DETAILED ACTION
Claims 1-20 are pending.
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 .
Response to Arguments
Applicant’s arguments with respect to the 35 U.S.C. 103 rejections (Remarks pp. 10-12) have been fully considered but are unpersuasive.
1. The Applicant argues that reference Merrill (US 20110161887 A1) does not teach Claim 1’s “receive, via an application executing on a computing device of a user, a request for navigation assistance within the application, wherein the request includes a destination within the application,” and “generate a first user interface including a first selectable option, wherein the first selectable option is associated with a first node along the optimized navigation route.” Applicant states:
“For instance, a user selecting a destination and being navigated to that destination does not constitute any generation of a first user interface including a ‘first node along the optimized navigation route.’ In fact, no navigation route is determined in these arrangements as the user is immediately traversed to the destination. Merrill at para. [0030]. Alternatively, the system of Merrill identifies possible destination locations and provides options to the user. Para. [0035]. However, nothing in Merrill teaches or suggests receiving a destination and generating a user interface including a selectable option associated with a first node along the optimized navigation route to the destination node associated with the destination within the application (received from the user). Instead, Merrill merely describes generating an interface with possible destination nodes - not with a selectable option for a node along the optimized navigation route to the destination node.”
The Examiner respectfully disagrees.
Merrill teaches “navigation route.”
Paragraph 30 of Merrill states that “In these and other embodiments, the application is traversed to the selected destination location through a user gesture, such as a right-mouse click with a cursor on a graphical element representing the selected destination location.” This means that the user’s request for navigating to the selected destination, containing an indication of said destination location, is received.
Merrill’s Figure 3 shows a graph consisting of nodes, each representing different pages of the web application that can be traversed starting from a root node. The traversal from the root node to a destination node is a navigation route, and said traversal is determined based on user input. Paragraph 5 of Merrill states that “However, anticipating all possible user navigation scenarios becomes more challenging as applications grow in complexity. As an example, a user may reach the same destination, and its corresponding function, by taking multiple and different paths through an application.”
Applicant’s next argument appears to be based on claim interpretation that is not BRI.
Applicant states, Merrill fails to teach “receiving a destination and generating a user interface including a selectable option associated with a first node along the optimized navigation route to the destination node associated with the destination within the application.”
The Examiner disagrees.
The Examiner prefers to analyze the claim language, and Applicant’s characterization appears to be consistent.
Claim 1 recites, “generate a first user interface including a first selectable option, wherein the first selectable option is associated with a first node along the optimized navigation route to the destination node associated with the destination within the application.” Note:
The Examiner explained that Merrill teaches navigation route, and Examiner’s secondary references teaches the “optimized navigation route” and its generation.
The claim requires that the “first selectable option” be associated with the “first node.”
The claim does not require that the “first selectable option” be directly associated with “the optimized navigation route” or “the destination,” except for the association through the “first selectable option.”
For example, the first node could be the current node or any node along the optimized navigation route that taught by the Examiner’s secondary references. While a user is at the current node, the system may provide selectable options associated with the current node and context. An example would be navigation software: depending on the location of a car on route to a destination, the software may offer options/information about landmarks/shops/alternative routes.
Merrill exactly teaches that, “State information associated with the user's current location is then processed in step 508 to generate possible destination locations within the application. The resulting possible location destinations within the application are then displayed to the user in step 510. In various embodiments, the user's current location and the possible destination locations are contextually displayed to the user within a user interface as graphical elements,” ¶ 0035.
2. Applicant’s arguments with respect to the amended limitation consisting of using customer application navigation logs to train a machine learning model, wherein said logs further include a purpose of a visit to a respective node, are moot in view of the Examiner’s new reference Velez-Rojas (US 20180174060 A1), added to address said limitation.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1-2, 9-10, and 17-18 are rejected under 35 U.S.C. 103 as being unpatentable over Merrill (US 20110161887 A1) in view of Rizi (“Shortest Path Distance Approximation using Deep learning Techniques”) and Velez-Rojas (US 20180174060 A1).
Regarding Claim 1, Merrill teaches a computing platform, comprising: at least one processor; a communication interface communicatively coupled to the at least one processor; and a memory storing computer-readable instructions that, when executed by the at least one processor, cause the computing platform to (
Merrill discloses, “FIG. 1 is a block diagram of an exemplary client computer 102 in which the present invention may be utilized. Client computer 102 includes a processor unit 104 that is coupled to a system bus 106. A video adapter 108, which controls a display 110, is also coupled to system bus 106. System bus 106 is coupled via a bus bridge 112 to an Input/Output (PO) bus 114. An I/O interface 116 is coupled to I/O bus 114. The I/O interface 116 affords communication with various I/O devices, including a keyboard 118, a mouse 120, a Compact Disk-Read Only Memory (CD-ROM) drive 122, a floppy disk drive 124, and a flash drive memory 126. The format of the ports connected to I/O interface 116 may be any known to those skilled in the art of computer architecture, including but not limited to Universal Serial Bus (USB) ports,” ¶ 0021.):
receive, via an application executing on a computing device of a user, a request for navigation assistance within the application, wherein the request includes a destination within the application (
Merrill discloses, “User input is then received from the user to select a desired destination location within the application,” ¶ 0007, and “In these and other embodiments, a request 202 for look-ahead navigation assistance is received by the look-ahead navigation module 150,” ¶ 0028.);
identify, based on the request, a current node of the user within the application (
PNG
media_image1.png
435
599
media_image1.png
Greyscale
Merrill discloses, “In this embodiment, navigation look-ahead operations are begun in step 502, followed by receiving user input requesting navigation look-ahead assistance in step 504. In various embodiments, the request is received as a user gesture, such as a right-mouse click with a cursor on a graphical element within a user interface. In step 506, the user's current location within the application is determined,” ¶ 0035.
The claimed “current node” is mapped to the disclosed “current location within the application”.
Fig. 3 shows that the possible locations are node locations.);
;
generate a first user interface including a first selectable option, wherein the first selectable option is associated with a first node along the optimized navigation route to the destination node associated with the destination within the application (
Merrill discloses, “State information associated with the user's current location is then processed in step 508 to generate possible destination locations within the application. The resulting possible location destinations within the application are then displayed to the user in step 510. In various embodiments, the user's current location and the possible destination locations are contextually displayed to the user within a user interface as graphical elements,” ¶ 0035.);
send, to the computing device, the first user interface including the first selectable option, wherein sending the first user interface causes the computing device to display the first user interface on a display of the computing device (
Merrill discloses, “State information associated with the user's current location is then processed in step 508 to generate possible destination locations within the application. The resulting possible location destinations within the application are then displayed to the user in step 510. In various embodiments, the user's current location and the possible destination locations are contextually displayed to the user within a user interface as graphical elements,” ¶ 0035.);
receive, from the computing device, user selection of the first selectable option (
Merrill discloses, “In step 512, user input is received from the user to select a desired destination location within the application,” ¶ 0036.);
responsive to receiving the user selection of the first selectable option, generate a second user interface including a second selectable option, wherein the second selectable option is associated with a second node along the optimized navigation route to the destination node associated with the destination within the application (
Merrill discloses, “However, if it is determined in step 514 to traverse the application to the selected destination location, then the application is traversed in step 516 and the selected destination location is displayed to the user. The user then performs operations associated with the selected destination location in step 520, followed by a determination being made in step 522 whether the operations have been completed. If not, the process is continued, proceeding with step 520. Otherwise, a determination is made in step 524 whether to continue navigation look-ahead operations. If so, then the process is continued, proceeding with step 504,” ¶ 0037, and “In this embodiment, navigation look-ahead operations are begun in step 502, followed by receiving user input requesting navigation look-ahead assistance in step 504… The resulting possible location destinations within the application are then displayed to the user in step 510. In various embodiments, the user's current location and the possible destination locations are contextually displayed to the user within a user interface as graphical elements,” ¶ 0035.
In this example, the claimed “second selectable option” is mapped to the disclosed second location that can be selected after the user chooses to continue the process in step 524.);
and send, to the computing device, the second user interface including the second selectable option, wherein sending the second user interface causes the computing device to display the second user interface on the display of the computing device (
Merrill discloses, “However, if it is determined in step 514 to traverse the application to the selected destination location, then the application is traversed in step 516 and the selected destination location is displayed to the user. The user then performs operations associated with the selected destination location in step 520, followed by a determination being made in step 522 whether the operations have been completed. If not, the process is continued, proceeding with step 520. Otherwise, a determination is made in step 524 whether to continue navigation look-ahead operations. If so, then the process is continued, proceeding with step 504,” ¶ 0037, and “In this embodiment, navigation look-ahead operations are begun in step 502, followed by receiving user input requesting navigation look-ahead assistance in step 504… The resulting possible location destinations within the application are then displayed to the user in step 510. In various embodiments, the user's current location and the possible destination locations are contextually displayed to the user within a user interface as graphical elements,” ¶ 0035.
In this example, the user chooses to continue the process in step 524, and a second location is selected after then first location.).
Merrill does not teach to train, using customer application navigation logs, a machine learning model, wherein training the machine learning model causes the machine learning model to identify nodes within an application and edges between nodes indicating a connection between the nodes and wherein the customer application navigation logs further include a purpose of a visit to a respective node;
or to execute the machine learning model to identify an optimized navigation route within the application from the current node of the user to a destination node associated with the destination within the application, wherein executing the machine learning model includes using, as inputs to the machine learning model, the current node and the destination node, to output the optimized navigation route based on the identified nodes and edges between the nodes.
However, Rizi teaches to trainconnection between the nodes (
Rizi discloses, “Finding shortest path distances between nodes in a graph is an important primitive in a variety of applications. For instance, the number of links between two URLs indicates page similarity in a graph of the Web [1],” Page 1, and “Let G = (V, E) be an unweighted undirected graph with n nodes and m edges. Graph embedding techniques create a real-valued, the so called vector embedding φ(v) ∈ Rd for every node v ∈ V. Given a pair of nodes u, v ∈ V with the real shortest path distance du,v, the goal is to approximate the distance as ˆd using a feedforward neural network… To train the neural network, we need to extract training pairs from the entire graph G,” Page 3.
Here, a neural network (a type of machine learning model) is trained on possible paths within a graph.);
and to execute the machine learning model to identify an optimized navigation route within the application from the current node of the user to a destination node associated with the destination within the application, wherein executing the machine learning model includes using, as inputs to the machine learning model, the current node and the destination node, to output the optimized navigation route based on the identified nodes and edges between the nodes (
Rizi discloses, “Finding shortest path distances between nodes in a graph is an important primitive in a variety of applications. For instance, the number of links between two URLs indicates page similarity in a graph of the Web [1],” Page 1, and “Let G = (V, E) be an unweighted undirected graph with n nodes and m edges. Graph embedding techniques create a real-valued, the so called vector embedding φ(v) ∈ Rd for every node v ∈ V. Given a pair of nodes u, v ∈ V with the real shortest path distance du,v, the goal is to approximate the distance as ˆd using a feedforward neural network,” Page 3.).
Merrill and Rizi are both considered to be analogous to the claimed invention because they are in the same field of network navigation. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified Merrill to incorporate the teachings of Rizi and provide to train a machine learning model, wherein training the machine learning model causes the machine learning model to identify nodes within an application and edges between nodes indicating a connection between the nodes; and to execute the machine learning model to identify an optimized navigation route within the application from the current node of the user to a destination node associated with the destination within the application, wherein executing the machine learning model includes using, as inputs to the machine learning model, the current node and the destination node, to output the optimized navigation route based on the identified nodes and edges between the nodes. Doing so would help reduce the time taken to identify the optimized navigation route. (Rizi discloses, “We show that neural networks can predict the shortest path distances effectively and efficiently, especially for shorter paths (section IV),” Page 1.).
Merrill in view of Rizi does not teach to train, using customer application navigation logs, a machine learning model, wherein training the machine learning model causes the machine learning model to identify nodes within an application and edges between nodes indicating a connection between the nodes and wherein the customer application navigation logs further include a purpose of a visit to a respective node.
However, Velez-Rojas teaches to train, using customer application navigation logs, a machine learning model, wherein training the machine learning model causes the machine learning model to identify nodes within an application and edges between nodes indicating a connection between the nodes and wherein the customer application navigation logs further include a purpose of a visit to a respective node (
Velez-Rojas discloses, “Some embodiments select graphs for the dashboards with machine learning models. In some embodiments, the models are trained to select graphs based on features and patterns found in the data being analyzed, heuristics from user preferences and role requirements, as well as behavior patterns exhibited during task performance. … Some embodiments automatically generate dashboards according to characteristics of the data sources (e.g., with a data driven approach described below), user preferences, roles and task heuristics (e.g., with a user-driven approach described below), and problem-solving strategies (e.g., with a behavior driven approach described below),” ¶ 0021, and
“In some embodiments, the system processes new users by creating dashboards based on organizational heuristics and then learning over time from user preferences and feedback. In some embodiments, the system learns over time based on individual preferences and allows users to share those preferences in a social media setting by maintaining a graph of user accounts in memory and passing messages between user account nodes of the graph with the shared content. Some embodiments may receive a user request to share their dashboard template or to add annotations based on the type of tasks for which the template is useful, and those requests may be logged for subsequent training of the system,” ¶ 0046, and
“Some embodiments log these user inputs and the state of the system when the input was entered, and these logs may be used as a training set to learn from user behavior. Examples of such inputs include records from eye tracking technology to detect the most used visualization, the sequence in which graphs are seen, user navigation between graphs, user interaction with graphs, and the like. Based on trained models, some embodiments may infer details about the user's problem solving process and take predictive action in the future.,” ¶ 0047, and
“Some embodiments provide for dashboard sharing in which the system learns which dashboard graphs are preferably shared among individuals. Some embodiments records for each task: the task, logs and databases and recommended and preferred methods for visualizing data; the other roles/persons brought into solving the task and which visualizations are shared,” ¶ 0084.
The claimed “customer application navigation logs” is mapped to the disclosed “logs”, which are used for training a machine learning model. Said logs can include information regarding tasks, which indicates a purpose that a user aims to accomplish.
After the combination of Merrill in view of Rizi, with Velez-Rojas, the logs from Velez-Rojas, which contain task-related data, are inputted into the machine learning model from Merrill in view of Rizi for training.).
Merrill in view of Rizi, and Velez-Rojas are both considered to be analogous to the claimed invention because they are in the same field of network navigation. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified Merrill in view of Rizi to incorporate the teachings of Velez-Rojas and provide to train, using customer application navigation logs, a machine learning model, wherein training the machine learning model causes the machine learning model to identify nodes within an application and edges between nodes indicating a connection between the nodes and wherein the customer application navigation logs further include a purpose of a visit to a respective node. Doing so would help improve the training of the machine learning model based on the additional information provided by the logs of real activities.
Claims 9 and 17 are a method and non-transitory computer-readable medium claim (¶ 0016 of Merrill.), respectively corresponding to the computing platform Claim 1. Therefore, Claims 9 and 17 are rejected for the same reasons set forth in the rejection of Claim 1.
Regarding Claim 2, Merrill in view of Rizi and Velez-Rojas teaches the computing platform of claim 1, wherein the second node along the optimized navigation route is the destination node (
Merrill discloses, “However, if it is determined in step 514 to traverse the application to the selected destination location, then the application is traversed in step 516 and the selected destination location is displayed to the user. The user then performs operations associated with the selected destination location in step 520, followed by a determination being made in step 522 whether the operations have been completed. If not, the process is continued, proceeding with step 520. Otherwise, a determination is made in step 524 whether to continue navigation look-ahead operations. If so, then the process is continued, proceeding with step 504. Otherwise, navigation look-ahead operations are ended in step 526,” ¶ 0037.).
Claims 10 and 18 are a method and non-transitory computer-readable medium claim (¶ 0016 of Merrill.), respectively corresponding to the computing platform Claim 2. Therefore, Claims 10 and 18 are rejected for the same reasons set forth in the rejection of Claim 2.
Claims 3-5, 11-13, and 19 are rejected under 35 U.S.C. 103 as being unpatentable over Merrill (US 20110161887 A1) in view of Rizi (“Shortest Path Distance Approximation using Deep learning Techniques”), Velez-Rojas (US 20180174060 A1), and Catalano (US 20220397989 A1).
Regarding Claim 3, Merrill in view of Rizi and Velez-Rojas teaches the computing platform of claim 1. Merrill in view of Rizi and Velez-Rojas does not teach wherein the first selectable option is available for selection and wherein other options on the first user interface are not available for selection.
However, Catalano teaches wherein the first selectable option is available for selection and wherein other options on the first user interface are not available for selection (
Catalano discloses, “In response to determining that the third-party resource is excluded or not included in the list of approved third-party resources (e.g., by comparing a unique identifier of the third-party resource with unique identifiers of the list of approved third-party resources to failing to find a matching identifier), the messaging client 104 disables or prevents selection of the option to share the content with the messaging application playlist feature,” ¶ 0039.).
Merrill in view of Rizi and Velez-Rojas, and Catalano are both considered to be analogous to the claimed invention because they are in the same field of computer systems. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified Merrill in view of Rizi and Velez-Rojas to incorporate the teachings of Catalano and provide wherein the first selectable option is available for selection and wherein other options on the first user interface are not available for selection. Doing so would help ensure greater security. (Catalano discloses, “In response to determining that the third-party resource is excluded or not included in the list of approved third-party resources (e.g., by comparing a unique identifier of the third-party resource with unique identifiers of the list of approved third-party resources to failing to find a matching identifier), the messaging client 104 disables or prevents selection of the option to share the content with the messaging application playlist feature,” ¶ 0039.).
Claims 11 and 19 are a method and non-transitory computer-readable medium claim (¶ 0016 of Merrill.), respectively corresponding to the computing platform Claim 3. Therefore, Claims 11 and 19 are rejected for the same reasons set forth in the rejection of Claim 3.
Regarding Claim 4, Merrill in view of Rizi, Velez-Rojas, and Catalano teaches the computing platform of claim 3, wherein the other options are disabled (
Catalano discloses, “In response to determining that the third-party resource is excluded or not included in the list of approved third-party resources (e.g., by comparing a unique identifier of the third-party resource with unique identifiers of the list of approved third-party resources to failing to find a matching identifier), the messaging client 104 disables or prevents selection of the option to share the content with the messaging application playlist feature,” ¶ 0039.).
Merrill in view of Rizi and Velez-Rojas, and Catalano are both considered to be analogous to the claimed invention because they are in the same field of computer systems. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified Merrill in view of Rizi and Velez-Rojas to incorporate the teachings of Catalano and provide wherein the other options are disabled. Doing so would help ensure greater security. (Catalano discloses, “In response to determining that the third-party resource is excluded or not included in the list of approved third-party resources (e.g., by comparing a unique identifier of the third-party resource with unique identifiers of the list of approved third-party resources to failing to find a matching identifier), the messaging client 104 disables or prevents selection of the option to share the content with the messaging application playlist feature,” ¶ 0039.).
Claim 12 is a method claim corresponding to the computing platform Claim 4. Therefore, Claim 12 is rejected for the same reasons set forth in the rejection of Claim 4.
Regarding Claim 5, Merrill in view of Rizi, Velez-Rojas, and Catalano teaches the computing platform of claim 3, wherein the other options have a modified appearance in the first user interface (
Catalano discloses, “In this case, the option may be displayed in a greyed out manner or with a visual distinguishing attribute indicate that the option is disabled,” ¶ 0039.).
Merrill in view of Rizi and Velez-Rojas, and Catalano are both considered to be analogous to the claimed invention because they are in the same field of computer systems. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified Merrill in view of Rizi and Velez-Rojas to incorporate the teachings of Catalano and provide wherein the first selectable option is available for selection and wherein the other options have a modified appearance in the first user interface. Doing so would help ensure greater security. (Catalano discloses, “In response to determining that the third-party resource is excluded or not included in the list of approved third-party resources (e.g., by comparing a unique identifier of the third-party resource with unique identifiers of the list of approved third-party resources to failing to find a matching identifier), the messaging client 104 disables or prevents selection of the option to share the content with the messaging application playlist feature,” ¶ 0039.)
Claim 13 is a method claim corresponding to the computing platform Claim 5. Therefore, Claim 13 is rejected for the same reasons set forth in the rejection of Claim 5.
Claims 6-8, 14-16, and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Merrill (US 20110161887 A1) in view of Rizi (“Shortest Path Distance Approximation using Deep learning Techniques”), Velez-Rojas (US 20180174060 A1), and Talpur (“Congestion Detection in Software Defined Networks using Machine Learning”).
Regarding Claim 6, Merrill in view of Rizi and Velez-Rojas teaches the computing platform of claim 1. Merrill in view of Rizi and Velez-Rojas does not teach further including instructions that, when executed, cause the computing platform to: generate a request for computer resource availability data; send, to one or more computing systems, the request for computer resource availability data; and receive, from the one or more computing systems, computer resource availability response data, wherein executing the machine learning model to identify the optimized navigation route within the application from the current node of the user to the destination node associated with the destination within the application, further includes using, as inputs to the machine learning model, the computer resource availability response data.
However, Talpur teaches further including instructions that, when executed, cause the computing platform to: generate a request for computer resource availability data (
Talpur discloses, “Controller-to-switch Messages: Receiving this type of messages from controller, a switch may or may not respond to the controller. Example of this type of messages are, Features, Configuration, Packet-out , Barrier etc. At the start up of a connection with the controller, a request in the form Features message is sent to a switch that is used to identify the capabilities of a switch. The Packet-out messages are sent to the controller in response to packet-in messages. Barrier messages are used to check dependencies between switch and controller,” Page 17, and “Port-status messages indicate that a port is available, or unavailable due to the link being down,” Page 17.);
send, to one or more computing systems, the request for computer resource availability data (
Talpur discloses, “At the start up of a connection with the controller, a request in the form Features message is sent to a switch that is used to identify the capabilities of a switch. The Packet out messages are sent to the controller in response to packet-in messages,” Page 17.);
and receive, from the one or more computing systems, computer resource availability response data, wherein executing the machine learning model to identify the optimized navigation route within the application from the current node of the user to the destination node associated with the destination within the application, further includes using, as inputs to the machine learning model, the computer resource availability response data (
Talpur teaching determining link failure, disclosing “Features such as, inter-arrival times, one-way delay, RTT and the number of packets lost are mostly used in the literature. Based on the work in [118], in this report, one-way delay and inter-arrival time are selected as features for congestion detection, and only the one-way delay feature is used for building a multiple flows detection classifier,” Page 46.
Talpur teaches computer resources such as data links, and the availability of said data links, disclosing “In data plane restoration, whenever a link fails, a list of affected paths is created to calculate a restoration path using the shortest path algorithm on the available links,” Page 19.
Talpur already teaches using the computer resource availability to calculate an optimized path with the available links, resulting in a more accurate prediction. After the combination of Merrill in view of Rizi and Velez-Rojas, with Talpur, Rizi’s machine learning model could be used to automate Talpur’s process, by having the model be trained using Talpur’s computer resource availability, and then predicting the optimized path accordingly.
Further, Rizi discloses, “Finding shortest path distances between nodes in a graph is an important primitive in a variety of applications. For instance, the number of links between two URLs indicates page similarity in a graph of the Web [1],” Page 1, and “Let G = (V, E) be an unweighted undirected graph with n nodes and m edges. Graph embedding techniques create a real-valued, the so called vector embedding φ(v) ∈ Rd for every node v ∈ V. Given a pair of nodes u, v ∈ V with the real shortest path distance du,v, the goal is to approximate the distance as ˆd using a feedforward neural network,” Rizi, Page 3.).
Merrill in view of Rizi and Velez-Rojas, and Talpur are both considered to be analogous to the claimed invention because they are in the same field of network navigation. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified Merrill in view of Rizi and Velez-Rojas to incorporate the teachings of Talpur and provide further including instructions that, when executed, cause the computing platform to: generate a request for computer resource availability data; send, to one or more computing systems, the request for computer resource availability data; and receive, from the one or more computing systems, computer resource availability response data, wherein executing the machine learning model to identify the optimized navigation route within the application from the current node of the user to the destination node associated with the destination within the application, further includes using, as inputs to the machine learning model, the computer resource availability response data. Doing so would help allow for optimized paths when taking the resource availability into account (Talpur discloses, “In data plane restoration, whenever a link fails, a list of affected paths is created to calculate a restoration path using the shortest path algorithm on the available links,” Page 19.).
Claims 14 and 20 are a method and non-transitory computer-readable medium claim (¶ 0016 of Merrill.), respectively corresponding to the computing platform Claim 6. Therefore, Claims 14 and 20 are rejected for the same reasons set forth in the rejection of Claim 6.
Regarding Claim 7, Merrill in view of Rizi, Velez-Rojas, and Talpur teaches the computing platform of claim 6, wherein the optimized navigation route is a shortest route from the current node to the destination node (
Rizi discloses, “Let G = (V, E) be an unweighted undirected graph with n nodes and m edges. Graph embedding techniques create a real-valued, the so called vector embedding φ(v) ∈ Rd for every node v ∈ V. Given a pair of nodes u, v ∈ V with the real shortest path distance du,v, the goal is to approximate the distance as ˆd using a feedforward neural network,” Page 3.).
Merrill and Rizi are both considered to be analogous to the claimed invention because they are in the same field of network navigation. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified Merrill to incorporate the teachings of Rizi and provide wherein the optimized navigation route is a shortest route from the current node to the destination node. Doing so would help reduce the time taken to identify the optimized navigation route. (Rizi discloses, “We show that neural networks can predict the shortest path distances effectively and efficiently, especially for shorter paths (section IV),” Page 1.).
Claim 15 is a method claim corresponding to the computing platform Claim 7. Therefore, Claim 15 is rejected for the same reasons set forth in the rejection of Claim 7.
Regarding Claim 8, Merrill in view of Rizi, Velez-Rojas, and Talpur teaches the computing platform of claim 6, wherein the optimized navigation route is a shortest route from the current node to the destination node that avoids computer resources identified as unavailable in the computer resource availability response data (
Talpur discloses, “In data plane restoration, whenever a link fails, a list of affected paths is created to calculate a restoration path using the shortest path algorithm on the available links,” Page 19.).
Merrill in view of Rizi and Velez-Rojas, and Talpur are both considered to be analogous to the claimed invention because they are in the same field of network navigation. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified Merrill in view of Rizi and Velez-Rojas to incorporate the teachings of Talpur and provide wherein the optimized navigation route is a shortest route from the current node to the destination node that avoids computer resources identified as unavailable in the computer resource availability response data. Doing so would help allow for optimized paths when taking the resource availability into account (Talpur discloses, “In data plane restoration, whenever a link fails, a list of affected paths is created to calculate a restoration path using the shortest path algorithm on the available links,” Page 19.).
Claim 16 is a method claim corresponding to the computing platform Claim 8. Therefore, Claim 16 is rejected for the same reasons set forth in the rejection of Claim 8.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Vasseur et al. (US 20190116485 A1): Dynamic Rerouting of Wireless Traffic Based on Input From Machine Learning-based Mobility Path Analysis
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ANDREW SUN whose telephone number is (571)272-6735. The examiner can normally be reached Monday-Friday 8:00-5:00.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Aimee Li can be reached at (571) 272-4169. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/ANDREW NMN SUN/Examiner, Art Unit 2195
/Aimee Li/Supervisory Patent Examiner, Art Unit 2195