DETAILED ACTION
This communication is in response to the Amendments and Arguments filed on 03/25/2026.
Claim(s) 1-30 are pending and have been examined.
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 .
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 03/25/2026 has been entered.
Response to Arguments and Amendments
Amendments to the claims by the Applicant have been considered and addressed below.
With respect to the 35 USC § 101 and 103 rejections, the Applicant provides several arguments in which the Examiner will respond accordingly, below.
35 USC § 101 rejection(s)
Arguments in pages 11-17 of the Remarks filed on 03/25/2026.
Examiner’s Response to Arguments:
Applicant' s arguments with respect to the 35 USC § 101 rejection(s) have been fully considered and are persuasive. The 35 USC § 101 rejection(s) of claims 1-30 have been withdrawn.
35 USC § 103 rejection(s)
Arguments in pages 18-19 of the Remarks filed on 03/25/2026.
Examiner’s Response to Arguments:
Applicant’s arguments with respect to claim(s) 1, 9, and 20 under 35 U.S.C. § 103 have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
Therefore, the rejection has been withdrawn. However, upon further consideration, a new ground(s) of rejection is made in view of Johnson et al. (US 20210117972 A1) and further in view of Bae et al. (US 20140082501 A1) and Plouffe et al. (US 20050120160 A1).
The Examiner notes that Johnson et al. discloses:
processing, by a machine learning model that is trained using an infrastructure- specific training dataset comprising one or more infrastructure-specific server names, inputs (see ¶ [0042]: “In one example embodiment, the machine learning model is located on a backend server, e.g., backend server 112. The backend server can receive data from various sources. For example, the backend server can be connected to a plurality of servers (e.g., email server 102 or processor server 104) and/or the user's device, e.g., client device 111. The backend server can receive email, calendar entry, phone call, social media activity, and financial transaction data from these servers, e.g., data streams 101, 103. As another example, the backend server can receive data from the user's device or provide data to the user's device, e.g., data stream 105. The backend server can use this data to train the machine learning model. The backend server can also use this data to execute the rules with respect to incoming data points. For example, based on past transactions, a machine learning model generated a rule which requires restaurant transactions to be shared, i.e., divided in two and that a friend of the user pay for half of the bill. When a transaction at a restaurant is recorded, the backend server can execute the rule and require the transaction to be shared. If so, the backend server can transmit an email on behalf of the user to request a payment.”
Here, the Examiner notes that the server name is read by the names of the plurality of servers (e.g., email server), wherein Johnson, explicitly discloses using data (i.e., from various sources, such as, emails from the email servers) to train the machine learning model (which is mapped to the machine learning model of the instant application, not to the NLP as argued).) to
output, from the machine learning model, one or more infrastructure- specific rules (see ¶ [0042] citation as in limitation above, more specifically ¶ [0042]: “…For example, based on past transactions, a machine learning model generated a rule which requires restaurant transactions to be shared, i.e., divided in two and that a friend of the user pay for half of the bill. When a transaction at a restaurant is recorded, the backend server can execute the rule and require the transaction to be shared. If so, the backend server can transmit an email on behalf of the user to request a payment.”),
each infrastructure-specific rule including one or more triggers and one or more actions (see ¶ [0031]: “In an example embodiment, the dataset can include data relating to emails, calendar entries, phone calls, social media activities, and financial transactions. The NLP module can review this data and assign some or all the items to one or more data groups. By assigning these items to data groups, one can create training data for the machine learning model. Specifically, the machine learning model can learn about a trigger event that occurs first and one or more actions that follow the trigger event. The trigger event can be an email, calendar entry, phone call, social media activity, or financial transaction. The action that follows can be an email, calendar entry, phone call, social media activity, or financial transaction. The machine learning model can be provided with this training data, and using the training data, the machine learning model can generate various rules. For example, the machine learning model can be trained to generate rules which provide that when the trigger event occurs, the action must follow.”);
Bae et al. teaches:
processing inputs comprising, a natural-language expression (see Figs. 7-13 (e.g., from Fig. 7: “If I get in a taxi, send my location to father and little brother(sister) at every 5 minutes”) and ¶ [0191-0192]: “[0191] FIG. 7 shows an exemplary situation in which a user configures and executed a rule through speech interaction, and the user device 100 recognizes the condition specified in the rule and executes the action triggered when the condition is fulfilled. Particularly in FIG. 7, the user may define a rule of transmitting the user location (or location of the user device 100) to at least one target user device automatically at a predetermined interval. The user device 100 may execute the rule in response to a user input and perform the action of transmitting the location information to the at least one target user device at an interval specified in the execution rule. [0192] Referring to FIG. 7, the user may define the rule using the user device 100. For example, the user may activate the rule generation function (or application) by manipulating the user device 100 and define a rule of transmitting the location information to at least one target user device 200 at a predetermined interval. According to an exemplary embodiment of the present invention, the user may generate a rule of "if I take a taxi, then send my location information to my father and little brother (or sister) at every 5 minutes." At this time, the rule may be generated through speech interaction with the microphone 143 or text interaction with the input unit 120 or touchscreen 130 as to be described later. It is preferred that the speech and text interaction is based on a natural language as described above. The situation to be detected (e.g., condition), specified in the defined rule may be "when movement occurs", and the action to be taken in fulfillment of the condition may be "send location information to father and little brother (sister) at every 5 minutes."” and ¶ [0212]: “Referring to FIG. 9, the user may define a rule using the user device 100. For example, the user may generate a rule of outputting an alarm upon detection of an event caused by change in environment (hereinafter, first rule) and a rule of controlling a function of the user device 100 upon detection of an event caused by change in environment (hereinafter, second rule) with the activation of the function (or application) capable of generating the rules by manipulating the user device 100. According to an exemplary embodiment, it is possible to generate the first rule such as "output alarm when driving speed is equal to or greater than 80 Km/h" and the second rule such as "increase audio volume of the car or the user device 100 when the driving speed is equal to or greater than 60 Km/h." At this time, as described above, the rule may be defined by speech input through the microphone 143 or text input through the input unit 120 or touchscreen 130. The speech and text input may be made with a natural language. The situation to be detected (e.g., condition), may be "when change in environment (e.g., driving speed) is equal to or greater than a predetermined threshold," and the action to be taken in fulfillment of the condition may be "output alarm or control audio volume."”
Here, the Examiner notes that the processing of the natural language expression is associated with the processing of the user speech or text interaction with the device to define a rule as described above in Bae et al. Also, that the generation of the rule including trigger(s) and action(s) present in the natural language expression read on Bae et al.’s situation/condition (i.e., trigger) met to perform an action/fulfillment (i.e., action).) to
output one or more infrastructure- specific rules specified by the natural-language expression (see Figs. 7-13 (e.g., from Fig. 7: “If I get in a taxi, send my location to father and little brother(sister) at every 5 minutes”) and ¶ [0191-0192 and 0212] citations as in limitation above, more specifically: “[0191] FIG. 7 shows an exemplary situation in which a user configures and executed a rule through speech interaction, and the user device 100 recognizes the condition specified in the rule and executes the action triggered when the condition is fulfilled…)
each infrastructure-specific rule including one or more triggers and one or more actions specified by the natural-language expression (see Figs. 7-13 (e.g., from Fig. 7: “If I get in a taxi, send my location to father and little brother(sister) at every 5 minutes”) and ¶ [0191-0192 and 0212] citations as in limitation above.
Here, the Examiner notes, similar to what was discussed in the limitations above, that the generation of the rule including trigger(s) and action(s) present in the natural language expression read on Bae et al.’s situation/condition (i.e., trigger) met to perform an action/fulfillment (i.e., action).)
Plouffe et al. teaches:
the first infrastructure-specific rule comprising an infrastructure-specific server name for the server (see ¶ [0061]: “FIG. 1 shows one example system 101 that may be used to execute one or more data center applications. System 101 may include one or more system layers providing layers of abstraction between programming entities. As discussed above, a virtualization layer 104 is provided that isolates applications on a guest operating system (GOS) operating in layers 102 and 103, respectively, from an underlying hardware layer 105. Such applications may be, for example, any application program that may operate in a data center environment. For instance, a database server application, web-based application, e-mail server, file server, or other application that provides resources to other systems (e.g., systems 107A-107C) may be executed on system 101…” and
¶ [0090 and 0097]: “[0090] Management actions may be grouped into a set and executed as single transaction, referred to herein as a "job." For instance, a particular job may include multiple management actions that are performed as part of an overall management function. For example, a virtual server may need to be stopped prior to adding resources (e.g., memory) and then restarted. Therefore, a command for stopping the virtual server may need to be executed, followed by a command which adds the particular resource, and then a start command needs to be executed. As discussed, a transaction history may be maintained (e.g., by the management server or other entity) that permits transactions to be rolled back in certain situations. For example, if, after a job is executed, an error occurs, the job may be rolled back prior to the point when the error occurred. Such a rollback may be initiated, for example, by an administrator using an interface program, by a management system, or by any other program or system. […] [0097] The ability to manage the mapping of storage resources, such as one or more disks and one or more World Wide Node Name (WWNN) identifiers to one or more virtual servers (VSs).” and
¶ [0137]: “Further, there may be a "pool" of identifiers associated with virtual I/O entities such as, for example, virtual network interfaces or virtual storage adapters. These virtual identifiers may include, for example, network addresses such as MAC addresses or storage adapter identifiers such as World Wide Node Name (WWNN) identifiers. As discussed, the management server may maintain a pool of these identifiers, and assign one or more identifiers as virtual servers and other resources are created.”) and
transmitting instructions that cause the server to perform a server management action specified by the first infrastructure-specific rule based on the occurrence of at least one of the one or more triggers (see ¶ [0061, 0090, 0097, and 0137] citations as in limitation above and further
¶ [0170-0183]: “[0170] FIG. 21 shows a diagram of a policy 2100 according to one embodiment of the present invention. Policy 2100 may include one or more rules 2101-2103. These rules may be attached to one or more managed objects, such as virtual mainframes, virtual partitions and virtual servers, in the virtual computing system. Rules may include parameters that define what performance or configuration information trigger actions that change the configuration or operating parameters of the virtual server. [0171] For example, rule 2103 may comprise a number of elements, including operator 2105, parameter 2106, relation 2107, parameter value 2108, and action 2109. Operator 2105 may indicate, for instance, what condition rule 2103 will apply, and may be expressed as a logical operator such as IF, WHILE, etc. Operator 2105, along with other values, may determine whether 2103 is triggered. Parameter 2106 may be a generalized parameter (e.g. CPU utilization) that, when attached to a hardware or virtual element instance (or group of instances) within the virtual computing system, allows that particular instance or group of instances to act as a trigger for a particular rule. In one embodiment, rules may be selectively attached to particular instances within the virtual computing system. [0172] Rule 2103 may also comprise a relation 2107 that relates parameter 2106 to a parameter value 2108. The relation may be, for example, a mathematical relation such as greater than, less than or equal to, greater than or equal to, within, or any other relation (e.g., mathematical, Boolean, etc.). Also, relations may be combined to create hierarchical rule sets. Parameter value 2108 may correspond to a value of parameter 2106, when satisfied by the relation, one or more actions 2109 may be performed. For example, if a parameter 2106 such as the CPU utilization on a particular virtual server needs a relation 2107 of greater than a parameter value 2108 on 85% then an action 2109 of a SCALEUP may be performed on the particular virtual server. Rule 2103 may trigger one or more actions 2109, depending on what functions need to be performed. [0173] Actions 2109 may include, for example, a SCALEUP action which adds resources to a particular virtual server, a SCALEDOWN action that subtracts one or more resources from a virtual server, a REPAIR action which may, for example, replace a particular resource with a spare resource within the virtual computing system. Resources, as discussed above, may be associated with one or more physical nodes. Thus, a SCALEUP action may add resources from a physical node not previously associated with a particular virtual server. In this manner, the capabilities of the virtual server may be expanded. [0174] Also, a SHUTDOWN action may be performed that shuts down a particular virtual server. […] [0178] For CPU utilization average>70% for 10 minutes, add another node to the virtual partition [0179] For CPU utilization average <30% for 10 minutes, remove a node from the virtual partition [0180] Although the above examples show performance-triggered policies, it should be appreciated that, according to one embodiment of the invention, different types of triggers may be used to initiate an action. [0181] For example, according to one embodiment, actions may be started based on the following triggers: (Table III) [0182] A trigger generally may include an object's property, event or method result, time or user-defined script. As outlined in Table III above, triggers may be defined for performance issues (e.g., resource utilization) or failures, as measured instantaneously or over some period of time. Other triggers may be time-based. For example, a trigger may cause an action to occur immediately, or at some other time. There may be other types of triggers that relate to other events (e.g., a user defined trigger based on one or more parameters, an event-based trigger such as a "node availability" trigger that is fired when a particular node or nodes become available). Any number and type of triggers may be defined, using any type of interface to the management server. [0183] Based on the triggers as discussed above, actions as shown below in Table IV may be available to be performed on the defined objects: (Table IV)”
Also, the Examiner notes that no agreement as to whether “Johnson is completely silent with regard to training an NLP module using an infrastructure-specific training dataset that includes an infrastructure-specific server name, as required by the limitations of amended claim 1” was made during the interview of February 25, 2026. Instead, the Examiner notes that during said interview, the Examiner noted that language associated with “infrastructure-specific server name” would potentially help overcoming the prior art but further consideration and an updated search would be needed. Hence, after further consideration and updating the search, a rejection is made in view of Johnson et al. (US 20210117972 A1) and further in view of Bae et al. (US 20140082501 A1) and Plouffe et al. (US 20050120160 A1).
For more details, please refer to updated 35 U.S.C. § 103 rejections for claims 1-30, below.
Claim Objections
Claims 2, 10, and 21 objected to because of the following informalities:
The limitations of claim 2 of:
monitoring a current resource utilization of one or more nodes of the deployed infrastructure,
monitoring a resource exhaustion of one or more resources of the deployed infrastructure,
monitoring a schedule of one or more applications or services executed by one or more nodes of the deployed infrastructure,
monitoring a performance of one or more services executed by one or more nodes of the deployed infrastructure, or monitoring a latency of the one or more services executed by one or more nodes of the deployed infrastructure.
should read:
monitoring a current resource utilization of one or more nodes of the deployed infrastructure, monitoring a resource exhaustion of one or more resources of the deployed infrastructure,
monitoring a schedule of one or more applications or services executed by the one or more nodes of the deployed infrastructure,
monitoring a performance of one or more services executed by the one or more nodes of the deployed infrastructure, or
monitoring a latency of the one or more services executed by the one or more nodes of the deployed infrastructure.
The limitations of claims 10 and 21 of:
monitoring a current resource utilization of one or more nodes of the infrastructure,
monitoring a resource exhaustion of one or more resources of the infrastructure,
monitoring a schedule of one or more applications or services executed by one or more nodes of the infrastructure,
monitoring a performance of one or more services executed by one or more nodes of the infrastructure, or monitoring a latency of the one or more services executed by one or more nodes of the infrastructure.
should read:
monitoring a current resource utilization of one or more nodes of the infrastructure,
monitoring a resource exhaustion of the one or more resources of the infrastructure,
monitoring a schedule of one or more applications or services executed by one or more nodes of the infrastructure,
monitoring a performance of one or more services executed by the one or more nodes of the infrastructure, or
monitoring a latency of the one or more services executed by the one or more nodes of the infrastructure.
Appropriate correction is required.
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 20-21 is/are rejected under 35 U.S.C. 103 as being unpatentable over Johnson et al. (US 20210117972 A1) and further in view of Bae et al. (US 20140082501 A1) and Plouffe et al. (US 20050120160 A1).
As to independent claim 1, Johnson et al. teaches:
1. One or more non-transitory computer-readable media storing program instructions (see ¶ [0059]: “…In addition, although aspects of an implementation consistent with the above are described as being stored in a memory, one skilled in the art will appreciate that these aspects can also be stored on or read from other types of computer program products or computer-readable media, such as secondary storage devices, including hard disks, floppy disks, or CD-ROM; or other forms of RAM or ROM. The computer-readable media may include instructions for controlling the computer system 600, to perform a particular method, such as methods described above.”) that, when executed by one or more processors (see ¶ [0056]: “Also, as noted, processor 606 may execute one or more software applications to provide the functions described in this specification, specifically to execute and perform the steps and functions in the process flows described above…”), cause the one or more processors to perform a method (see Fig. 1 and ¶ [0005]: “…The system can additionally include a streaming data platform that exposes the transaction information to the rules platform such that the rules platform is able to examine the transaction information, determine whether a transaction included in the transaction information is the shared transaction based on the rule, and generate a rule trigger for the shared transaction.”) comprising:
processing, by a machine learning model that is trained using an infrastructure- specific training dataset comprising one or more infrastructure-specific server names, inputs (see ¶ [0042]: “In one example embodiment, the machine learning model is located on a backend server, e.g., backend server 112. The backend server can receive data from various sources. For example, the backend server can be connected to a plurality of servers (e.g., email server 102 or processor server 104) and/or the user's device, e.g., client device 111. The backend server can receive email, calendar entry, phone call, social media activity, and financial transaction data from these servers, e.g., data streams 101, 103. As another example, the backend server can receive data from the user's device or provide data to the user's device, e.g., data stream 105. The backend server can use this data to train the machine learning model. The backend server can also use this data to execute the rules with respect to incoming data points. For example, based on past transactions, a machine learning model generated a rule which requires restaurant transactions to be shared, i.e., divided in two and that a friend of the user pay for half of the bill. When a transaction at a restaurant is recorded, the backend server can execute the rule and require the transaction to be shared. If so, the backend server can transmit an email on behalf of the user to request a payment.”
Here, the Examiner notes that the server name is read by the names of the plurality of servers (e.g., email server), wherein Johnson, explicitly discloses using data (i.e., from various sources, such as, emails from the email servers) to train the machine learning model (which is mapped to the machine learning model of the instant application).) to
output, from the machine learning model, one or more infrastructure- specific rules (see ¶ [0042] citation as in limitation above, more specifically ¶ [0042]: “…For example, based on past transactions, a machine learning model generated a rule which requires restaurant transactions to be shared, i.e., divided in two and that a friend of the user pay for half of the bill. When a transaction at a restaurant is recorded, the backend server can execute the rule and require the transaction to be shared. If so, the backend server can transmit an email on behalf of the user to request a payment.”),
each infrastructure-specific rule including one or more triggers and one or more actions (see ¶ [0031]: “In an example embodiment, the dataset can include data relating to emails, calendar entries, phone calls, social media activities, and financial transactions. The NLP module can review this data and assign some or all the items to one or more data groups. By assigning these items to data groups, one can create training data for the machine learning model. Specifically, the machine learning model can learn about a trigger event that occurs first and one or more actions that follow the trigger event. The trigger event can be an email, calendar entry, phone call, social media activity, or financial transaction. The action that follows can be an email, calendar entry, phone call, social media activity, or financial transaction. The machine learning model can be provided with this training data, and using the training data, the machine learning model can generate various rules. For example, the machine learning model can be trained to generate rules which provide that when the trigger event occurs, the action must follow.”);
executing a monitoring engine that monitors a server in that monitors a server in a deployed infrastructure to detect an occurrence of at least one of the one or more triggers of a first infrastructure- specific rule of the one or more infrastructure- specific rules (see ¶ [0031] citation as in limitation above, more specifically: “In an example embodiment, the dataset can include data relating to emails, calendar entries, phone calls, social media activities, and financial transactions. The NLP module can review this data and assign some or all the items to one or more data groups. By assigning these items to data groups, one can create training data for the machine learning model… For example, the machine learning model can be trained to generate rules which provide that when the trigger event occurs, the action must follow.”, and further
¶ [0014-15]: “[0014] In some embodiments, the dataset can include data points relating to incoming and/or outgoing emails from a user's account or device. The data relating to each email can include a sender's email address, at least one recipient's email address, a subject line, a body, a transmission time, and optionally one or more attachments. [0015] In some embodiments, the dataset can include data points relating to other electronic communication items such as calendar entries (or invites), instant messaging communications, name of an application run on a client device, etc.” and
¶ [0042-0043]: “[0042] In one example embodiment, the machine learning model is located on a backend server, e.g., backend server 112. The backend server can receive data from various sources. For example, the backend server can be connected to a plurality of servers (e.g., email server 102 or processor server 104) and/or the user's device, e.g., client device 111. The backend server can receive email, calendar entry, phone call, social media activity, and financial transaction data from these servers, e.g., data streams 101, 103. As another example, the backend server can receive data from the user's device or provide data to the user's device, e.g., data stream 105. The backend server can use this data to train the machine learning model. The backend server can also use this data to execute the rules with respect to incoming data points. For example, based on past transactions, a machine learning model generated a rule which requires restaurant transactions to be shared, i.e., divided in two and that a friend of the user pay for half of the bill. When a transaction at a restaurant is recorded, the backend server can execute the rule and require the transaction to be shared. If so, the backend server can transmit an email on behalf of the user to request a payment. [0043] As another example, the backend server can trigger an action on the user's device (or the client device). The backend server can send a message to the client device and the message is configured to trigger an action contemplated by an applicable rule. For example, in response to receiving an email about a weather alert, the applicable rule requires initiation of a weather application on the client device. The backend server can be notified about this incoming email, e.g., through a communication with the user's email server. This can trigger the rule. In response, the backend server can transmit a message to the client device. This message can trigger initiation of the weather application on the user's device.”); and
transmitting instructions that cause the server to perform [an] action specified by the first infrastructure- specific rule based on the occurrence of at least one of the one or more triggers (see ¶ [0014-0015, 0031, and 0042-0043] citation(s) as in limitation(s) above, more specifically ¶ [0031]: “…Specifically, the machine learning model can learn about a trigger event that occurs first and one or more actions that follow the trigger event. The trigger event can be an email, calendar entry, phone call, social media activity, or financial transaction. The action that follows can be an email, calendar entry, phone call, social media activity, or financial transaction. The machine learning model can be provided with this training data, and using the training data, the machine learning model can generate various rules. For example, the machine learning model can be trained to generate rules which provide that when the trigger event occurs, the action must follow.” and
¶ [0042]: “…The backend server can also use this data to execute the rules with respect to incoming data points. For example, based on past transactions, a machine learning model generated a rule which requires restaurant transactions to be shared, i.e., divided in two and that a friend of the user pay for half of the bill. When a transaction at a restaurant is recorded, the backend server can execute the rule and require the transaction to be shared. If so, the backend server can transmit an email on behalf of the user to request a payment.”)
However, Johnson et al. does not explicitly teach, but Bae et al. does teach:
processing inputs comprising, a natural-language expression (see Figs. 7-13 (e.g., from Fig. 7: “If I get in a taxi, send my location to father and little brother(sister) at every 5 minutes”) and ¶ [0191-0192]: “[0191] FIG. 7 shows an exemplary situation in which a user configures and executed a rule through speech interaction, and the user device 100 recognizes the condition specified in the rule and executes the action triggered when the condition is fulfilled. Particularly in FIG. 7, the user may define a rule of transmitting the user location (or location of the user device 100) to at least one target user device automatically at a predetermined interval. The user device 100 may execute the rule in response to a user input and perform the action of transmitting the location information to the at least one target user device at an interval specified in the execution rule. [0192] Referring to FIG. 7, the user may define the rule using the user device 100. For example, the user may activate the rule generation function (or application) by manipulating the user device 100 and define a rule of transmitting the location information to at least one target user device 200 at a predetermined interval. According to an exemplary embodiment of the present invention, the user may generate a rule of "if I take a taxi, then send my location information to my father and little brother (or sister) at every 5 minutes." At this time, the rule may be generated through speech interaction with the microphone 143 or text interaction with the input unit 120 or touchscreen 130 as to be described later. It is preferred that the speech and text interaction is based on a natural language as described above. The situation to be detected (e.g., condition), specified in the defined rule may be "when movement occurs", and the action to be taken in fulfillment of the condition may be "send location information to father and little brother (sister) at every 5 minutes."” and ¶ [0212]: “Referring to FIG. 9, the user may define a rule using the user device 100. For example, the user may generate a rule of outputting an alarm upon detection of an event caused by change in environment (hereinafter, first rule) and a rule of controlling a function of the user device 100 upon detection of an event caused by change in environment (hereinafter, second rule) with the activation of the function (or application) capable of generating the rules by manipulating the user device 100. According to an exemplary embodiment, it is possible to generate the first rule such as "output alarm when driving speed is equal to or greater than 80 Km/h" and the second rule such as "increase audio volume of the car or the user device 100 when the driving speed is equal to or greater than 60 Km/h." At this time, as described above, the rule may be defined by speech input through the microphone 143 or text input through the input unit 120 or touchscreen 130. The speech and text input may be made with a natural language. The situation to be detected (e.g., condition), may be "when change in environment (e.g., driving speed) is equal to or greater than a predetermined threshold," and the action to be taken in fulfillment of the condition may be "output alarm or control audio volume."”
Here, the Examiner notes that the processing of the natural language expression is associated with the processing of the user speech or text interaction with the device to define a rule as described above in Bae et al. Also, that the generation of the rule including trigger(s) and action(s) present in the natural language expression read on Bae et al.’s situation/condition (i.e., trigger) met to perform an action/fulfillment (i.e., action).) to
output one or more infrastructure- specific rules specified by the natural-language expression (see Figs. 7-13 (e.g., from Fig. 7: “If I get in a taxi, send my location to father and little brother(sister) at every 5 minutes”) and ¶ [0191-0192 and 0212] citations as in limitation above, more specifically: “[0191] FIG. 7 shows an exemplary situation in which a user configures and executed a rule through speech interaction, and the user device 100 recognizes the condition specified in the rule and executes the action triggered when the condition is fulfilled…)
each infrastructure-specific rule including one or more triggers and one or more actions specified by the natural-language expression (see Figs. 7-13 (e.g., from Fig. 7: “If I get in a taxi, send my location to father and little brother(sister) at every 5 minutes”) and ¶ [0191-0192 and 0212] citations as in limitation above.
Here, the Examiner notes, similar to what was discussed in the limitations above, that the generation of the rule including trigger(s) and action(s) present in the natural language expression read on Bae et al.’s situation/condition (i.e., trigger) met to perform an action/fulfillment (i.e., action).)
Johnson et al. and Bae et al. are considered to be analogous to the claimed invention because they are in the same field of endeavor in user input natural language processing. 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 Johnson et al. to incorporate the teachings of Bae et al. of processing inputs comprising, a natural-language expression to output one or more infrastructure- specific rules specified by the natural-language expression, each infrastructure-specific rule including one or more triggers and one or more actions specified by the natural-language expression which provides the benefit of providing method and apparatus capable of improving user convenience and device usability with the implementation of optimal environment (¶ [0017] of Bae et al.).
However, Johnson et al. in combination with Bae et al. do not explicitly teach, but Plouffe et al. does teach:
the first infrastructure-specific rule comprising an infrastructure-specific server name for the server (see ¶ [0061]: “FIG. 1 shows one example system 101 that may be used to execute one or more data center applications. System 101 may include one or more system layers providing layers of abstraction between programming entities. As discussed above, a virtualization layer 104 is provided that isolates applications on a guest operating system (GOS) operating in layers 102 and 103, respectively, from an underlying hardware layer 105. Such applications may be, for example, any application program that may operate in a data center environment. For instance, a database server application, web-based application, e-mail server, file server, or other application that provides resources to other systems (e.g., systems 107A-107C) may be executed on system 101…” and
¶ [0090 and 0097]: “[0090] Management actions may be grouped into a set and executed as single transaction, referred to herein as a "job." For instance, a particular job may include multiple management actions that are performed as part of an overall management function. For example, a virtual server may need to be stopped prior to adding resources (e.g., memory) and then restarted. Therefore, a command for stopping the virtual server may need to be executed, followed by a command which adds the particular resource, and then a start command needs to be executed. As discussed, a transaction history may be maintained (e.g., by the management server or other entity) that permits transactions to be rolled back in certain situations. For example, if, after a job is executed, an error occurs, the job may be rolled back prior to the point when the error occurred. Such a rollback may be initiated, for example, by an administrator using an interface program, by a management system, or by any other program or system. […] [0097] The ability to manage the mapping of storage resources, such as one or more disks and one or more World Wide Node Name (WWNN) identifiers to one or more virtual servers (VSs).” and
¶ [0137]: “Further, there may be a "pool" of identifiers associated with virtual I/O entities such as, for example, virtual network interfaces or virtual storage adapters. These virtual identifiers may include, for example, network addresses such as MAC addresses or storage adapter identifiers such as World Wide Node Name (WWNN) identifiers. As discussed, the management server may maintain a pool of these identifiers, and assign one or more identifiers as virtual servers and other resources are created.”) and
transmitting instructions that cause the server to perform a server management action specified by the first infrastructure-specific rule based on the occurrence of at least one of the one or more triggers (see ¶ [0061, 0090, 0097, and 0137] citations as in limitation above and further
¶ [0170-0183]: “[0170] FIG. 21 shows a diagram of a policy 2100 according to one embodiment of the present invention. Policy 2100 may include one or more rules 2101-2103. These rules may be attached to one or more managed objects, such as virtual mainframes, virtual partitions and virtual servers, in the virtual computing system. Rules may include parameters that define what performance or configuration information trigger actions that change the configuration or operating parameters of the virtual server. [0171] For example, rule 2103 may comprise a number of elements, including operator 2105, parameter 2106, relation 2107, parameter value 2108, and action 2109. Operator 2105 may indicate, for instance, what condition rule 2103 will apply, and may be expressed as a logical operator such as IF, WHILE, etc. Operator 2105, along with other values, may determine whether 2103 is triggered. Parameter 2106 may be a generalized parameter (e.g. CPU utilization) that, when attached to a hardware or virtual element instance (or group of instances) within the virtual computing system, allows that particular instance or group of instances to act as a trigger for a particular rule. In one embodiment, rules may be selectively attached to particular instances within the virtual computing system. [0172] Rule 2103 may also comprise a relation 2107 that relates parameter 2106 to a parameter value 2108. The relation may be, for example, a mathematical relation such as greater than, less than or equal to, greater than or equal to, within, or any other relation (e.g., mathematical, Boolean, etc.). Also, relations may be combined to create hierarchical rule sets. Parameter value 2108 may correspond to a value of parameter 2106, when satisfied by the relation, one or more actions 2109 may be performed. For example, if a parameter 2106 such as the CPU utilization on a particular virtual server needs a relation 2107 of greater than a parameter value 2108 on 85% then an action 2109 of a SCALEUP may be performed on the particular virtual server. Rule 2103 may trigger one or more actions 2109, depending on what functions need to be performed. [0173] Actions 2109 may include, for example, a SCALEUP action which adds resources to a particular virtual server, a SCALEDOWN action that subtracts one or more resources from a virtual server, a REPAIR action which may, for example, replace a particular resource with a spare resource within the virtual computing system. Resources, as discussed above, may be associated with one or more physical nodes. Thus, a SCALEUP action may add resources from a physical node not previously associated with a particular virtual server. In this manner, the capabilities of the virtual server may be expanded. [0174] Also, a SHUTDOWN action may be performed that shuts down a particular virtual server. Other actions may be performed, and those listed above are only by way of example. According to one embodiment, there may be multiple actions performed as a result of a rule trigger, and such actions may be performed in a particular sequence. [0175] Although an example policy 2100 is shown having one or more rules, the invention is not limited to any particular policy. Rather, policies may be implemented using other methods, rule formats, and implementations. [0176] According to one embodiment, an interface is provided for users to create management policies within the management server. In one example, these policies may cause automated actions to objects defined in the virtual computing system based on triggers. For instance, a user, within a user interface associated with the management server, may be permitted to define one or more policies. These policies may be defined by, for example, an object (e.g., a server, a node, etc.), a trigger, and an action as discussed above. [0177] Policies, in one implementation, may include rules that are applied to individual groupings of hardware (referred to hereinafter as virtual partitions), objects within those virtual partitions such as virtual servers and nodes, and nodes in a free pool of the virtual computing system for that virtual partition. Each policy may include one or more objects, triggers, and actions. The following example policies may be applied to one or more objects: [0178] For CPU utilization average>70% for 10 minutes, add another node to the virtual partition [0179] For CPU utilization average <30% for 10 minutes, remove a node from the virtual partition [0180] Although the above examples show performance-triggered policies, it should be appreciated that, according to one embodiment of the invention, different types of triggers may be used to initiate an action. [0181] For example, according to one embodiment, actions may be started based on the following triggers: (Table III) [0182] A trigger generally may include an object's property, event or method result, time or user-defined script. As outlined in Table III above, triggers may be defined for performance issues (e.g., resource utilization) or failures, as measured instantaneously or over some period of time. Other triggers may be time-based. For example, a trigger may cause an action to occur immediately, or at some other time. There may be other types of triggers that relate to other events (e.g., a user defined trigger based on one or more parameters, an event-based trigger such as a "node availability" trigger that is fired when a particular node or nodes become available). Any number and type of triggers may be defined, using any type of interface to the management server. [0183] Based on the triggers as discussed above, actions as shown below in Table IV may be available to be performed on the defined objects: (Table IV)”
Table IV: actions (e.g., start))
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Johnson et al., Bae et al., and Plouffe et al. are considered to be analogous to the claimed invention because they are in the same field of endeavor in processing user input (e.g., natural language) and/or managing infrastructures/servers. 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 Johnson et al. to incorporate the teachings of Plouffe et al. of the first infrastructure-specific rule comprising an infrastructure-specific server name for the server and transmitting instructions that cause the server to perform a server management action specified by the first infrastructure-specific rule based on the occurrence of at least one of the one or more triggers which provides the benefit of easily managing computing resources (¶ [0028] of Plouffe et al.).
As to independent claim 9, Johnson et al. in combination with Bae et al. and Plouffe et al. teach the limitations as in claim 1, above.
Johnson et al. further teaches:
9. A system (see Fig. 1 and ¶ [0005]: “In one example embodiment, a system is described. The system can include a communication interface that is connected to a network, receives transaction information, and enables the automatic transmission of a shared transaction request based on rule trigger. The system can also include a rules platform that provides, via the network, a user interface that enables a user to establish a rule to generate a rule trigger, wherein the rule specifies that a particular transaction is a shared transaction, the portion of the transaction to be shared, and identification information that identifies an account that is responsible for the shared portion of the transaction.”), comprising:
a memory that stores instructions (see ¶ [0059] citation as in claim 1, above.), and
a processor that is coupled to the memory (see ¶ [0056 and 0059] citations as in claim 1, above.) and, when executing the instructions, is configured to:
[to perform the limitations taught by Johnson et al. in combination with Bae et al., and Plouffe et al. as in claim 1, above.]
As to independent claim 20, Johnson et al. in combination with Bae et al. and Plouffe et al. teach the limitations as in claim 1, above.
Johnson et al. further teaches:
20. A method (see Fig. 1 and ¶ [0005]: “…The system can additionally include a streaming data platform that exposes the transaction information to the rules platform such that the rules platform is able to examine the transaction information, determine whether a transaction included in the transaction information is the shared transaction based on the rule, and generate a rule trigger for the shared transaction.”) comprising:
[to perform the limitations taught by Johnson et al. in combination with Bae et al., and Plouffe et al. as in claim 1, above.]
Regarding claims 2, 10, and 21, Johnson et al. in combination with Bae et al. and Plouffe et al. teaches the limitations as in claims 1, 9, and 20, above.
Johnson et al. further teaches:
2, 10, and 21. The one or more non-transitory computer-readable media/system/method of claims 1, 9, and 20,
wherein monitoring the deployed infrastructure (see ¶ [0014-15 and 0031] citation(s) as applied to claims 1, 9, and 20, above.) includes one or more of:
monitoring a current resource utilization of one or more nodes of the deployed infrastructure, monitoring a resource exhaustion of one or more resources of the deployed infrastructure, monitoring a schedule of one or more applications or services executed by one or more nodes of the deployed infrastructure, monitoring a performance of one or more services executed by one or more nodes of the deployed infrastructure, or monitoring a latency of the one or more services executed by one or more nodes of the deployed infrastructure (see ¶ [0015] citation as applied to claims 1, 9, and 20, above, and further: “…The data relating to each calendar entry can include a sender's email address, at least one recipient's email address, a time for when the entry was made or emailed, a time for the event, a location for the event, and a message. The data relating to each instant messaging communication can include a sender, a recipient, a time for the message, and a body of the message. In one example embodiment, each time a user receives a particular type of message, e.g., weather alert email, the user starts a weather application [i.e., associated with current resource utilization; node: weather email/app]. In this example embodiment, the dataset can include the name of the application, a time when the application was started [i.e., associated with the schedule of app/services executed], an amount of time the user was using the application [i.e., associated with the latency of services executed], a task performed by the application [i.e., associated with the performance of the service executed], etc.”).
Claims 3, 11, and 22 is/are rejected under 35 U.S.C. 103 as being unpatentable over Johnson et al. (US 20210117972 A1) further in view of Bae et al. (US 20140082501 A1) and Plouffe et al. (US 20050120160 A1) as applied to claims 1, 9, and 20 above, and further in view of Abbondanzio et al. (US 20180167261 A1).
Regarding claims 3, 11, and 22, Johnson et al. in combination with Bae et al. and Plouffe et al. teaches the limitations as in claims 1, 9, and 20, above.
However, Johnson et al. in combination with Bae et al. and Plouffe et al.do not explicitly teach, but Abbondanzio et al. does teach:
3, 11, and 22. The one or more non-transitory computer-readable media/system/method of claims 1, 9, and 20,
wherein the server management action is specified by the natural-language expression, and the server management action comprises one or more of a shutting down action, a starting action, or a memory auto-scaling action (see ¶ [0037]: “FIG. 4C illustrates a third graphical user interface 120 that is similar to the second graphic user interface 110 of FIG. 4B, except that the current location 122 is “at home”, such that the text description 124 for the recommended action is to “Migrate workloads and shut down server.”)
Johnson et al., Bae et al., Plouffer et al., and Abbondanzio et al. are considered to be analogous to the claimed invention because they are in the same field of endeavor in processing user input (e.g., natural language) and/or managing infrastructures/servers. 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 Johnson et al. and Plouffer to incorporate the teachings of Abbondanzio et al. of wherein the server management action is specified by the natural-language expression, and the server management action comprises one or more of a shutting down action, a starting action, or a memory auto-scaling action which provides the benefit of providing transparent user access to resources such as application programs (¶ [0030] of Abbondanzio et al.).
Claims 4-5, 8, 12-13, 16, 23-24, and 27 is/are rejected under 35 U.S.C. 103 as being unpatentable over Johnson et al. (US 20210117972 A1) further in view of Bae et al. (US 20140082501 A1) and Plouffe et al. (US 20050120160 A1) as applied to claims 1, 9, and 20 above, and further in view of Kadarundalagi et al. (US 20220100772 A1).
Regarding claims 4, 12, and 23, Johnson et al. in combination with Bae et al. and Plouffe et al. teaches the limitations as in claim 1, 9, and 20, above.
However, Johnson et al. in combination with Bae et al. and Plouffe et al. do not explicitly teach, but Kadarundalagi et al. does teach:
4, 12, and 23. The one or more non-transitory computer-readable media/system/method of claims 1, 9, and 20,
wherein the machine learning model classifies each token of one or more tokens of the natural-language expression as a trigger token or an action token (see ¶ [0191-0195]: “[0191] Clause 13. One or more non-transitory computer-readable storage media storing program instructions that, when executed on or across one or more processors, perform: [0192] identifying, using one or more machine learning models, one or more trigger groups in a document comprising a sequence of tokens, wherein an individual one of the trigger groups comprises a plurality of textual references to an occurrence of an event type, and wherein the one or more trigger groups are associated with one or more argument slots representing one or more semantic roles for entities; [0193] identifying, using the one or more machine learning models, one or more entities in the document, wherein an individual one of the entities comprises a textual reference to a real-world object type; [0194] assigning, using the one or more machine learning models, one or more of the entities to one or more of the argument slots; and [0195] generating an output indicating the one or more trigger groups and the one or more of the entities assigned to the one or more of the argument slots.”).
Johnson et al., Bae et al., Plouffe et al. and Kadarundalagi et al. are considered to be analogous to the claimed invention because they are in the same field of endeavor in user processing user input (e.g., natural language) and/or managing infrastructures/servers. 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 Johnson et al. in combination with Bae et al. and Plouffe et al. to incorporate the teachings of Kadarundalagi et al. of wherein the machine learning model classifies each token of one or more tokens of the natural-language expression as a trigger token or an action token which provides the benefit of (1) improving the latency of entity linking from text using automated techniques instead of manual review; (2) improving the scalability of entity linking from text using automated techniques instead of manual review; (3) improving the accuracy of entity linking from text using automated techniques instead of manual review; (4) improving the security of entity linking to private databases using access credentials to access the databases as needed ([0026] of Kadarundalagi et al.).
Regarding claims 5, 13, and 24, Johnson et al. in combination with Bae et al. and Plouffe et al. teaches the limitations as in claim 1, 9, and 20, above.
However, Johnson et al. in combination with Bae et al. and Plouffe et al. does not explicitly teach, but Kadarundalagi et al. does teach:
5, 13, and 24. The one or more non-transitory computer-readable media/system/method of claims 1, 9, and 20. The one or more non-transitory computer-readable media of claim 1,
wherein the machine learning model determines, for each token of the natural-language expression, a confidence score indicating a classification confidence of the token as being one of a trigger token or an action token (see ¶ [0191-0195] citations as in claims 4, 12, and 23, above and further ¶ [0043]: “… Mentions, arguments, and triggers may be assigned scores by the event extraction service 100, and the scores may be reported in the output. The scores may represent estimates of accuracy.”).
Johnson et al. in combination with Bae et al. and Plouffe et al. and Kadarundalagi et al. are considered to be analogous to the claimed invention because they are in the same field of endeavor in processing user input (e.g., natural language) and/or managing infrastructures/servers. 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 Johnson et al. in combination with Bae et al. and Plouffe et al. to incorporate the teachings of Kadarundalagi et al. of wherein the machine learning model determines, for each token of the natural-language expression, a confidence score indicating a classification confidence of the token as being one of a trigger token or an action token which provides the benefit of (1) improving the latency of entity linking from text using automated techniques instead of manual review; (2) improving the scalability of entity linking from text using automated techniques instead of manual review; (3) improving the accuracy of entity linking from text using automated techniques instead of manual review; (4) improving the security of entity linking to private databases using access credentials to access the databases as needed ([0026] of Kadarundalagi et al.).
Regarding claims 8, 16, and 27, Johnson et al. in combination with Bae et al. and Plouffe et al. teaches the limitations as in claim 1, 9, and 20, above.
However, Johnson et al. in combination with Bae et al. and Plouffe et al. does not explicitly teach, but Kadarundalagi et al. does teach:
8, 16, and 27. The one or more non-transitory computer-readable media/system/method of claims 1, 9, and 20. The one or more non-transitory computer-readable media of claim 1,
wherein the machine learning model includes a filter that excludes, from the natural-language expression, one or more tokens of the natural-language expression that are not classified as a trigger token or an action token (see ¶ [0043 and 0191-0195] citations as in claims 4-5, 12-13, and 23-24, above and further ¶ [0025]: “The entity linking service may convert or transform records in private databases into flat strings or vectors with fixed numbers of values or fixed lengths. Records may be converted into contextual representations that are sensitive to a context within the source database, e.g., a context of the converted record. The entity linking service may use the contextual representations to determine a set of candidate records for a particular mention, e.g., by filtering out a large number of contextual representations that represent unlikely matches with the mention.”
¶ [0028]: “…For a particular input document, the event extraction service may produce output that identifies one or more events described in the document along with relevant entities that fill roles for the particular event type while filtering out and not reporting irrelevant entities. For example, for a public health event, the event extraction service may report the event type (e.g., a disease outbreak), the organization that made the announcement, the date of the announcement, the place at which the announcement was made, and so on. Using automated techniques, the event extraction service may simplify the extraction of events from documents for clients while providing both accuracy and scalability.”
and ¶ [0085]: “Context-sensitive entity linking 1140 may include using automated techniques for entity filtering 1160. The entity filtering 1160 may be performed without retrieving data from the database(s) 1110 at runtime. In some embodiments, the entity linking service 1100 may use the contextual representations 1135 to determine a set of candidate records for a particular mention, e.g., by filtering 1160 out a large number of contextual representations that represent unlikely matches with the mention. A candidate record may often include one or more of the same tokens (e.g., words) in the contextual representation of a mention. A particular contextual representation may correspond to one record in one of the private databases 1110. Using the entity filtering 1160, the candidate contextual representations and/or corresponding candidate records may be ranked according to scores or other values indicative of a likely match with an entity mention. In some embodiments, one or more techniques such as BERT encoding and Elasticsearch may be used to generate the ranking of candidates. In some embodiments, the top N candidate records (or their corresponding entity representations) may be selected and ranked by the filtering 1160. For example, the top 32 or 64 records may be ranked. In some embodiments, the value N may vary according to one or more performance optimization goals, e.g., to balance latency with accuracy.”).
Johnson et al. in combination with Bae et al. and Plouffe et al. and Kadarundalagi et al. are considered to be analogous to the claimed invention because they are in the same field of endeavor in processing user input (e.g., natural language) and/or managing infrastructures/servers. 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 Johnson et al. in combination with Bae et al. and Plouffe et al. to incorporate the teachings of Kadarundalagi et al. of wherein the machine learning model includes a filter that excludes, from the natural-language expression, one or more tokens of the natural-language expression that are not classified as a trigger token or an action token which provides the benefit of (1) improving the latency of entity linking from text using automated techniques instead of manual review; (2) improving the scalability of entity linking from text using automated techniques instead of manual review; (3) improving the accuracy of entity linking from text using automated techniques instead of manual review; (4) improving the security of entity linking to private databases using access credentials to access the databases as needed ([0026] of Kadarundalagi et al.).
Claims 6-7, 14-15, and 25-26 is/are rejected under 35 U.S.C. 103 as being unpatentable over Johnson et al. (US 20210117972 A1) further in view of Bae et al. (US 20140082501 A1) and Plouffe et al. (US 20050120160 A1) as applied to claims 1, 9, and 20 above, and further in view of Choi (US 20220310066 A1).
Regarding claims 6, 14, and 25, Johnson et al. in combination with Bae et al. and Plouffe et al. teaches the limitations as in claim 1, 9, and 20, above.
However, Johnson et al. in combination with Bae et al. and Plouffe et al. does not explicitly teach, but Choi does teach:
6, 14, and 25. The one or more non-transitory computer-readable media/system/method of claims 1, 9, and 20. The one or more non-transitory computer-readable media of claim 1,
wherein the machine learning model segments the natural-language expression into one or more trigger portions of the natural-language expression and one or more action portions of the natural-language expression (see ¶ [0116]: “In an embodiment, when recognizing both the trigger voice and the command, the processor 511 may be configured to skip processing of a voice detected after the trigger voice. For example, it is assumed that the media device 502 outputs a voice of “Hi, Bixby, buy one soccer ball” which is a corpus. For example, the processor 511 may perform ASR for voice data so as to identify text of “Hi, Bixby, buy one soccer ball” which is a corpus. The processor 511 may identify that the trigger voice of “Hi, Bixby” is included in a voice 503 acquired through the microphone 514. The processor 511 may determine whether to process a command in the text of “buy one soccer ball” following the trigger voice of “Hi, Bixby”, according to whether the trigger voice is output from the media device 502. For example, the media device 502 may identify that an output voice of “Hi, Bixby, buy one soccer ball” includes the trigger voice, and may inform the electronic device 501 of the detection of the trigger voice through the communication circuit 522. When it is identified that the trigger voice has been output from the media device 502, the processor 511 may skip processing of “buy one soccer ball” following the trigger voice of “Hi, Bixby”. When it is not identified that the trigger voice has been output from the media device 502, the processor 511 may perform processing of “buy one soccer ball” following the trigger voice of “Hi, Bixby”. For example, the processor 511 may request processing of “buy one soccer ball” from the AI server 504, and the AI server 504 may perform a command corresponding to “buy one soccer ball” by performing an e-commerce purchase for a soccer ball. Alternatively, the processor 511 may recognize “buy one soccer ball” and generate a purchase command, and may also transmit the purchase command to the AI server 504 (or an e-commerce-associated external device).”).
Johnson et al. in combination with Bae et al. and Plouffe et al. and Choi are considered to be analogous to the claimed invention because they are in the same field of endeavor in processing user input (e.g., natural language) and/or managing infrastructures/servers. 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 Johnson et al. in combination with Bae et al. and Plouffe et al. to incorporate the teachings of Choi of wherein the machine learning model segments the natural-language expression into one or more trigger portions of the natural-language expression and one or more action portions of the natural-language expression which provides the benefit of performing the processing of the command from the user ([0115-0116] of Choi).
Regarding claims 7, 15, and 26, Johnson et al. in combination with Bae et al. and Plouffe et al. teaches the limitations as in claim 1, 9, and 20, above.
However, Johnson et al. in combination with Bae et al. and Plouffe et al. does not explicitly teach, but Choi does teach:
7, 15, and 26. The one or more non-transitory computer-readable media/system/method of claims 1, 9, and 20. The one or more non-transitory computer-readable media of claim 1,
wherein the machine learning model translates one or more trigger tokens of the natural-language expression into at least one trigger of the one or more triggers of the one or more infrastructure- specific rules (see ¶ [0116] citation as in claims 6, 14, and 25, above. “trigger voice”), and
the machine learning model translates one or more action tokens of the natural- language expression into the one or more actions of the one or more infrastructure- specific rules (see ¶ [0116] citation as in claims 6, 14, and 25, above. “commands”).
Johnson et al. in combination with Bae et al. and Plouffe et al. and Choi are considered to be analogous to the claimed invention because they are in the same field of endeavor in processing user input (e.g., natural language) and/or managing infrastructures/servers. 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 Johnson et al. in combination with Bae et al. and Plouffe et al. to incorporate the teachings of Choi of wherein the machine learning model translates one or more trigger tokens of the natural-language expression into at least one trigger of the one or more triggers of one or more rules and the machine learning model translates one or more action tokens of the natural- language expression into the one or more actions of one or more rules which provides the benefit of performing the processing of the command from the user ([0115-0116] of Choi).
Claims 17 and 28 is/are rejected under 35 U.S.C. 103 as being unpatentable over Johnson et al. (US 20210117972 A1) further in view of Bae et al. (US 20140082501 A1) and Plouffe et al. (US 20050120160 A1) as applied to claims 9, and 20 above, and further in view of Li (US 20170261954 A1).
Regarding claim 17, Johnson et al. in combination with Bae et al. and Plouffe et al. teaches the limitations as in claim 9, above.
However, Johnson et al. in combination with Bae et al. and Plouffe et al. does not explicitly teach, but Li does teach:
17. The system of claim 9,
wherein the machine learning model generates, from the natural-language expression, a first infrastructure- specific rule including one or more triggers and a first action (see ¶ [0047]: “In one or more embodiments, the recommendation system 400 can first define similarity between smart homes in terms of the parameters 407, such as footage, location, energy usage, family size, people's ages, professions, education level, and the like. In this way, no privacy is compromised. The recommendation system 400 can then define the similarity between rules and context in terms of target device and action, rule triggers (time, or dependencies), context type and data (sensors), and the like. For example, a first rule (turning off the light at 9 pm) can be more similar with a second rule (turning off the light at 10 pm), but less similar with a third rule (turning on the light at 8 am). Thus, the override action learned from similar rules, such as the first rule, in similar smart homes can be recommended for the second rule in this home in similar context. The recommendation system 400 enables the user of smart home to have the most desirable recommended override action in the unplanned scenario, even if the most desirable recommendation is the first occurrence in that home.”), and
a second rule including one or more triggers and a second action (see ¶ [0047] citation as in limitation above.: “…a first rule (turning off the light at 9 pm) can be more similar with a second rule (turning off the light at 10 pm),…”).
Johnson et al. in combination with Bae et al. and Plouffe et al. and Li are considered to be analogous to the claimed invention because they are in the same field of endeavor in processing user input (e.g., natural language) and/or managing infrastructures/servers. 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 Johnson et al. in combination with Bae et al. and Plouffe et al. to incorporate the teachings of Li of wherein the machine learning model generates, from the natural-language expression, a first rule including one or more triggers and a first action and a second rule including one or more triggers and a second action which provides the benefit of enhancing the learning process of recommendation engine ([0052] of Li).
Regarding claim 28, Johnson et al. in combination with Bae et al. and Plouffe et al. teaches the limitations as in claim 20, above.
However, Johnson et al. in combination with Bae et al. and Plouffe et al. does not explicitly teach, but Li does teach:
28. The method of claim 20,
wherein the machine learning model generates, from the natural-language expression, the first infrastructure- specific rule including the trigger and a first action (see ¶ [0047] as in claim 17 above.), and
a second rule including the trigger and a second action (see ¶ [0047] citation as in claim 17 above.: “…a first rule (turning off the light at 9 pm) can be more similar with a second rule (turning off the light at 10 pm),…”).
Johnson et al. in combination with Bae et al. and Plouffe et al. and Li are considered to be analogous to the claimed invention because they are in the same field of endeavor in processing user input (e.g., natural language) and/or managing infrastructures/servers. 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 Johnson et al. in combination with Bae et al. and Plouffe et al. to incorporate the teachings of Li of wherein the machine learning model generates, from the natural-language expression, a first rule including one or more triggers and a first action and a second rule including one or more triggers and a second action which provides the benefit of enhancing the learning process of recommendation engine ([0052] of Li).
Claims 18 and 29 is/are rejected under 35 U.S.C. 103 as being unpatentable over Johnson et al. (US 20210117972 A1) further in view of Bae et al. (US 20140082501 A1) and Plouffe et al. (US 20050120160 A1) as applied to claims 9, and 20 above, and further in view of Motwani et al. (US 20200396207 A1).
Regarding claims 18, and 29, Johnson et al. in combination with Bae et al. and Plouffe et al. teaches the limitations as in claim 9, and 20, above.
However, Johnson et al. in combination with Bae et al. and Plouffe et al. does not explicitly teach, but Motwani et al. does teach:
18 and 29. The system/method of claims 9 and 20,
wherein the machine learning model generates, from the natural-language expression, a first infrastructure- specific rule including a first trigger and the one or more actions (see ¶ [0018]: “Therefore, a disclosed solution for firewall auto-learning is presented for zero trust environments, such as cloud environments. The user reviews the learned rules and can either remove them (equal to deny by default), modify them, or accept them. When the list of rules (“rule collection”) is ready, the user assigns a priority and action to the collection and deploys it. In some examples, this includes: based at least on a first trigger event, determining a first set of restricted dependencies for a cloud service firewall to learn for a first application; during a first learning phase, learning a first set of candidate rules corresponding to at least a portion of the first set of restricted dependencies; receiving an indication of verifying, blocking, or tailoring one or more candidate rules within the first set of candidate rules, to generate a first set of verified rules; and operating the firewall with the first set of verified rules for the first application. Some examples include receiving a set of constraints, such as a selection from a set of preset constraints and/or a custom constraint. Some examples include retraining based at least on a second trigger event and/or learning rules for a second application.”), and
a second infrastructure- specific rule including a second trigger and the one or more actions (see ¶ [0018] citation as in limitation above. “…retraining based at least on a second trigger event and/or learning rules for a second application.”).
Johnson et al. in combination with Bae et al. and Plouffe et al. and Motwani et al. are considered to be analogous to the claimed invention because they are in the same field of endeavor in processing user input (e.g., natural language) and/or managing infrastructures/servers. 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 Johnson et al. in combination with Bae et al. and Plouffe et al. to incorporate the teachings of Motwani et al. of wherein the machine learning model generates, from the natural-language expression, a first rule including a first trigger and the one or more actions and a second rule including a second trigger and the one or more actions which provides the benefit of allowing users to develop, run, and manage applications without the complexity of building and maintaining for themselves the entirety of the infrastructure typically associated with developing and launching an application ([0016] of Motwani et al.).
Claims 19 and 30 is/are rejected under 35 U.S.C. 103 as being unpatentable over Johnson et al. (US 20210117972 A1) further in view of Bae et al. (US 20140082501 A1) and Plouffe et al. (US 20050120160 A1) as applied to claims 9, and 20 above, and further in view of Ferrucci et al. (US 20230236857 A1).
Regarding claims 19, and 30, Johnson et al. in combination with Bae et al. and Plouffe et al. teaches the limitations as in claim 9, and 20, above.
However, Johnson et al. in combination with Bae et al. and Plouffe et al. does not explicitly teach, but Ferrucci et al. does teach:
19 and 30. The system/method of claims 9 and 20,
wherein the instructions further configure the processor to retrain the machine learning model based on a failure of the machine learning model to generate one or more infrastructure- specific rules for the natural-language expression (see ¶ [0177]: “In some examples, the trained ML model(s) 242 may classify an input query with context as relevant to one of the inference rules and determine an associated confidence score. In various examples, if the trained ML model(s) 242 has low confidence (e.g., a confidence score is at or below a low threshold) in its proof for an explanation to an input query, this low confidence may return no rules found. An extremely high confidence score (e.g., a confidence score is at or exceeds a high threshold) may indicate the rule is proof for an input query. After the inference rule has been applied to an explanation, the data with the inference rules may be labeled as correct or incorrect by a user, and the data may be used as additional training data to retrain the model(s) 242. Thus, the system may retrain the ML model(s) 242 with the additional training data to generate the new ML model(s) 242. The new ML model(s) 242 may be applied to new inference rules as a continuous retraining cycle to improve the rules generator.”), wherein the retraining is based on the natural language expression and a provided one or more rules (see ¶ [0177] citation as in limitation above.: “…After the inference rule has been applied to an explanation, the data with the inference rules may be labeled as correct or incorrect by a user, and the data may be used as additional training data to retrain the model(s) …”).
Johnson et al. in combination with Bae et al. and Plouffe et al. and Ferrucci et al. are considered to be analogous to the claimed invention because they are in the same field of endeavor in processing user input (e.g., natural language) and/or managing infrastructures/servers. 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 Johnson et al. in combination with Bae et al. and Plouffe et al. to incorporate the teachings of Ferrucci et al. of wherein the instructions further configure the processor to retrain the machine learning model based on a failure of the machine learning model to generate one or more rules for the natural-language expression, wherein the retraining is based on the natural language expression and a provided one or more rules which provides the benefit of providing a faster, more efficient, and less costly method to conduct research ([0054] of Ferrucci et al.).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Regarding field-specific training of models associated with server infrastructure and semantic analysis (pertinent to claims 1, 9, and 20):
Zhao et al. (US 12399918 B1: col. 7, line 38 - col. 8. Line 55).
Regarding user providing instructions via user interface for actions associated with infrastructures (e.g., server) (pertinent to claims 3,11, and 22):
Sharma et al. (US 11221907 B1: col. 7, lines 25-47).
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Keisha Y. Castillo-Torres
Examiner
Art Unit 2659
/Keisha Y. Castillo-Torres/Examiner, Art Unit 2659
Prosecution Insights