DETAILED ACTION
The instant application having Application No. 18/428,231 filed on 01/31/2024 is presented for examination by the examiner.
Claim 1-20 is/are pending in the application.
Claims 1, 8 and 15 is/are independent claims.
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 .
Examiner Notes
Examiner cites particular columns and line numbers in the references as applied to the claims below for the convenience of the applicant. Although the specified citations are representative of the teachings in the art and are applied to the specific limitations within the individual claim, other passages and figures may apply as well. It is respectfully requested that, in preparing responses, the applicant fully consider the references in entirety as potentially teaching all or part of the claimed invention, as well as the context of the passage as taught by the prior art or disclosed by the examiner.
Drawings
The applicant’s drawings submitted are acceptable for examination purposes.
Information Disclosure Statement
As required by M.P.E.P. 609, the applicant’s submissions of the Information Disclosure Statement dated 01/31/2024 is acknowledged by the examiner and the cited references have been considered in the examination of the claims now pending.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1, 3-8, 10-15 and 17-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter.
Claims 1-20, the claims are within at least one of the four categories of patent eligible subject matter as it is directing to a system/method/computer program product claims under Step 1. However, claim 1-20 are/is rejected under 35 USC 101 because the claims are/is directed to an abstract idea without being integrated into a practical application nor being significantly more.
Per claims 1, 8 and 15, the limitations “analyzing data to determine …”, “employing a matching algorithm to identify …”, “determining a task to be performed …”, and “generating a prioritized list of the additional available software and hardware …”, as drafted, recite functions that, under its broadest reasonable interpretation, covers functions that could reasonably be performed in the mind, including with the aid of pen and paper, but for the recitation of generic computer components. That is, the limitation “collecting and analyzing data to determine …”, “employing a matching algorithm to identify …”, “determining a task to be performed …”, and “generating a prioritized list of the additional available software and hardware …” as drafted, are functions that, under its broadest reasonable interpretation, recite the abstract idea of a mental process. The limitations encompass a human mind carrying out the functions through observation, evaluation, judgment and /or opinion, or even with the aid of pen and paper. Thus, these limitations recite and fall within the “Mental Processes” grouping of abstract ideas under Prong 1 Step 2A.
Under Prong 2 Step 2A, this judicial exception is not integrated into a practical application. The claim recites the following additional elements “a memory storing program instructions”, “a processor in communication with the memory”, “the program instructions executable by a processor” and “collecting …” The “a memory storing program instructions”, “a processor in communication with the memory” and “the program instructions executable by a processor” are recited at a high-level of generality such that it amounts no more than mere instructions to apply the exception using a generic computer component, or merely a generic computer or generic computer components to perform the judicial exception. . The claim recites the additional elements "collecting..."; this limitation amounts to data gathering which is considered to be insignificant extra solution activity (MPEP 2106.05(g). Accordingly, the additional elements do not integrate the recited judicial exception into a practical application, and the claim is therefore directed to the judicial exception. See MPEP 2106.05(f).
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements are “a memory storing program instructions”, “a processor in communication with the memory”, “the program instructions executable by a processor”, and “collecting …” the mere use of generic computer to implement the abstract idea, as discussed above, which does not amount to significantly more, thus, not an inventive concept, and the courts have identified gathering data, storing data, and outputting the result is well-understood, routine and conventional activity (Berkheimer v. HP, Inc., 881 F.3d 1360, 1368, 125 USPQ2d 1649, 1654 (Fed. Cir. 2018)), thus, cannot amount to an inventive concept.. Accordingly, the claim does not appear to be patent eligible under 35 USC 101. See MPEP 2106.05(d).
Regarding claims 3, 10 and 16, the limitation “applying machine learning algorithms to analyze” is an additional metal process under prong 1.
Regarding claims 4, 11 and 18, the limitation “identifying, based on the extracted patterns and insights with respect to the contextual environment …” is an additional metal process under prong 1.
Regarding claims 5, 12 and 19, the limitation “utilizing natural language processing techniques to extract and categorize task-related information …”, “identifying patterns and commonalities among the task “, and “utilizing machine learning with respect to historical task data and the categorized task-related information “ are an additional metal process under prong 1.
Regarding claims 6, 13 and 20, the limitation “comparing the collected metadata …” is an additional metal process under prong 1. Under prong 2, the “retrieving information”, and “collecting metadata …” and “receive” limitations are additional elements that recite insignificant extra solution activity which do not amount to a practical application, nor amount to significantly more under step 2B as explained above.
Regarding claims 7 and 14, the limitation “wherein the matching algorithm utilizes keyword matching” is an additional metal process under prong 1.
Allowable Subject Matter
Claims 5-6, 12-13 and 19-20 would be allowable if rewritten to overcome the rejection(s) under 101, set forth in this Office action and to include all of the limitations of the base claim and any intervening claims.
The following prior art made of record and not relied upon is cited to establish the level of skill in the applicant’s art and those arts considered reasonably pertinent to applicant’s disclosure. See MPEP 707.05(c).
Prior arts:
US 2022/0100568 to Seetharaman
Data asset resources are any elements (e.g., hardware, software, or a combination) configured to store data (e.g., databases, object storage, storage drives and storage drive services, block volumes, file systems, big data clusters, data or metadata repositories, data dictionaries, etc. In embodiments as described herein, a harvester application continues to add computing resource objects to a resource collection as those computing resource objects are identified. Dependencies are identified as each computing resource object is added to the resource collection.
US 2021/0200595 to Wolpoff
a system such as described herein is configured to assign one or more tasks that collect information from a targeted computing resource including, but not limited to: addresses, software versions, hardware versions, and so on. Each of these data items can be aggregated to make decisions or determinations, via any suitable statistical matching technique, about a characteristic, configuration, or property of a specified computing resource. More simply, performing electronic reconnaissance can yield raw data that, in turn, can be aggregated to identify a specific “service” (defined below) such as described herein.
US 2014/0278808 to Iyoob
Examples of Resource Group management actions include, but are not limited to, configuring capacity/storage (e.g., increase the amount/quantity of processors, memory, network bandwidth, storage, etc); increasing quantity of a VM; deleting a resource group; moving selected resources between VDCs, environments, or layers; managing VMs parameters (e.g., name, status, capacity, login password and IP address, etc); controlling power state of VMs (e.g., power on, power off, reboot). Examples of VDC management actions include, but are not limited to, adding resource groups (e.g., VMs), adding VMs to a VDC, a adding resources or services to a VM; viewing services configured to a VDC as well as service provider and the service status; provisioning changes made to a VDC; connecting into a VDC using a VPN connection; viewing activity logs for a VDC; and synching to an existing VM.
US 2013/0117454 to Rangarajan
The request may specify one or more different kinds of resources 114 and requested quantities, such as ad space (e.g. impressions), online computing resources, webpages, load balancing parameters, message routing criteria, storage or processing capacity, account access, and so forth. A request may also include or otherwise be associated with a price to be paid for the corresponding resources 114.
US 2011/0083179 to Lawson
The multitenancy resource cloud 110 of the preferred embodiment functions to be the software and hardware resources that operate a networked application. The resource cloud 110 may have any suitable combination of software platforms or hardware resources. The resource cloud 110 may alternatively be any suitable multi-tenant cloud-hosting environment, such as Amazon EC2. In some embodiments, a second party independently may operate the resource cloud 110. An independently operated resource cloud 110 preferably provides interfaces to perform the necessary actions to enable the system (e.g., such as resource allocation and deallocation). The number of resources that are operated is preferably dynamic and can vary depending upon the capacity requirements.
US 2010/0333167 to Luo
For example, a first filter set 321 may be a whitelist for a particular back-end application/service 332 which specifies patterns for user inputs that are approved for accessing the backend application/service 332. The whitelist essentially contains a list of input patterns to implement server-side checking for user input values, which can reduce the false negatives of any blacklists utilized. A second filter set 322 may be associated with a detecting and preventing HyperText Transport Protocol (HTTP) violations by checking if a request violates the HTTP protocol, determining if an argument is too large, etc. The second filter set 322 regulates HTTP usage to prevent application layer attacks.
The prior art of record does not disclose and/or fairly suggest at least claimed limitations recited in such manners in dependent claims, 5-6, 12-13 and 19-20.
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.
The factual inquiries set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 1, 8 and 15 are rejected under 35 U.S.C. 103 as being unpatentable over US 2013/0174168 to Abuelsaad et al. (hereafter “Abuelsaad”) in further view of US 2014/0304023 to Li et al. (hereafter “Li”) and US 2009/0113442 to Deidda et al. (hereafter “Deidda”)
As per claim 1, Abuelsaad discloses a system comprising:
a memory storing program instructions (FIGs. 1 and 4-6: paragraphs 0005, 0029 and 0053); and
a processor in communication with the memory (FIG. 1), the processor being configured to execute the program instructions to perform processes comprising:
collecting (FIGs. 4-6 and 8; paragraphs 0005, 0029, 0053, 0060, 0074 and 0076: “Specifically, among other functions, engine 70 may (among other things): receive a workload request 76 in a computer memory medium (e.g., 40 of FIG. 1) for a customer; identify a set of computing resources 72 (e.g., in a pool 74 of computing resources) available in the network computing environment 86 to process the workload request 76; detect a need to scale the set of computing resources based on a comparison of the set of computing resources to a level of computing resources needed to process the workload request (via engine 70 and rules 78 leveraging historical data or the like from storage device 82)” [Wingdings font/0xE0] receiving a workload information [Wingdings font/0xE0] identifying a set of computing resources available to process the workload) and analyzing data to determine a contextual environment of the system (FIGs. 4-6 and 8; paragraphs 0005, 0029, 0053, 0060, 0074 and 0076: “Specifically, among other functions, engine 70 may (among other things): receive a workload request 76 in a computer memory medium (e.g., 40 of FIG. 1) for a customer; identify a set of computing resources 72 (e.g., in a pool 74 of computing resources) available in the network computing environment 86 to process the workload request 76; detect a need to scale the set of computing resources based on a comparison of the set of computing resources to a level of computing resources needed to process the workload request (via engine 70 and rules 78 leveraging historical data or the like from storage device 82)” [Wingdings font/0xE0] detecting a need to scale the set of computing resources …), wherein the contextual environment of the system includes existing software and hardware resources available to the system (FIGs. 1 and 4-6; paragraphs 0023 and 0029: “a shared pool of configurable computing resources (e.g. networks, network bandwidth, servers, processing, memory, storage, applications, virtual machines, and services)” [Wingdings font/0xE0] software resources (i.e., application, virtual machines or services) and hardware resources (i.e., networks, bandwidth, servers, memory, storage …));
employing a matching algorithm to identify additional available software and hardware resources that complement the existing software and hardware resources available to the system (FIGs. 4-8; paragraph 0060: “identify a set of computing resources 72 (e.g., in a pool 74 of computing resources) available in the network computing environment 86 to process the workload request 76; detect a need to scale the set of computing resources based on a comparison of the set of computing resources to a level of computing resources needed to process the workload request (via engine 70 and rules 78 leveraging historical data or the like from storage device 82); access a resource scaling policy 80 associated with the customer; identify a set of pricing criteria in the resource scaling policy; scale (the set of computing resources 72 to process the workload request 76 based on the need and the set of pricing criteria; provision additional computing resources 88 to the set of computing resources 72 (e.g., add to pool 74) based on the need without exceeding the pricing constraint” [Wingdings font/0xE0] scaling policy (matching algorithm) [Wingdings font/0xE0] scale (the set of computing resources 72 to process the workload request 76 based on the need and the set of pricing criteria (i.e., adding extra computing resources to process the workload based on the need and pricing constraint);
determining a task to be performed on the system (FIGs. 4-6 and 8; paragraphs 0005, 0029, 0053, 0060, 0074 and 0076: “Specifically, among other functions, engine 70 may (among other things): receive a workload request 76 in a computer memory medium (e.g., 40 of FIG. 1) for a customer; identify a set of computing resources 72 (e.g., in a pool 74 of computing resources) available in the network computing environment 86 to process the workload request 76; detect a need to scale the set of computing resources based on a comparison of the set of computing resources to a level of computing resources needed to process the workload request (via engine 70 and rules 78 leveraging historical data or the like from storage device 82)”).
Abuelsaad discloses the computing resources including software and hardware resources (FIGs. 1 and 4-6; paragraphs 0023 and 0029: “a shared pool of configurable computing resources (e.g. networks, network bandwidth, servers, processing, memory, storage, applications, virtual machines, and services)” [Wingdings font/0xE0] software resources (i.e., application, virtual machines or services) and hardware resources (i.e., networks, bandwidth, servers, memory, storage …)), and the additional available computing resources that complement existing the existing computing resources (FIGs. 4-8; paragraph 0060: “identify a set of computing resources 72 (e.g., in a pool 74 of computing resources) available in the network computing environment 86 to process the workload request 76; detect a need to scale the set of computing resources based on a comparison of the set of computing resources to a level of computing resources needed to process the workload request (via engine 70 and rules 78 leveraging historical data or the like from storage device 82); access a resource scaling policy 80 associated with the customer; identify a set of pricing criteria in the resource scaling policy; scale (the set of computing resources 72 to process the workload request 76 based on the need and the set of pricing criteria; provision additional computing resources 88 to the set of computing resources 72 (e.g., add to pool 74) based on the need without exceeding the pricing constraint” [Wingdings font/0xE0] scaling policy (matching algorithm) [Wingdings font/0xE0] scale (the set of computing resources 72 to process the workload request 76 based on the need and the set of pricing criteria (i.e., adding extra computing resources to process the workload based on the need and pricing constraint), however, Abuelsaad does not explicitly disclose generating a prioritized list of the additional available computing resources, the list being ordered based on a degree of relevance with respect to the task to be performed by the system.
Li further discloses generating a prioritized list of the additional available computing resources (paragraphs 0031-0032: “the electronic system 500 simultaneously monitors multiple resource utilizations, e.g., CPU usage and available network bandwidth. In this embodiment, the electronic system 500 sets a priority between those monitored multiple resource utilizations. For example, the electronic system 500 may set "CPU usage" has a higher priority than "available network bandwidth." This priority may be pre-determined by a user. Alternatively, a user may program the electronic system 500, e.g., by using a programming language, to be able to modify the priority when the user wants to modify the priority.”)
It would have been obvious to a person having ordinary skill in the art before the effective filling date of the claimed invention to combine a teaching of Li into Abuelsaad’s teaching because it would provide for the purpose of monitoring that a resource utilization of a user's computer meets a pre-determined standard and also monitors that another resource utilization of that user's computer does not meet another pre-determined standard. When this or similar conflict occurs, the electronic system 500 determines, e.g., based on the priority set by the user, which monitored resource utilization has a higher priority, and then disregards a monitored resource utilization which has a lower priority (Li, paragraph 0032).
Deidda further discloses the list being ordered based on a degree of relevance with respect to the task to be performed by the system (FIGs. 2-6; paragraphs 0031-0033: “he preferred embodiment orders the capable resources of each job j.sub.i based on the job's resource selection policy and the values of the relevant attributes of the capable resources. For example, if RAttr(r.sub.11, j.sub.1)>RAttr(r.sub.12, j.sub.1) and the resource selection policy requires the minimization of the relevant attribute, the ordered resource list for job j.sub.1 will become r.sub.2, r.sub.1. More generally and referring to FIG. 4, the preferred embodiment uses the resource selection policies shown in FIG. 2 to re-order the resources for job j.sub.2 as r.sub.1, r.sub.3 and r.sub.2; the resources for job j.sub.3 as r.sub.2, r.sub.3, r.sub.6 and r.sub.4; and the resources for job j.sub.n as r.sub.5, r.sub.2.”).
It would have been obvious to a person having ordinary skill in the art before the effective filling date of the claimed invention to combine a teaching of Deidda into Abuelsaad’s teaching and Li’s teaching because it would provide for the purpose of recognizing the influence of a given job distribution on the operation of other subsequent jobs. In particular, the preferred embodiment recognizes that the deployment of a job to a given resource may cause that resource to be effectively reserved by the job, thereby preventing other subsequent jobs from being deployed to the resource in question (Deidda, paragraph 0008).
As per claim 8, it is a method claim, which recite(s) the same limitations as those of claim 1. Accordingly, claim 8 is rejected for the same reasons as set forth in the rejection of claim 1.
As per claim 15, it is a program product claim, which recite(s) the same limitations as those of claim 1. Accordingly, claim 15 is rejected for the same reasons as set forth in the rejection of claim 1.
Claims 2, 9 and 16 are rejected under 35 U.S.C. 103 as being unpatentable over Abuelsaad, Li and Deidda, as applied to claims 1, 8, and 15 and further in view of US 2007/0044151 to Whitmore and US 2009/0025023 to Pradeep et al. (hereafter “Pradeep”)
As per claim 2, Abuelsaad does not explicitly disclose wherein collecting and analyzing data about the contextual environment includes: collecting data from an environment of the system using sensors and Application Programming Interfaces (APIs); and pre-processing the collected data to remove noise and irrelevant information.
Whitmore further discloses wherein collecting and analyzing data about the contextual environment includes: collecting data from an environment of the system using sensors (paragraphs 0025: system integrity sensor and SIM sensors) and Application Programming Interfaces (APIs) (paragraph 0025: “system integrity sensors may interface …”)
It would have been obvious to a person having ordinary skill in the art before the effective filling date of the claimed invention to combine a teaching of Whitmore into Abuelsaad’s teaching, Li’s teaching, and Deidda’s teaching because it would provide for the purpose of east one system integrity sensor to gather operational data related to operating conditions and operations within the computing system. The system may also include at least one system integrity effector to apply changes to configuration, operating conditions and operations within the computing system. (Whitmore, paragraph 0004).
Pradeep further discloses pre-processing the collected data to remove noise and irrelevant information (paragraphs 0036).
It would have been obvious to a person having ordinary skill in the art before the effective filling date of the claimed invention to combine a teaching of Pradeep into Abuelsaad’s teaching, Li’s teaching, Deidda’s teaching, and Whitmore’s teaching because it would provide for the purpose of response data is analyzed and enhanced for each subject and further analyzed and enhanced by integrating data across multiple subjects (Pradeep, paragraph 0024).
As per claim 9, it is a method claim, which recite(s) the same limitations as those of claim 2. Accordingly, claim 9 is rejected for the same reasons as set forth in the rejection of claim 2.
As per claim 16, it is a program product claim, which recite(s) the same limitations as those of claim 2. Accordingly, claim 16 is rejected for the same reasons as set forth in the rejection of claim 2.
Claims 3, 10 and 17 are rejected under 35 U.S.C. 103 as being unpatentable over Abuelsaad, Li, Deidda, Whitmore, and Pradeep, as applied to claims 2, 9 and 16, and further in view of US 2021/0110394 to Chen et al. (hereafter “Chen”)
As per claim 3, Abuelsaad does not explicitly disclose wherein collecting and analyzing data about the contextual environment includes: applying machine learning algorithms to analyze the pre-processed data and extract patterns and insights with respect to the contextual environment.
Chen further discloses wherein collecting and analyzing data about the contextual environment includes: applying machine learning algorithms to analyze the pre-processed data and extract patterns (paragraphs 0022, 0077 and 0079: “one or more patterns may be identified using a pattern mining operations such as, for example, extracting sequence patterns.”) and insights with respect to the contextual environment (paragraph 0024: “4) Weigh and evaluate responses that are based only on relevant evidence; 5) Provide situation-specific advice, insights, estimations, determinations, evaluations, calculations, and guidance;”).
It would have been obvious to a person having ordinary skill in the art before the effective filling date of the claimed invention to combine a teaching of Chen into Abuelsaad’s teaching, Li’s teaching, Deidda’s teaching, Whitmore’s teaching, and Pradeep’s teaching because it would provide for the purpose of implementing intelligent automation of opportunity transaction workflows, by a processor, is provided. One or more tasks identified in an existing transaction opportunity workflow suitable for automation may be automated in a current transaction opportunity workflow. The automated tasks may be scheduled and executed in the current transaction opportunity workflow. The automated tasks in the current transaction opportunity workflow may be monitored (Chen, paragraph 0003).
As per claim 10, it is a method claim, which recite(s) the same limitations as those of claim 3. Accordingly, claim 9 is rejected for the same reasons as set forth in the rejection of claim 3.
As per claim 17, it is a program product claim, which recite(s) the same limitations as those of claim 3. Accordingly, claim 17 is rejected for the same reasons as set forth in the rejection of claim 3.
Claims 4, 11, and 18 are rejected under 35 U.S.C. 103 as being unpatentable over Abuelsaad, Li, Deidda, Whitmore, Pradeep, and Chen, as applied to claims 3, 10, and 17 and further in view of US 2013/0006648 to O’Sullivan et al. (hereafter “O’Sullivan”)
As per claim 4, Abuelaad does not explicitly disclose wherein collecting and analyzing data about the contextual environment includes: identifying, based on the extracted patterns and insights with respect to the contextual environment, the existing software and hardware resources available to the system.
O’Sullivan further discloses wherein collecting and analyzing data about the contextual environment includes: identifying, based on the extracted patterns and insights with respect to the contextual environment, the existing software and hardware resources available to the system (paragraphs 0050, 0146 and 0161).
It would have been obvious to a person having ordinary skill in the art before the effective filling date of the claimed invention to combine a teaching of O’Sullivan into Abuelsaad’s teaching, Li’s teaching, Deidda’s teaching, Whitmore’s teaching, Pradeep’s teaching, and Chen’s teaching because it would provide for the purpose of monitoring a total amount of computing resources of a computing resource cohort that are subject to entitlements specified in CEECs of CEEC cohorts associated with the computing resource cohort to determine if and how much of the computing resources are available for reservation by other CEECs of the same or different CEEC cohorts (O’Sullivan, paragraph 0161).
As per claim 11, it is a method claim, which recite(s) the same limitations as those of claim 4. Accordingly, claim 11 is rejected for the same reasons as set forth in the rejection of claim 4.
As per claim 18, it is a program product claim, which recite(s) the same limitations as those of claim 4. Accordingly, claim 18 is rejected for the same reasons as set forth in the rejection of claim 4.
Claims 7 and 14 are rejected under 35 U.S.C. 103 as being unpatentable over Abuelsaad, Li, and Deidda, as applied to claims 1 and 8, and further in view of US 2008/0317037 to Vogl et al. (hereafter “Vogl”) and US 2011/0252366 to Balasubramanian et al. (hereafter “Balasubramanian”)
As per claim 7, Abuelsaad discloses Abuelsaad discloses the computing resources including software and hardware resources (FIGs. 1 and 4-6; paragraphs 0023 and 0029: “a shared pool of configurable computing resources (e.g. networks, network bandwidth, servers, processing, memory, storage, applications, virtual machines, and services)” [Wingdings font/0xE0] software resources (i.e., application, virtual machines or services) and hardware resources (i.e., networks, bandwidth, servers, memory, storage …)), however, Abuelsaad does not explicitly disclose wherein the matching algorithm utilizes keyword matching, data retrieval algorithms, and connectivity analysis to identify compatible resources.
Vogl further discloses wherein the matching algorithm utilizes data retrieval algorithms (paragraph 0166: delivering criteria record 914 .. can be satisfied … (data retrieval alg), and connectivity analysis to identify compatible resources (paragraphs 0081, 0179, 0186 and 0198).
It would have been obvious to a person having ordinary skill in the art before the effective filling date of the claimed invention to combine a teaching of Vogl into Abuelsaad’s teaching, Li’s teaching, and Deidda’s teaching because it would provide for the purpose of a network use criteria record 550 is chosen from the network use criteria table 500, and the defined network usage 510 is compared against the aggregate amount of network usage 515 to determine a network resource availability (Vegi, paragraph 0081).
Balasubramanian further discloses wherein the matching algorithm utilizes keyword matching to identify compatible resources (paragraph 0061: “Accordingly, `IBM` is a keyword used to describe the context of the presentation. The user may provide the word `IBM` as a keyword as preliminary user input by opening a world wide web browser session referencing the URL http://ibm.com. See, for example, the operation of resource A 411 and resource A 511 of FIGS. 4 and 5, respectively. Using this URL, the domain name provides metadata in the second level domain. The activity tracker can use the metadata as a screening criteria later at step 615. In other words, the term `IBM` may be used to determine if a new resource satisfies or fails to satisfy the context.”).
It would have been obvious to a person having ordinary skill in the art before the effective filling date of the claimed invention to combine a teaching of Balasubramanian into Abuelsaad’s teaching, Li’s teaching, Deidda’s teaching, and Vogl’s teaching because it would provide for the purpose of selecting a resource to share in a screen sharing session (Balasubramanian, paragraph 0005).
As per claim 14, it is a method claim, which recite(s) the same limitations as those of claim 7. Accordingly, claim 14 is rejected for the same reasons as set forth in the rejection of claim 7.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Tuan Dao whose telephone number is (571) 270 3387. The examiner can normally be reached on Monday to Friday from 09am to 05pm. The examiner can also be reached on alternate Fridays.
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/TUAN C DAO/ Primary Examiner, Art Unit 2198
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