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 .
Priority
Receipt is acknowledged of certified copies of papers required by 37 CFR 1.55.
Information Disclosure Statement
The information disclosure statements (IDS) submitted on 09/01/2024 and 03/29/2026. The submission is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statements are being considered by the examiner.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1-3 and 8-11 are rejected under 35 U.S.C. 103 as being unpatentable over Kattamanchi (US 20180321796 A1 hereafter Kattamanchi, in view of Guruswamy (US 20100106742 A1) hereafter Guruswamy.
Regarding claim 1, Kattamanchi teaches:
An asset information collection method, comprising: (Claim 18. A method comprising: )
configuring collection parameters, wherein the collection parameters comprise collection time and collection point information. ([0089] To facilitate discovery, proxy servers 312 may be configured with information regarding one or more subnets in managed network 300 that are reachable by way of proxy servers 312. For instance, proxy servers 312 may be given the IP address range 192.168.0/24 as a subnet. Then, customer instance 322 may store this information in CMDB 500 and place tasks in task list 502 for discovery of devices at each of these addresses.[0090] FIG. 5A also depicts devices and services in managed network 300 as configuration items 504, 506, 508, 510, and 512. As noted above, these configuration items represent a set of physical and/or virtual devices (e.g., client devices, server devices, routers, or virtual machines), services executing thereon (e.g., web servers, email servers, databases, or storage arrays), relationships therebetween, as well as higher-level services that involve multiple individual configuration items.; see also [0086-0088])
in a case of arrival of the collection time, generating a collection task according to the collection point information. ([0091] Placing the tasks in task list 502 may trigger or otherwise cause proxy servers 312 to begin discovery. Alternatively, or additionally, discovery may be manually triggered or automatically triggered based on triggering events (e.g., discovery may automatically begin once per day at a particular time). [0092] In general, discovery may proceed in four logical phases: scanning, classification, identification, and exploration. Each phase of discovery involves various types of probe messages being transmitted by proxy servers 312 to one or more devices in managed network 300. The responses to these probes may be received and processed by proxy servers 312, and representations thereof may be transmitted to CMDB 500. Thus, each phase can result in more configuration items being discovered and stored in CMDB 500.)
acquiring collected data from a collection point corresponding to the collection point information according to the collection task, wherein the collected data comprises: asset information and asset association relationship information; and ([0087] In FIG. 5A, CMDB 500 and task list 502 are stored within customer instance 322. Customer instance 322 may transmit discovery commands to proxy servers 312. In response, proxy servers 312 may transmit probes to various devices and services in managed network 300. These devices and services may transmit responses to proxy servers 312, and proxy servers 312 may then provide information regarding discovered configuration items to CMDB 500 for storage therein. Configuration items stored in CMDB 500 represent the environment of managed network 300. [0099] Furthermore, CMDB 500 may include entries regarding dependencies and relationships between configuration items. More specifically, an application that is executing on a particular server device, as well as the services that rely on this application, may be represented as such in CMDB 500. see also [0096-0098])
Kattamanchi does not appear to explicitly teach: generating an information processing task; and constructing an asset association relationship map according to the information processing task and the collected data, and storing the constructed asset association relationship map; or updating a stored asset association relationship map according to the information processing task and the collected data.
However, Guruswamy teaches [0048] The discovery system 110 coordinates the DOM analyzing components 230 to discover assets and their roles and functional relationships in the network system 120. Initially, the discovery system 110 has one or a few seed objects representing known assets (also referred to as seed assets). Examples of such seed assets include a local network interface, and hosts associated with a specific range of IP addresses. The seed object(s) can be pre-configured with the discovery system 110 or provided by users. [0049] The discovery system 110 (or the DOM analyzing component 230) executes 310 queries 222 associated with a seed object by transmitting the corresponding protocol messages to the corresponding seed asset. The responding messages from the seed asset are normalized into DOMs and passed on to the DOM analyzing component 230 corresponding to the executed queries 222. The DOM analyzing component 230 contextually analyzes 320 the DOMs to identify hyperlinks to other assets and determine their roles and functional relationships. The DOM analyzing component 230 determines 330 whether the identified assets are already known to the discovery system 110 (e.g., represented by an asset object 220). If an identified asset is already known to the discovery system 110, the DOM analyzing component 230 merges 340 the results (e.g., by storing information about the asset together). Otherwise, the DOM analyzing component 230 creates 350 an asset object 220 and associated queries 222 for the asset based on the relevant contextual information in the DOM. Information about the DOM, the functional relationships, and the identified assets are also stored in a persistent data store (e.g., the data storage component 240. See also [0050-0051][0059-0063]).
Accordingly, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to modify Kattamanchi task based network discovery system to process the collected asset and relationship information according to Guruswamy’s analysis and functional map technique, including such processing as subsequent information processing task in Kattamanchi’s existing task based workflow. One would have been motivated to do so because Guruswamy teaches that the resulting functional map identifies functional relationships between services and host.
Regarding claim 2, Guruswamy teaches:
The asset information collection method of claim 1, wherein the collection parameters further comprise data to be collected; and generating the collection task according to the collection point information comprises: generating the collection task according to the collection point information and the data to be collected. ([0037] As illustrated, each asset object 220 has one or more queries 222. A query 222 represents a configured protocol suite where the customizable parameters of the underlying protocol suite 210 are defined and specified. Because parameters of a protocol suite 210 can take various values, a single protocol suite 210 can be associated with multiple queries 222, each having a same or different configuration. For example, the DNS request protocol suite 210 illustrated above may have numerous queries 222 associated with various IP addresses, such as the following query 222 soliciting information about the uspto.gov domain: [0038] DNS.query (www.uspto.gov) The value of the parameters can be determined on the fly. For example, the value of the IP address argument of the DNS request protocol suite 210 can be extracted from a response message of the queried asset.)
Regarding claim 3, Guruswamy teaches:
The asset information collection method of claim 1, wherein after acquiring the collected data from the collection point corresponding to the collection point information according to the collection task, the method further comprises: preprocessing the collected data to obtain preprocessed collected data, and storing the preprocessed collected data. ([0021]The responding messages from the corresponding seed asset is normalized into Document Object Models (DOMs) and contextually analyzed to identify references to other assets, their roles and functional relationships with the queried asset. The identified assets are represented by objects. Protocol commands supported by the identified assets are represented by methods associated with the corresponding objects and invoked for responses. The responses are then normalized and analyzed for further assets. This process can be recursively applied to newly discovered assets to discover additional assets available in the network system and their roles and functional relationships. This process can also be applied continuously by a discovery system to make multiple periodical passes of the assets to extract real time status and functional relationship information. [0044] The data storage component 240 is configured to store information related to the discovery process of the discovery system 110. Examples of such information include protocol grammars (e.g., supported protocol commands), discovered assets (e.g., information extracted from responses from the assets, DOMs), their roles and functional relationships (e.g., hyperlinks and associated information). The data storage component 240 may be a relational database or any other type of database that stores the data, such as a flat file. See also [0049])
Regarding claim 8, Kattamanchi teaches:
The asset information collection method of claims 1, wherein the asset information comprises at least one of: physical asset information or virtual asset information. ([0090] FIG. 5A also depicts devices and services in managed network 300 as configuration items 504, 506, 508, 510, and 512. As noted above, these configuration items represent a set of physical and/or virtual devices (e.g., client devices, server devices, routers, or virtual machines), services executing thereon (e.g., web servers, email servers, databases, or storage arrays), relationships therebetween, as well as higher-level services that involve multiple individual configuration items.)
Regarding claim 9, the claim recites similar limitation as corresponding claim 1 and is rejected for similar reasons as claim 1 using similar teachings and rationale.
Regarding claim 10, the claim recites similar limitation as corresponding claim 1 and is rejected for similar reasons as claim 1 using similar teachings and rationale.
Regarding claim 11, Guruswamy teaches:
The asset information collection method of claim 3, wherein constructing the asset association relationship map according to the information processing task and the collected data and storing the constructed asset association relationship map comprises: constructing the asset association relationship map according to the information processing task and the preprocessed collected data, and storing the constructed asset association relationship map; updating the stored asset association relationship map according to the information processing task and the collected data comprises: updating the stored asset association relationship map according to the information processing task and the preprocessed collected data. ([0050-0051][0058][0059] The updated asset data can be used to provide various visibilities about the network system. For example, the asset data can be used to construct a real-time functional map of all known assets in the network system 120 with new assets added as they are discovered in real time. The functional map identifies assets by their roles and connects assets through their functional relationships. The functional graph not only identifies related assets but also specifies exactly their roles and how they are related. This functional graph is useful for purposes such as determining impact radius of a certain asset in the network system 120. E.N.: Guruswamy repeatedly crawls assets, stores normalized results and merges changes into persistent data store to maintain current asset and relationship information)
Claims 4 is rejected under 35 U.S.C. 103 as being unpatentable over Kattamanchi (US 20180321796 A1, in view of Guruswamy (US 20100106742 A1) and in further view of Morsi (US 20140074770 A1) hereafter Morsi.
Regarding claim 4, Kattamanchi in view of Guruswamy do not appear to explicitly teach:
The asset information collection method of claim 1, wherein before storing the constructed asset association relationship map, or before updating the stored asset association relationship map according to the information processing task and the collected data, the method further comprises: configuring timed cleaning time and information-processing-data retention time; and after storing the constructed asset association relationship map, or after updating the stored asset association relationship map according to the information processing task and the collected data, the method further comprises: in a case of arrival of the timed cleaning time, deleting a node with update time exceeding the information-processing-data retention time among nodes representing assets in the asset association relationship map, and deleting a corresponding asset association relationship.
However, Morsi teaches: [0043-0044], [0045] In some examples, data decay is computed by a periodic process. The frequency at which the computation is performed is configurable (e.g., configure the period between computations) as well as the difference between the last time the state of an entity, attribute, or relationship was valid (e.g., as defined by the time indicating the period that the entity is valid) and the current time (i.e., the time at which the retention process ran). Since each entity includes a time period identifying the times the entity is valid, data decay may be indicated by the beginning and end of the time period as well as the difference between the time period and the current time. For example, the period at which the data decay is computed may be configured to be one day, while the required length of time since the entity was valid is one week. Thus, each day, the system may check the entity to determine if the entity has been invalid for more than 7 days. If, when the data decay computation is performed, the entity has been invalid for more than 7 days, then the entity may be deleted from the system. See also [0050-0051]
Accordingly, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to apply Morsi’s configurable periodic data retention, decay technique to the asset association relationship map of Kattamanchi and Guruswamy so that asset nodes and their associated relationships that have become stale beyond a configured retention period are periodically removed. One would have been motivated to do so to prevent obsolete asset and relationship information from being indefinitely retained on the map.
Claims 5 and 7 is rejected under 35 U.S.C. 103 as being unpatentable over Kattamanchi (US 20180321796 A1, in view of Guruswamy (US 20100106742 A1) and in further view of Li (US 20180203767 A1) hereafter Li.
Regarding claim 5, Kattamanchi in view of Guruswamy do not appear to explicitly teach
The asset information collection method of claim 1, further comprising: configuring information processing timeout time and a first number of retransmission times; in a case where execution time of constructing the asset association relationship map according to the information processing task and the collected data or updating the stored asset association relationship map according to the information processing task and the collected data exceeds the information processing timeout time and a number of re-execution times is less than the first number of retransmission times, re-executing the operation of constructing the asset association relationship map according to the information processing task and the collected data or updating the stored asset association relationship map according to the information processing task and the collected data; and in a case where the execution time of constructing the asset association relationship map according to the information processing task and the collected data or updating the stored asset association relationship map according to the information processing task and the collected data exceeds the information processing timeout time and the number of re-execution times is greater than or equal to the first number of retransmission times, determining that information processing timeout occurs.
However, Li teaches:[0034] [0035] During interaction with the datax service, a result returned by an interaction API provided by the datax service can be a success, a failure, or a timeout. If the interaction with the datax service fails or times out, it can be determined that the operation of the data synchronization job fails. [0041] In substep S12, a retry interval base is calculated according to a preset interval. In some embodiments, a retry policy may be preset, for example, retrying according to a constant time or on an exponential growth basis, a preset interval, a preset maximum number of retries, or the like. [0055-0057][0058] [0058] If the number-of-retries condition or the state condition is not satisfied, the process proceeds to substep S23. In substep S23, … . See also [0046][0060]
Accordingly, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention, to apply Li’s timeout and retry technique to the information to the asset association relationship map of Kattamanchi and Guruswamy. One would have been motivated to do so to improve processing reliability by re-executing an operation while limiting the number of re-executions to a preset maximum.
Regarding claim 7, Li teaches:
The asset information collection method of claim 1, further comprising: configuring collection timeout time and a second number of retransmission times; in a case where execution time of acquiring the collected data exceeds the collection timeout time and a number of re-execution times is less than the second number of retransmission times, re-executing the operation of acquiring the collected data from the collection point corresponding to the collection point information according to the collection task; and in a case where the execution time of acquiring the collected data exceeds the collection timeout time and the number of re-execution times is greater than and equal to the second number of retransmission times, determining that collection timeout occurs. ([0034] For example, when being executed, a job interacts with a source device 201, a destination device 203, and the datax service. During an operation of data reading from the source device 201, a result returned by a data-reading application programming interface (API) provided by the source device 201 can be a success, a failure, or a timeout. If data reading from the source device 201 fails or times out, it can be determined that the operation of the data synchronization job fails.[0035] During interaction with the datax service, a result returned by an interaction API provided by the datax service can be a success, a failure, or a timeout. If the interaction with the datax service fails or times out, it can be determined that the operation of the data synchronization job fails.[0036] During an operation of data writing into a destination device 203, a result returned by a data-writing API provided by the destination device is usually a success, a failure, or a timeout. When data writing into the destination device fails or times out, it can be determined that the operation of the data synchronization job fails. E.N: Li records each retry, compares it with the allowed maximum and terminates retry once the maximum is exceeded). Refer to claim 5 for the motivation to combine.
Claims 6 is rejected under 35 U.S.C. 103 as being unpatentable over Kattamanchi (US 20180321796 A1, in view of Guruswamy (US 20100106742 A1) and in further view of Esserlieu (US 20190057101 A1) hereafter Esserlieu.
Regarding claim 6, Kattamanchi in view of Guruswamy do not appear to explicitly teach:
The asset information collection method of claim 3, wherein before storing the preprocessed collected data, the method further comprises: configuring timed cleaning time and collected-data retention time; and after storing the preprocessed collected data, the method further comprises: in a case of arrival of the timed cleaning time, deleting preprocessed data with storage time exceeding the collected-data retention time among the stored preprocessed collected data.
However, Esserlieu teaches:[0024-0025] [0026] The deletion job scheduler can then calculate an oldest allowable archive timestamp value. The oldest allowable archive timestamp value defines a point in time (e.g., date and time) where any archive record that has an archive timestamp less than the oldest allowable archive timestamp value will be considered to be expired and ready for deletion. In one embodiment, the oldest allowable archive timestamp value is equal to a difference between a current date and time when the deletion job runs and the archive retention period for that tenant for that object type. See also [0027-0028]
Accordingly, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to apply Esserlieu’s periodic retention based deletion technique to the stored collected data of Kattamanchi and Guruswamy. One would have been motivated to do so to automatically remove stored data that age exceeds a configured retention period, thereby preventing accumulation of expired data and maintaining the store collection data according to a predetermined retention policy.
Claims 12-13 is rejected under 35 U.S.C. 103 as being unpatentable over Kattamanchi (US 20180321796 A1, in view of Guruswamy (US 20100106742 A1) and in further view of Bhandari (US 20190347126 A1) hereafter Bhandari.
Regarding claim 12, Kattamanchi in view of Guruswamy do not appear to explicitly teach:
The asset information collection method of claim 1, wherein after generating the collection task according to the collection point information, the method further comprises: adding the collection task to a task list, wherein each entry of the task list comprises: a task identification, a task type, and at least one of task start time, a task execution state, and state description information.
However, Bhandari teaches: [0028] For example, a task may include pouring concrete or drafting a report. The same task may be repeated at various times throughout a project. A task data record 150a-150n may include one or more characteristics of a task including but not limited to: a task object name 153, a task type 154, a task identifier 155, task time period 156, a task requirement 157, a task dependency 158, other tasks which current task is dependent upon 159, a task workload 160, and a task budget 161. The system 100 may receive a project work breakdown structure plan and/or other data input to populate the task data records 150a-150n. The task data records 150a-150n correspond to different tasks to be executed. [0029] In one or more embodiments, the task object name 153 in the task data record may include a brief description of the task to be executed. For example, “Draft Report” may be used as a task object name 153. The task type 154 in the task data record may include a reference to the type of task to be executed. For example, the task type may be administrative, include research and writing, data input, data analysis, writing code, or a combination thereof. A task identifier 155 may uniquely identify the task. The task identifier 155 may include a unique numerical value, a unique color, and/or a unique pattern in one or more embodiments. The task object name 153 determines the task identifier 155 and the task type 154 in one or more embodiments.[0030] In one or more embodiments, a task time period 156 may include timing associated with the task. The task time period 156 may include a start time, an end time, a duration, or a combination thereof.
Accordingly, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention, to store, for each task, task record information as taught by Bhandari including a task identifier, task type and task start time. One would have been motivated to include such information to identify and distinguish individual task, type of operation represented by each task and maintain timing information associated with execution of the task.
Regarding claim 13, Bhandari teaches:
The asset information collection method of claim 1, wherein after generating the information processing task, the method further comprises: adding the information processing task to a task list, wherein each entry of the task list comprises: a task identification, a task type, and at least one of task start time, a task execution state, and state description information. ([0028] In one or more embodiments, a task includes an activity to be executed by a resource or using a resource. For example, a task may include pouring concrete or drafting a report. The same task may be repeated at various times throughout a project. A task data record 150a-150n may include one or more characteristics of a task including but not limited to: a task object name 153, a task type 154, a task identifier 155, task time period 156, a task requirement 157, a task dependency 158, other tasks which current task is dependent upon 159, a task workload 160, and a task budget 161. The system 100 may receive a project work breakdown structure plan and/or other data input to populate the task data records 150a-150n. The task data records 150a-150n correspond to different tasks to be executed.[0029] In one or more embodiments, the task object name 153 in the task data record may include a brief description of the task to be executed. For example, “Draft Report” may be used as a task object name 153. The task type 154 in the task data record may include a reference to the type of task to be executed. For example, the task type may be administrative, include research and writing, data input, data analysis, writing code, or a combination thereof. A task identifier 155 may uniquely identify the task. The task identifier 155 may include a unique numerical value, a unique color, and/or a unique pattern in one or more embodiments. The task object name 153 determines the task identifier 155 and the task type 154 in one or more embodiments.) Refer to claim 12 for the motivation to combine.
Claims 14-15 are rejected under 35 U.S.C. 103 as being unpatentable over Kattamanchi (US 20180321796 A1, in view of Guruswamy (US 20100106742 A1) and Bhandari (US 20190347126 A1) and further view of Morsi (US 20140074770 A1).
Regarding claim 14, Kattamanchi in view of Guruswamy and Bhandari do not appear to explicitly teach:
The asset information collection method of claim 12, further comprising: configuring timed cleaning time and task-list-entry retention time; and in a case of arrival of the timed cleaning time, deleting an entry from the task list, with a time interval between add time of the entry and current time exceeding the task-list-entry retention time.
Morsi teaches: ([0041] In some examples, in addition to the retention type annotation, a retention period (e.g., configurable value representing the size of time to use, such as a millisecond, hour, day, week, month, quarter, year, etc.) may also be selected for an entity. See also [0045]. E.N: Morsi compares an earlier timestamp with “the current time” and checks whether the elapsed duration exceeds the configured period
Accordingly, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention, to apply Morsi’s configurable retention period and periodic data decay technique to task list entries of the combined Kattamanchi- Guruswamy- Bhandari system. One would have been motivated to periodically remove task entries whose age exceeds a configured retention period in order to prevent obsolete task records from accumulating and to maintain the task list with information relevant to the current or recent task. as taught by Morsi[0036-0037][0043]) .
Regarding claim 15, Morsi teaches:
The asset information collection method of claim 13, further comprising: configuring timed cleaning time and task-list-entry retention time; and in a case of arrival of the timed cleaning time, deleting an entry from the task list, with a time interval between add time of the entry and current time exceeding the task-list-entry retention time. ([0041] In some examples, in addition to the retention type annotation, a retention period (e.g., configurable value representing the size of time to use, such as a millisecond, hour, day, week, month, quarter, year, etc.) may also be selected for an entity. See also [0045]. E.N: Same reasoning as claim 14 apply to an information processing task.)
Same motivation as claim 14
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
Wilson (US 20200076698 A1) – Teaches collecting data from network devices storing the extracted information and using it to create network maps.
Yokota (US 20210264352 A1) – teaches periodic acquisition and updating of network device inventory.
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/C.A.E./Examiner, Art Unit 2199
/LEWIS A BULLOCK JR/Supervisory Patent Examiner, Art Unit 2199
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