercises to the user oriented with a pistol at night in the rain.)
Therefore, it would have been obvious to one of ordinary skill in the art at the time the invention was made to modify the invention of Izquierdo Domenech et al. as taught by McCormack et al. for purpose of creating and delivering more training exercises to the user.
For claim 11, the combination of Izquierdo Domenech et al. and Bennett do not disclose the method of claim 4, establishing one or more thresholds or targets of performance aspects related to the operational environment.
In the same field of endeavor, McCormack et al. disclose establishing one or more thresholds or targets of performance aspects related to the operational environment (at least [0158]. The ITutor develops and maintains a baseline average for the population (all users) for each key variable. Compared to the baseline, each user's skills will be automatically and ongoingly assessed based on their performance. The simulations are then altered accordingly to challenge the user appropriately by modifying the intensity of the variables correlated with the performance indicator. For example, expanding on the example above, for the user performing poorly with a pistol at night in the rain, after identifying this user's deficiency, the System 100 will autonomously create and deliver more training exercises to the user oriented with a pistol at night in the rain.)
Therefore, it would have been obvious to one of ordinary skill in the art at the time the invention was made to modify the invention of Izquierdo Domenech et al. as taught by McCormack et al. for purpose of creating and delivering more training exercises to the user.
For claim 12, the combination of Izquierdo Domenech et al., Bennett and McCormack et al. disclose the method of claim 11. McCormack et al. disclose that based on the analyzing, identifying one or more trends or gaps in performance within the operational environment with respect to the one or more thresholds or targets (at least [0158]. The ITutor develops and maintains a baseline average for the population (all users) for each key variable. Compared to the baseline, each user's skills will be automatically and ongoingly assessed based on their performance. The simulations are then altered accordingly to challenge the user appropriately by modifying the intensity of the variables correlated with the performance indicator. For example, expanding on the example above, for the user performing poorly with a pistol at night in the rain, after identifying this user's deficiency, the System 100 will autonomously create and deliver more training exercises to the user oriented with a pistol at night in the rain.)
For claim 13, the combination of Izquierdo Domenech et al., Bennett and McCormack et al. disclose the method of claim 11. McCormack et al. disclose based on the analyzing, modifying and presenting the one or more applicable information modules about the operational environment based on a context of a particular user or the plurality of users in order to close the one or more gaps of a particular user (at least [0158]. The ITutor develops and maintains a baseline average for the population (all users) for each key variable. Compared to the baseline, each user's skills will be automatically and ongoingly assessed based on their performance. The simulations are then altered accordingly to challenge the user appropriately by modifying the intensity of the variables correlated with the performance indicator. For example, expanding on the example above, for the user performing poorly with a pistol at night in the rain, after identifying this user's deficiency, the System 100 will autonomously create and deliver more training exercises to the user oriented with a pistol at night in the rain.)
For claim 14, the combination of Izquierdo Domenech et al., Bennett and McCormack et al. disclose the method of claim 11. McCormack et al. disclose based on the analyzing, modifying the one or more applicable information modules about the operational environment based on the context of a particular user or the plurality of users and the one or more thresholds or targets (at least [0158]. The ITutor develops and maintains a baseline average for the population (all users) for each key variable. Compared to the baseline, each user's skills will be automatically and ongoingly assessed based on their performance. The simulations are then altered accordingly to challenge the user appropriately by modifying the intensity of the variables correlated with the performance indicator. For example, expanding on the example above, for the user performing poorly with a pistol at night in the rain, after identifying this user's deficiency, the System 100 will autonomously create and deliver more training exercises to the user oriented with a pistol at night in the rain.)
Allowable Subject Matter
Claims 6 and 20 are objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to DAI PHUONG whose telephone number is 571-272-7896. The examiner can normally be reached on Monday-Friday, 8am-5pm.
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/DAI PHUONG/Primary Examiner, Art Unit 2644