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 .
Claims 1, 3-11, and 13-25 are presented for examination.
Response to Amendment
Applicant’s amendment appears to have overcome the objections to the specification, drawings, and claims. Therefore, those objections are withdrawn.
Information Disclosure Statement
The information disclosure statements (IDS) submitted on November 3, 2025 (x4) are in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statements are being considered by the examiner. However, the prodigious number of references included precludes more than a perfunctory consideration of each reference. In particular, the over 1000 U.S. patent and U.S. patent application publications submitted were considered only insofar as claimed subject matter was searched for within the references. In the future, Applicant is advised only to submit documents that are directly relevant to the pending instant claims, and not to any other application in this family of applications or owned by the same assignee.
Claim Rejections - 35 USC § 101
The text of those sections of Title 35, U.S. Code not included in this action can be found in a prior Office action.
Claims 1, 3-11, and 13-25 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The analysis of the claims will follow the 2019 Revised Patent Subject Matter Eligibility Guidance, 84 Fed. Reg. 50 (“2019 PEG”).
Claim 1
Step 1: The claim recites a method; therefore, it is directed to the statutory category of processes.
Step 2A Prong 1: The claim recites, inter alia:
[T]racking … user inputs to the client computing device, at least during and after presentation of the first dashboard to the user, to identify at least one characteristic of the user inputs: This limitation could encompass a human visually inspecting the user inputs and mentally determining a characteristic of the inputs.
[A]pplying predictive analytics to the tracked user inputs to predict a type of data the user is attempting to access, wherein the predictive analytics includes logic for identifying one or more other users that accessed the first dashboard and for analyzing user characteristic information associated with the one or more users: This limitation could encompass the mental application of a simple predictive analytics algorithm to predict what data the user is attempting to access using data about what other users are accessing and user characteristics.
[C]orrelating the predicted type of data with one or more portions of one or more other second dashboards, wherein the one or more other second dashboards provide a representation of data having a type matching the predicted type of data: This limitation could encompass the mental correlation of the predicted data type with portions of other dashboard interfaces having the claimed characteristics.
[O]utputting a recommendation to the user …, the recommendation … includ[ing] an indication of the correlated one or more portions of the one or more second dashboards: This limitation could encompass a human writing down a recommendation including a correlation of the dashboard portions.
Step 2A Prong 2: This judicial exception is not integrated into a practical application. The claim further recites that the method is carried out “in a data processing system including at least one memory and at least one processor, wherein the at least one memory includes instructions that are executed by the at least one processor to configure the at least one processor to implement the method” and that certain steps of the method are performed using a “client computing device”. However, these are mere instructions to apply the judicial exception using a generic computer programmed with a generic class of computer algorithm. MPEP § 2106.05(f).
The claim further recites “presenting, on a client computing device, a first dashboard providing a graphical user interface to a user”, “outputting a recommendation … via the graphical user interface, the recommendation being an interactive graphical user interface element”, “receiving, via the graphical user interface, a user selection of the recommended interactive graphical user interface element”, and “in response to the user selection, modifying the first dashboard to present information obtained from the one or more second dashboards”. However, these limitations recite the insignificant extra-solution activity of mere data gathering and output. MPEP § 2106.05(g).
Step 2B: The claim does not contain significantly more than the judicial exception. The analysis at this step is identical to that of step 2A prong 2, except that the presenting, outputting, and modifying limitations, in addition to being insignificant extra-solution activity, also recites the well-understood, routine, and conventional activity of storing and retrieving information in memory. MPEP § 2106.05(d)(II); Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015), and insofar as the receiving limitation recites the well-understood, routine, and conventional activity of receiving or transmitting data over a network, OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network). As an ordered whole, the claim is directed to a mentally performable method of recommending dashboard interfaces to a user based on analysis of the user’s preferences. Nothing in the claim provides significantly more than this. As such, the claim is not patent eligible.
Claim 3
Step 1: A process, as above.
Step 2A Prong 1: The claim recites, inter alia, “analyzing the input data to generate analytics results data”. This limitation could encompass the mental performance of the analytics and generation of the results data.
Step 2A Prong 2: This judicial exception is not integrated into a practical application. The claim further recites “receiving input data from one or more source computing systems via one or more data networks” and “generating the first dashboard by populating portions of a dashboard data structure with corresponding portions of the analytics results data to generate a plurality of data representations within the first dashboard”. However, these limitations recite the insignificant extra-solution activity of mere data gathering and output. MPEP § 2106.05(g).
Step 2B: The claim does not contain significantly more than the judicial exception. The generating limitation is directed to the well-understood, routine, and conventional activity of storing and retrieving information in memory. MPEP § 2106.05(d)(II); Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015). The receiving limitation recites the well-understood, routine, and conventional activity of receiving or transmitting data over a network. OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network).
Claim 4
Step 1: A process, as above.
Step 2A Prong 1: The claim recites that “tracking user inputs to the client computing device comprises tracking user inputs to user interface elements of the first dashboard and tracking user inputs to at least one other user experience system providing another user interface for accessing content or exchanging information with another user”. This limitation could encompass a human visually inspecting user inputs to the dashboard interface and to another user experience system.
Step 2A Prong 2: This judicial exception is not integrated into a practical application. See claim 1 analysis.
Step 2B: The claim does not contain significantly more than the judicial exception. See claim 1 analysis.
Claim 5
Step 1: A process, as above.
Step 2A Prong 1: The claim recites that “the at least one other user experience system comprises at least one of a search engine user interface or an electronic messaging user interface”. The underlying tracking of inputs to this experience system remains mentally performable under this further assumption.
Step 2A Prong 2: This judicial exception is not integrated into a practical application. See claim 4 analysis.
Step 2B: The claim does not contain significantly more than the judicial exception. See claim 4 analysis.
Claim 6
Step 1: A process, as above.
Step 2A Prong 1: The claim recites:
[T]racking user inputs to the at least one other user experience system comprises extracting key terms or key phrases entered by the user into the search engine user interface or electronic messaging user interface: This limitation could encompass the mental extraction of key terms or phrases entered by the user into the interfaces by visually inspecting the inputs and selecting certain portions thereof.
[C]orrelating the predicted type of data with one or more portions of one or more other second dashboards comprises searching metadata associated with the one or more other second dashboards based on the identified key terms or key phrases to identify the one or more other second dashboards as having at least one matching key term or key phrase in the metadata as the extracted key terms or key phrases: This limitation could encompass the mental searching of the metadata of the other dashboard interface to determine similar keywords by visually inspecting the metadata and making a mental determination that the keywords match.
Step 2A Prong 2: This judicial exception is not integrated into a practical application. See claim 5 analysis.
Step 2B: The claim does not contain significantly more than the judicial exception. See claim 5 analysis.
Claim 7
Step 1: A process, as above.
Step 2A Prong 1: The claim recites the same judicial exceptions as in claim 1.
Step 2A Prong 2: This judicial exception is not integrated into a practical application. The claim further recites that “the outputting the recommendation to the user comprises outputting a portion of the first dashboard or another user interface having user selectable links that, when selected by the user, access corresponding second dashboards of the one or more other second dashboards”. However, this amounts to the insignificant extra-solution activity of mere data gathering and output. MPEP § 2106.05(g).
Step 2B: The claim does not contain significantly more than the judicial exception. The claim further recites that “the outputting the recommendation to the user comprises outputting a portion of the first dashboard or another user interface having user selectable links that, when selected by the user, access corresponding second dashboards of the one or more other second dashboards”. However, this amounts to the well-understood, routine, and conventional activity of storing and retrieving information in memory. MPEP § 2106.05(d)(II); Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015).
Claim 8
Step 1: A process, as above.
Step 2A Prong 1: The claim recites, inter alia, “generating a dashboard recommendation based on the cumulative tracked usage metrics”. This limitation could encompass the mental generation of a recommendation based on tracked usage metrics.
Step 2A Prong 2: This judicial exception is not integrated into a practical application. The claim further recites “storing, for the first dashboard, tracked usage metrics based on tracking the user inputs to the client computing device cumulatively with other user inputs to the first dashboard; … and outputting the dashboard recommendation to a system administrator”. However, these limitations recite the insignificant extra-solution activity of mere data gathering and output. MPEP § 2106.05(g).
Step 2B: The claim does not contain significantly more than the judicial exception. The claim further recites “storing, for the first dashboard, tracked usage metrics based on tracking the user inputs to the client computing device cumulatively with other user inputs to the first dashboard; … and outputting the dashboard recommendation to a system administrator”. However, these limitations recite the well-understood, routine, and conventional activity of storing and retrieving information in memory. MPEP § 2106.05(d)(II); Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015).
Claim 9
Step 1: A process, as above.
Step 2A Prong 1: The claim recites, inter alia:
[C]reating a cluster of users that have at least one matching characteristic to the user based on the analysis of the user characteristic information associated with the one or more users: This limitation could encompass the mental identification of characteristics of other users and comparing them to those of another user.
[G]enerating the recommendation based on other second dashboards accessed by at least a portion of the cluster of users: This limitation could encompass a human communicating a recommendation in writing based on other interfaces accessed by other users.
Step 2A Prong 2: This judicial exception is not integrated into a practical application. See claim 1 analysis.
Step 2B: The claim does not contain significantly more than the judicial exception. See claim 1 analysis.
Claim 10
Step 1: A process, as above.
Step 2A Prong 1: The claim recites “ identifying at least one other second dashboard accessed by the at least the portion of the cluster of users which provides a representation of data having a type matching the predicted type of data, and which has not been previously accessed by the user during a current user session”. This limitation could encompass mentally identifying the second dashboard interface with the claimed characteristics.
Step 2A Prong 2: This judicial exception is not integrated into a practical application. See claim 9 analysis.
Step 2B: The claim does not contain significantly more than the judicial exception. See claim 9 analysis.
Claim 21
Step 1: A process, as above.
Step 2A Prong 1: The claim recites, inter alia:
[T]he identified at least one first characteristic comprises at least one key text portion extracted from a text input of the user inputs: The identification of the characteristic remains mentally performable under these further assumptions.
[C]orrelating the predicted type of data with one or more portions of one or more other second dashboard interfaces further comprises performing a search of metadata of a plurality of dashboard interfaces based on the extracted key text portion and selecting the one or more second dashboard interfaces based on a matching of the key text portion with content in metadata associated with the one or more second dashboard interfaces: This limitation could encompass a human searching metadata by visual inspection, mentally matching the key text portion with the metadata, and mentally choosing a dashboard interface based on this matching.
Step 2A Prong 2: This judicial exception is not integrated into a practical application. See claim 1 analysis.
Step 2B: The claim does not contain significantly more than the judicial exception. See claim 1 analysis.
Claims 11, 13-19, 22
Step 1: The claims are directed to a computer program product comprising a “computer readable storage medium”. Paragraph 30 of the specification defines the term “computer readable storage medium” as excluding transitory signals per se. Thus, the claims are directed to the statutory category of articles of manufacture.
Step 2A Prong 1: The claims recite the same judicial exceptions as in claims 1, 3-6, 8-10, and 21, respectively.
Step 2A Prong 2: This judicial exception is not integrated into a practical application. The analysis at this step is the same as that of claims 1, 3-6, 8-10, and 21, respectively, except insofar as these claims recite a “computer program product including a computer readable storage medium having a computer readable program stored therein, wherein the computer readable program, when executed on a computing device, causes the computing device to [perform the method]”. However, this is a mere instruction to apply the judicial exception using a generic computer. MPEP § 2106.05(f).
Step 2B: The claim does not contain significantly more than the judicial exception. The analysis at this step is the same as that of claims 1, 3-6, 8-10, and 21, respectively, except insofar as these claims recite a “computer program product including a computer readable storage medium having a computer readable program stored therein, wherein the computer readable program, when executed on a computing device, causes the computing device to [perform the method]”. However, this is a mere instruction to apply the judicial exception using a generic computer. MPEP § 2106.05(f).
Claim 20
Step 1: The claim is directed to an apparatus comprising a processor and a memory; therefore, it is directed to the statutory category of machines.
Step 2A Prong 1: The claim recites the same judicial exceptions as in claim 1.
Step 2A Prong 2: This judicial exception is not integrated into a practical application. The analysis at this step is the same as that of claim 1, except insofar as this claim recites an “apparatus comprising: a processor; and a memory coupled to the processor, wherein the memory includes instructions which, when executed by the processor, cause the processor to [perform the method]”. However, this is a mere instruction to apply the judicial exception using a generic computer. MPEP § 2106.05(f).
Step 2B: The claim does not contain significantly more than the judicial exception. The analysis at this step is the same as that of claim 1, except insofar as this claim recites an “apparatus comprising: a processor; and a memory coupled to the processor, wherein the memory includes instructions which, when executed by the processor, cause the processor to [perform the method]”. However, this is a mere instruction to apply the judicial exception using a generic computer. MPEP § 2106.05(f).
Claim 23
Step 1: A process, as above.
Step 2A Prong 1: The claim recites that “one or more of: the first dashboard; or the one or more other second dashboards, are predefined.” Performing the predictive analytics on the dashboards and correlating the predicted data type with second dashboards remain mentally performable under these further assumptions.
Step 2A Prong 2: This judicial exception is not integrated into a practical application. See claim 1 analysis.
Step 2B: The claim does not contain significantly more than the judicial exception. See claim 1 analysis.
Claim 24
Step 1: A process, as above.
Step 2A Prong 1: The claim recites the same judicial exceptions as in claim 1.
Step 2A Prong 2: This judicial exception is not integrated into a practical application. The claim further recites that “the recommendation is displayed in the first dashboard or as a pop-up window.” This limitation is directed to the insignificant extra-solution activity of mere data gathering and output. MPEP § 2106.05(g).
Step 2B: The claim does not contain significantly more than the judicial exception. The claim further recites that “the recommendation is displayed in the first dashboard or as a pop-up window.” This limitation is directed to the well-understood, routine, and conventional activity of storing or retrieving information in memory. MPEP § 2106.05(d)(II); Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015).
Claim 25
Step 1: A process, as above.
Step 2A Prong 1: The claim recites the same judicial exceptions as in claim 1.
Step 2A Prong 2: This judicial exception is not integrated into a practical application. The claim further recites that “the recommendation is stored in the at least one memory as user characteristic information associated with the user.” This limitation is directed to the insignificant extra-solution activity of mere data gathering and output. MPEP § 2106.05(g).
Step 2B: The claim does not contain significantly more than the judicial exception. The claim further recites that “the recommendation is stored in the at least one memory as user characteristic information associated with the user.” This limitation is directed to the well-understood, routine, and conventional activity of storing or retrieving information in memory. MPEP § 2106.05(d)(II); Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015).
Claim Rejections - 35 USC § 103
Claims 1, 3-5, 8, 11, 13-15, 17, 20, and 23-25 are rejected under 35 U.S.C. 103 as being unpatentable over Roy (US 11481092) (“Roy”) in view of Basson et al. (US 20060190822) (“Basson”) and further in view of Greenspan et al. (US 20180173372) (“Greenspan”).
Regarding claim 1, Roy discloses “[a] method, in a data processing system including at least one memory and at least one processor, wherein the at least one memory includes instructions that are executed by the at least one processor to configure the at least one processor to implement the method (example embodiment provides a computer program product for operating an intelligent workspace on an application of a computing device with memory; the product comprises a computer-readable storage medium [memory] readable by a self-learning processor [processor] and storing instructions for performing the method – Roy, col. 3, ll. 21-30), the method comprising:
presenting, on a client computing device, a first dashboard providing a graphical user interface to a user (system with an intelligent workspace [dashboard/GUI] for operating on an application of a computing device includes an electronic user interface configured to display [present] the intelligent workspace on the computing device [client computing device] and receive inputs from a user – Roy, col. 2, ll. 36-64);
tracking user inputs to the client computing device, at least during and after presentation of the first dashboard to the user, to identify at least one characteristic of the user inputs (system includes a user activity database coupled to a server for storing real-time user activity data obtained by identifying and tracking activity of the user in the workspace [i.e., during and after presentation of the dashboard to the user] – Roy, col. 2, ll. 36-64; an AI engine maps the user profile data and user behavior data that include the user’s preferred locations on the electronic user interface [preferred locations = characteristic] by analyzing the user’s intent and actions based on a data reciprocity developed among the AI engine, the workspace, the user activity database and the user profile database – id. at col. 7, ll. 8-42);
applying predictive analytics to the tracked user inputs to predict a type of data the user is attempting to access (AI engine [predictive analytics] predicts a set of actions that the user may want to perform in the application [prediction of a desired user action implies prediction of what data the user may want to access] by analyzing the user’s intent and actions based on a data reciprocity developed among the AI engine, the workspace, the user activity database and the user profile database – Roy, col. 7, ll. 8-42) …;
correlating the predicted type of data with one or more portions of one or more … dashboards (method includes a step of reconfiguring the workspace dynamically to provide a plurality of optimization and navigation options [portions of dashboard interfaces] to the user based on the predicted actions [predicted type of data] – Roy, col. 7, ll. 8-42 [reconfiguring workspace based on prediction = correlating the dashboard with the prediction]), wherein the one or more … dashboards provide a representation of data having a type matching the predicted type of data (recommended user preferred locations on the electronic user interface are generated by analyzing, in real-time, the predicted set of actions by the user on the electronic user interface as part of the real-time user activity data – Roy, claim 1 [i.e., the data type displayed on the dashboard matches that which the user was predicted to be attempting to access]);
outputting a recommendation to the user via the graphical user interface, the recommendation being an interactive graphical user interface element (recommended user preferred locations [recommendation/interactive GUI element] on the electronic user interface are generated [output] by analyzing, in real-time, the predicted set of actions by the user on the electronic user interface [i.e., via the GUI] as part of the real-time user activity data – Roy, claim 1) …; [and]
receiving, via the graphical user interface, a user selection of the recommended interactive graphical user interface element (Coachsmart is triggered at regular intervals to make recommendations to the user on how to personalize the navigation ribbon and make it suitable for quick access; at any point within the workspace, the user can add a page to her consistent navigation to use it as a shortcut [note that the adding of the page per the recommendation is a user selection] – Roy, col. 15, ll. 29-41) ….”
Roy appears not to disclose explicitly the further limitations of the claim. However, Basson discloses “correlating the predicted type of data with one or more portions of one or more other second dashboards, wherein the one or more second dashboards provide a representation of data (in a system for dynamic modification of user interfaces based on the current emotional and mental state of the user, conditions are sensed with respect to a controllable element, and a determination is made as to whether or not there is an exact match between the sensed conditions and previously-sensed conditions, and if not a new interface [second dashboard] is developed based upon modeling algorithms being applied to the newly-sensed conditions [i.e., a second interface is developed by correlating the dashboard with the predicted type of data the user wants to interact with based on the emotional state of the user] – Basson, paragraphs 73-75) …; …
[wherein] an interactive graphical user interface element includes an indication of the correlated one or more portions of the one or more other second dashboards (in a system for dynamic modification of user interfaces based on the current emotional and mental state of the user, conditions are sensed with respect to a controllable element, and a determination is made as to whether or not there is an exact match between the sensed conditions and previously-sensed conditions, and if not a new interface [second dashboard] is developed based upon modeling algorithms being applied to the newly-sensed conditions [note that the second interface is itself an indication of the correlation of the dashboard with the information the user is seeking] – Basson, paragraphs 73-75); … and
in response to the user selection, modifying the first dashboard to present information obtained from the one or more other second dashboards (Basson Fig. 3 shows that conditions are sensed and, if there is no exact match with previously sensed conditions, a new interface [second dashboard] is developed based on a model, after which the interface [e.g., the first dashboard] is modified based on the model [i.e., to present information from the new interface/second dashboard]; paragraph 8 establishes that the condition sensed may be a user selection).”
Basson and the instant application both relate to adaptable user interfaces and are analogous. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Roy to recommend the user access a second interface based on correlating the interface with a predicted type of data the user wants to access, as disclosed by Basson, and an ordinary artisan could reasonably expect to have done so successfully. Doing so would allow the most relevant interface to be presented to the user on-the-fly, thereby enhancing the user experience. See Basson, paragraph 10.
Neither Roy nor Basson appears to disclose explicitly the further limitations of the claim. However, Greenspan discloses that “the predictive analytics includes logic for identifying one or more other users that accessed the first dashboard and for analyzing user characteristic information associated with the one or more other users (a first computing device is instructed to display a first graph [first dashboard] depicting a first metric, and it is determined that the first graph is to be shared on [accessed by] a second computing device [i.e., with another user]; it is then inferred [analyzed] that the second user prefers to view the first metric in a second graph [characteristic information of the other users = preference for second graph over first] – Greenspan, Fig. 5, ref. chars. 202-06) ….”
Greenspan and the instant application both relate to analytics for user interfaces and are analogous. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the combination of Roy and Basson to identify other users that accessed the first dashboard and analyze characteristic information associated with the other users, as disclosed by Greenspan, and an ordinary artisan could reasonably expect to have done so successfully. Doing so would maintain the user’s preferred modes of visualization while enhancing data sharing. See Greenspan, paragraph 84.
Claim 11 is a computer readable storage medium claim corresponding to method claim 1 and is rejected for the same reasons as given in the rejection of that claim. Similarly, claim 20 is an apparatus claim corresponding to method claim 1 and is rejected for the same reasons as given in the rejection of that claim.
Regarding claim 3, the rejection of claim 1 is incorporated. Roy further discloses that “presenting the first dashboard comprises:
receiving input data from one or more source computing systems via one or more data networks (server system may include various servers [source computing systems] for communicating and processing data [input data] across the network [data network] – Roy, col. 6, ll. 55-60);
analyzing the input data to generate analytics results data (AI engine maps user profile data and user behavior data that include the user’s preferred locations [analytics results] on the electronic user interface by analyzing the user’s intent and actions based on a data reciprocity between the AI engine, the workspace, the user activity database and the user profile database – Roy, col. 7, ll. 8-42); and
generating the first dashboard by populating portions of a dashboard data structure with corresponding portions of the analytics results data to generate a plurality of data representations within the first dashboard (AI engine has the ability to evolve and learn over time and communicate relevant information to a self-learning data processor by receiving inputs from a user via a self-evolving [populating] user interface for processing the information through an intelligent workspace; the AI engine suggests optimized workflow options [plurality of data representations] to the user and the system automatically processes the workflows; the user activity data are mapped with the user profile data to correlate a requirement of the user and the behavioral pattern of the user for suggesting and implementing workflow options in the workspace on the computing device – Roy, col. 4, ll. 28-50; auto-selection of workflows is based on the user’s preferred location [analytics results data] on the display of a user interface – id.).”
Claim 13 is a computer readable storage medium claim corresponding to method claim 3 and is rejected for the same reasons as given in the rejection of that claim.
Regarding claim 4, the rejection of claim 1 is incorporated. Roy further discloses that “tracking user inputs to the client computing device comprises tracking user inputs to user interface elements of the first dashboard (smart nav tool is a self-learning navigation tool configured for optimizing a navigation page of the application based on the real-time user activity data and user profile data; the tool tracks the user’s frequency of usage, frequently visited locations [interface elements] on the electronic interface by the user, and frequency paths to reach a particular activity point – Roy, col. 10, ll. 12-39; user inputs are received from the user through the electronic user interface – id. at col. 7, ll. 11-42) ….”
Roy/Greenspan appears not to disclose explicitly the further limitations of the claim. However, Basson discloses “tracking user inputs to at least one other user experience system providing another user interface for accessing content or exchanging information with another user (each of the sensors are coupled to a processor/controller [user experience system] that receives [tracks] input [user input] from each of the sensors and then modifies a modifiable interface [other user interface] – Basson, paragraph 28; interface is designed to present information [access content] to a driver with a minimal amount of attention diversion – id. at paragraph 44).” It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Roy/Greenspan to track user inputs to a user interface for accessing content, as disclosed by Basson, and an ordinary artisan could reasonably expect to have done so successfully. Doing so would allow the most relevant interface to be presented to the user on-the-fly, thereby enhancing the user experience. See Basson, paragraph 10.
Claim 14 is a computer readable storage medium claim corresponding to method claim 4 and is rejected for the same reasons as given in the rejection of that claim.
Regarding claim 5, Roy, as modified by Greenspan and Basson, discloses that “the at least one other user experience system comprises at least one of a search engine user interface or an electronic messaging user interface (interface is designed to present information [electronic messages] to a driver with a minimal amount of attention diversion – id. at paragraph 44).” It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Roy/Greenspan to gather information from an electronic messaging user interface, as disclosed by Basson, and an ordinary artisan could reasonably expect to have done so successfully. Doing so would allow the most relevant interface to be presented to the user on-the-fly, thereby enhancing the user experience. See Basson, paragraph 10.
Claim 15 is a computer readable storage medium claim corresponding to method claim 5 and is rejected for the same reasons as given in the rejection of that claim.
Regarding claim 8, the rejection of claim 1 is incorporated. Roy further discloses “storing, for the first dashboard, tracked usage metrics based on tracking the user inputs to the client computing device cumulatively with other user inputs to the first dashboard (system includes a user activity database coupled to a server for storing real-time user activity data [tracked usage metrics] obtained by identifying and tracking activity of the user [user inputs] in the workspace [dashboard interface] – Roy, col. 2, ll. 36-64 [note that since the tracking is real-time, both previous user inputs and current [other] user inputs are tracked cumulatively]);
generating a dashboard recommendation based on the cumulative tracked usage metrics (recommended user preferred locations [dashboard recommendations] on the electronic user interface are generated by analyzing, in real-time, the predicted set of actions by the user on the electronic user interface as part of the real-time user activity data [tracked user metrics] – Roy, claim 1); and
outputting the dashboard recommendation to a system administrator (recommended user preferred locations [dashboard recommendations] on the electronic user interface are generated by analyzing, in real-time, the predicted set of actions by the user on the electronic user interface as part of the real-time user activity data – Roy, claim 1; screen may be configured in an admin module [i.e., the admin may see the results of the screen configuration, i.e., the recommendation] – id. at col. 7, ll. 43-53).”
Claim 17 is a computer readable storage medium claim corresponding to method claim 8 and is rejected for the same reasons as given in the rejection of that claim.
Regarding claim 23, Roy, as modified by Basson and Greenspan, discloses that “one or more of: the first dashboard; or the one or more other second dashboards, are predefined (electronic user interface displays an intelligent workspace on a computing device and receives input from a user; the electronic user interface is a self-evolving user interface configured to receive inputs from a user through voice commands, gesture controls, mouse, touch pads, or keyboards [i.e., the interface is predefined prior to receiving user input] – Roy, col. 2, ll. 36-64).”
Regarding claim 24, Roy, as modified by Basson and Greenspan, discloses that “the recommendation is displayed in the first dashboard or as a pop-up window (Coachsmart is triggered at regular intervals to make recommendations to the user on how to personalize the navigation ribbon and make it suitable for quick access; at any point within the workspace, the user can add a page to her consistent navigation to use it as a shortcut [i.e., the recommendation is displayed in the dashboard] – Roy, col. 15, ll. 29-41).”
Regarding claim 25, Roy, as modified by Basson and Greenspan, discloses that “the recommendation is stored in the at least one memory as user characteristic information associated with the user (Coachsmart is triggered at regular intervals to make recommendations to the user on how to personalize the navigation ribbon and make it suitable for quick access; at any point within the workspace, the user can add a page to her consistent navigation to use it as a shortcut [note that the fact that the added page is stored as a shortcut and is the result of a recommendation implies that the recommendation is stored as user characteristic information] – Roy, col. 15, ll. 29-41; see also col. 6, ll. 18-22 (indicating that a memory stores information within a computing device)).”
Claims 6, 16, and 21-22 are rejected under 35 U.S.C. 103 as being unpatentable over Roy in view of Basson and Greenspan and further in view of Saito et al. (US 20050010582) (“Saito”).
Regarding claim 6, the rejection of claim 5 is incorporated. Roy further discloses “dashboards”, as shown above in the rejection of claim 1.
Neither Roy, Greenspan, nor Basson appears to disclose explicitly the further limitations of the claim. However, Saito discloses “tracking user inputs to the at least one other user experience system comprises extracting key terms or key phrases entered by the user into the search engine user interface or electronic messaging user interface (user may register [enter, on an electronic messaging user interface] his favorite categories (drama, variety, etc.), his favorite genre names (drama, music, etc.), and his favorite television personalities; the registered information is used as keywords [key terms/key phrases] – Saito, paragraph 4), and … correlating the predicted type of data with one or more portions of one or more other second [items] comprises searching metadata associated with the one or more other second [items] based on the identified key terms or key phrases to identify the one or more other second [items] as having at least one matching key term or key phrase in the metadata as the extracted key terms or key phrases (information processing apparatus includes metadata acquisition means for acquiring [extracting] metadata of content [i.e., associated with items including a second item], metadata analysis means for analyzing [searching] an attribute of the metadata acquired by the metadata acquisition means, and dictionary generation means for generating dictionary data for correlating an attribute item contained in the attribute on the basis of an analysis result acquired by the metadata analysis means – Saito, paragraph 44; dictionary generation means detects, from among words contained in the metadata, a word that is high in co-occurrence in metadata having a particular attribute item as a keyword of the attribute item, thereby correlating the attribute item of the metadata with the keyword [i.e., key phrases are extracted from the metadata] – id. at paragraph 14; matching is made between one user’s viewing log and another user’s viewing log to acquire the viewing log of another user similar to the user concerned; program names that have not been viewed by the user concerned are acquired from among the programs viewed by another user similar to the user concerned [i.e., the keywords extracted are compared to other users’ keywords to identify matching keywords] – id. at paragraph 6).”
Saito and the instant application both relate to content recommendation algorithms and are analogous. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the combination of Roy, Greenspan, and Basson to search metadata keywords for similar keywords in order to recommend content to a user, as disclosed by Saito, and an ordinary artisan could reasonably expect to have done so successfully. Doing so would ensure that the users of the system are recommended more relevant content. See Saito, paragraph 44.
Claim 16 is a computer readable storage medium claim corresponding to method claim 6 and is rejected for the same reasons as given in the rejection of that claim.
Regarding claim 21, the rejection of claim 1 is incorporated. Roy further discloses “dashboards”, as shown above in the rejection of claim 1.
Neither Roy, Greenspan, nor Basson appears to disclose explicitly the further limitations of the claim. However, Saito discloses that “the identified at least one first characteristic includes at least one key text portion extracted from a text input of the user inputs (user may register [via text input] his favorite categories (drama, variety, etc.), his favorite genre names (drama, music, etc.), and his favorite television personalities; the registered information is used as keywords [key terms/key phrases] – Saito, paragraph 4), and … the correlating the predicted type of data with one or more portions of one or more other second [items] further comprises performing a search of metadata of a plurality of [items] based on the extracted key text portion and selecting the one or more second [items] based on a matching of the key text portion with content in metadata associated with the one or more second [items] (keywords are indicative of predetermined words extracted from text information which introduces content corresponding to metadata; there are three items of metadata each containing keywords [key text portion]; metadata analysis block detects a genre of the metadata [i.e., performs a search of the metadata based on the key text portion] – Saito, paragraphs 80-85; dictionary generation means detects, from among words contained in the metadata, a word which is high in co-occurrence in metadata having a particular attribute item as a keyword of the attribute item, thereby correlating [matching] the attribute item of the metadata [content in metadata] with the keyword – Saito, paragraph 14).” It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the combination of Roy, Greenspan, and Basson to perform recommendation by searching metadata and matching key text portions with content in the metadata, as disclosed by Saito, and an ordinary artisan could reasonably expect to have done so successfully. Doing so would ensure that the users of the system are recommended more relevant content. See Saito, paragraph 44.
Claim 22 is a computer readable storage medium claim corresponding to method claim 21 and is rejected for the same reasons as given in the rejection of that claim.
Claim 7 is rejected under 35 U.S.C. 103 as being unpatentable over Roy in view of Basson and Greenspan and further in view of Chase et al. (US 20150006205) (“Chase”).
Regarding claim 7, the rejection of claim 1 is incorporated. The combination of Roy, Greenspan, and Basson teaches “outputting the recommendation to the user”, as shown in the rejection of claim 1 above. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Roy/Greenspan to recommend the user access a second interface based on correlating the interface with a predicted type of data the user wants to access, as disclosed by Basson, for substantially the same reasons as given in the rejection of claim 1.
Neither Roy, Greenspan, nor Basson appears to disclose explicitly the further limitations of the claim. However, Chase discloses “outputting a portion of the first dashboard or another user interface having user selectable links that, when selected by the user, access corresponding second dashboards of the one or more other second dashboards (links interface [first dashboard interface] includes Uniform Resource Locator (“URL”) address links to country-wide internal web pages and country-wide external web pages (e.g., GOOGLE maps [second dashboard interface]) – Chase, paragraph 35; see also Fig. 9 (showing the links interface with the links)).”
Chase and the instant application both relate to user interfaces and are analogous. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the combination of Roy, Greenspan, and Basson to include a portion of the interface that includes selectable links to other interfaces, as disclosed by Chase, and an ordinary artisan could reasonably expect to have done so successfully. Doing so would provide a convenient way for the user to access additional information that may be of interest to him. See Chase, paragraph 35.
Claims 9-10 and 18-19 are rejected under 35 U.S.C. 103 as being unpatentable over Roy in view of Basson and Greenspan and further in view of Yamamoto (US 20170098001) (“Yamamoto”).
Regarding claim 9, the rejection of claim 1 is incorporated. Roy further discloses that “correlating the predicted type of data with one or more portions of one or more other second dashboards comprises:
identifying, by the data processing system, characteristics of the one or more … users (AI engine maps the user profile data and user behavior data that include the user’s preferred locations on the electronic user interface by analyzing the user’s intent and actions based on a data reciprocity developed between the AI engine, the workspace, the user activity database and the user profile database [i.e., the system identifies that a characteristic of the user is the intent to access a certain interface option] – Roy, col. 7, ll. 8-42) …; and
generating the recommendation based on … dashboards accessed by at least a portion of [a] cluster of users (recommended user preferred locations on the electronic user interface are generated by analyzing, in real-time, the predicted set of actions by the user interface as part of the real-time user activity data based on the data reciprocity among the workspace, the user activity database, and the user profile database [i.e., the recommendation is generated by analyzing the history of the user activity across multiple iterations of the workspace; note that a cluster of users may contain a single user] – Roy, claim 1).”
Roy/Basson/Greenspan appear not to disclose explicitly the further limitations of the claim. However, Yamamoto discloses “ creating a cluster of users that have at least one matching characteristic to the user based on the analysis of the user characteristic information associated with the one or more users (system can output, as information on a recommended item, information on the item identified by the characteristic item selected by the user and the search value among the items that the similar user has referred to in the past [items to which the other user refers = characteristic information of the other users; the fact that the other user is described as “similar” implies at least one characteristic of the other user that matches that of the user; note also that a single user may be characterized as a cluster]; thus, the information on an item similar to the item that the user is searching for can be presented as the information on a recommended item – Yamamoto, paragraph 91); and
generating the recommendation based on other second [items] accessed by at least a portion of the cluster of users (system can output, as information on a recommended item, information on the item identified by the characteristic item selected by the user and the search value among the items that the similar user [at least a portion of the cluster of users] has referred to in the past; thus, the information on an item similar to the item that the user is searching for can be presented as the information on a recommended item – Yamamoto, paragraph 91 [similar item = second item]).”
Yamamoto and the instant application both relate to recommendation systems and are analogous. It would have been obvious to one of ordinary skill in the ar