DETAILED ACTION
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Status of Claims
This Final Office Action is in reply to the communications filed on 12 January 2026.
Claims 1-20 are currently pending and have been examined.
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 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more, and therefore directed to non-statutory subject matter.
Under Step 1, the claims 1-14 recite a system (i.e. an apparatus) and claims 15-20 recite a method (i.e. a process). Thus the claims fall within one of the four statutory categories. See MPEP 2106.03.
Under Step 2A Prong 1, the claims are analyzed to determine whether the claims recite any judicial exceptions including certain groupings of abstract ideas (i.e., mathematical concepts, certain methods of organizing human activity such as a fundamental economic practice, or mental processes).
Claim 1 recites, receive as input information about an organization seeking to engage a managed service provider to manage third-party software for the organization as input and provide as an output a label corresponding to a managed service provider of the set of managed service providers to manage the third-party software for the organization as an output.
Claim 9 recites, receive, as input, information about an organization seeking to engage a managed service provider to manage third-party software for the organization; and select, as an output a label corresponding to, a managed service provider of the set of managed service providers to manage the third-party software for the organization
Claim 15 recites, a request comprising third structured data descriptive of an organization seeking to engage a managed service provider for management of third-party software for the organization; in response to the request, generating a response to the request, the response comprising a selected managed service provider of the set of managed service providers for managing the third-party software of the organization
Under their broadest reasonable interpretation, the limitations can be considered as belonging to methods of organizing human activity abstract idea category since they are directed to steps for providing a managed service provider from a set of managed service providers to an organization seeking to engage a managed service provider to manage third-party software for the organization. This is a method of managing business activities. Thus the claim recites an abstract idea. See MPEP 2106.02 (a)(2) subsection II, C.
Dependent Claims 4, 5, 12, 13, 18 and 19 further reiterate the abstract idea as identified in claim 1, 9 and 15 with further embellishments about the information/ structured data about each of the set of customers and the information about the organization. The additional limitations of the claims 4, 5, 12, 13, 18 and 19 are directed to an abstract idea.
Under Step 2A Prong 2 the claims are analyzed to determine whether the claims recite additional elements that integrate the judicial exception into a practical application.
With respect to claim 1-20, the judicial exception is not integrated into a practical application. In particular claims 1 and 9 recite additional elements, a server system, comprising: a memory resource; and a processing resource operably intercoupled with the memory resource and configured to instantiate an instance of software to perform the limitations. These computing components are recited at a high level of generality and used as tools to perform the abstract idea identified above, such that it amounts to no more than mere instructions to apply the exception using a generic computer. See MPEP 2106.05(f).
The limitations collect and store software licensing management information, the software licensing management information comprising: information about each of a set of managed service providers; and information about each of a set of customers of each of the set of managed service providers, the information about each customer of the set of customers comprising a set of third-party software licensed by and deployed by the customer are mere data gathering and storage recited at a high level of generality, and thus are insignificant extra-solution activity. See MPEP 2106.05(g). In addition, all uses of the recited judicial exceptions require such data gathering and, as such, these limitations do not impose any meaningful limits on the claims.
Claim 1 recites the additional limitations, generate training data for a machine learning model from the software licensing management information, the training data configured to cause a machine learning model trained using the training data. Claim 9 recites, “the selected managed service provider being generated as an output from a machine learning model provided the information about the organization as input, the machine learning model being trained at least in part using training data generated from the software licensing management information.” The “training data” is used to generally apply the abstract idea without placing any limits on how the trained data functions to train the machine learning model. Rather, these limitations only recite the outcome of the “training data” and do not include any details about how the “machine learning model being trained” is accomplished.
Claim 15 recites, a frontend instance of software instantiated at a client device, at a backend instance of software instantiated by cooperation of processing resources and memory resources. These computing components are recited at a high level of generality and used as tools to receive information, such that they amount to no more than mere instructions to apply the exception using a generic computer. See MPEP 2106.05(f).
The limitations collect and store software licensing management information, the software licensing management information comprising: first structured data descriptive of each of a set of customers of each of the set of managed service providers, second structured data descriptive of each of a set of customers of each of the set of managed service providers, the second structured data comprising a set of third party software licensed by and deployed by the customer; transmitting the response to the frontend instance to cause a graphical user interface of the frontend instance to display information about the selected managed service provide are mere data gathering, storage and output recited at a high level of generality, and thus are insignificant extra-solution activity. See MPEP 2106.05(g). In addition, all uses of the recited judicial exceptions require such data gathering and, as such, these limitations do not impose any meaningful limits on the claim.
Claim 15 recites, the selected managed service provider being generated as an output from a machine learning model provided the third structured data as input, the machine learning model being trained at least in part using training data generated from the first and second structed data. The “training data” is used to generally apply the abstract idea without placing any limits on how the training data functions to train the machine learning model and generate the output. Rather, these limitations only recite the outcome of the “training data” and do not include any details about how the “machine learning model being trained” is accomplished. See MPEP 2106.05(f)
Claims 2, 10, and 16 recite, wherein the software licensing information is collected, at least in part, from one or more third-party application programming interfaces. Claims 3, 11, and 17 recite, wherein collecting and storing the software licensing information comprises: providing an application programming interface endpoint for communication with one or more third-party services; and receiving at least a portion of the software licensing management information from the one or more third-party services via the application programming interface endpoint. The “application programming interface” is recited at a high level generality and used as a tool to receive/transmit information such that it amounts to no more than mere instructions to apply the exception using a generic computer. See MPEP 2106.05(f).
Claims 6 recites, wherein the instance of software is further configured to train the machine learning model using the generated training data. Claim 7 recites, wherein training the machine learning model is unsupervised. Claim 8 recites, wherein the generated training data for the machine learning model is unlabeled. These limitations provide nothing more than invoking the instance of software as a tool to train the machine learning model using the generated training data. such that it amounts to no more than mere instructions to apply the exception using a generic computer. See MPEP 2106.05(f). Also, these limitations do not include any details about how the “machine learning model” is trained beyond generic, functional language.
Claim 14 recites, wherein: the instance of software is a backend application instance configured to communicably couple to a frontend application instance instantiated by a client device communicably coupled to the server system; the information about the organization is received from the frontend application instance; and the selected MSP is provided to the frontend application instance to cause the frontend application instance to display, in a graphical user interface thereof, information about the managed service provider of the set of managed service providers to manage the third-party software of the organization. These computing components are recited at a high level of generality and used as tools to receive, transmit and display information, such that they amount to no more than mere instructions to apply the exception using a generic computer. See MPEP 2106.05(f).
Claim 20 recites, further comprising causing the graphical user interface of the frontend instance to display one or more graphical user elements for collecting the information about the organization and for initiating communication of the information about the organization to the backend instance. These computing components are recited at a high level of generality and used as tools to receive, transmit and display information, such that they amount to no more than mere instructions to apply the exception using a generic computer. See MPEP 2106.05(f).
Even when viewed in combination, these additional elements fail to integrate the recited abstract idea into any practical application since they do not impose any non-generic meaningful limits on practicing the abstract idea. Thus, the claimed invention is directed to an abstract idea.
Under Step 2B the claims are analyzed to determine whether the claims recite additional elements that amount to an inventive concept (aka “significantly more”) than the recited judicial exception.
Claims 1-20 as a whole do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed with respect to Step 2A Prong Two, the additional elements in the claims amount to no more than mere instructions to apply the exception using generic computer components. The same analysis applies here in 2B and does not provide an inventive concept.
For the collecting, storing, display information steps (Claims, 1, 9 and 15)) that was considered extra-solution activity in Step 2A, Prong Two, this has been re-evaluated in Step 2B and determined to be well understood, routine, and conventional in the field. The Symantec, TLI, and OIP Techs. court decisions indicate that mere collection, transmission and storage of data over a network is a well‐understood, routine, and conventional function when it is claimed in a merely generic manner (as it is here). For these reasons, there is no inventive concept. See MPEP 2106.05(d), subsection II.
Considered as an ordered combination, the additional elements of the claim do not add anything further than when they are considered separately. Thus, under Step 2B, the claims are ineligible as the claims do not recite additional elements which result in significantly more than the abstract idea itself.
Claim Rejections - 35 USC § 103
Note: 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.
Claim(s) 1, 4-6, 9, 12-15 and 18-20 are rejected under 35 U.S.C. 103 as being unpatentable over Doganata et al (US 2015/0348065 A1) in view of Kwatra et al (US 20230123399 A1) in view of Dawson et al (US 2012/0059917 A1).
Claims 1 and 9: Doganata discloses a server system, comprising (see [0084] As shown in FIG. 10, computer system/server 1002 in cloud computing node):
a memory resource (see [0087]: system memory); and
a processing resource operably intercoupled with the memory resource and configured to instantiate an instance of software configured to (see [0087-0088]: Computer system/server 1002 may further include other removable/non-removable, volatile/non-volatile computer system storage media. Program modules that are configured to carry out the functions of various embodiments of the invention):
collect and store information about each of a set of managed service providers (see [0026]: The SPM 118 calculates and/or records observed (actual) utility values 303 for a plurality of deployed instances, and stores this data in a history log 305. The SPM 118 utilizes these historical utility values as training data 224 to train (and re-train 307) prediction models 326 for each service provider [0063]: Measurements of the service provider include, architecture of a node on which the instance was deployed, notifications of its failure and recovery, and QoS parameters (e.g., runtime performance measurements such as throughput of various resources, delays, etc.).
generate training data for a machine learning model from the software licensing management information, the training data configured to cause a machine learning model trained using the training data to: receive as input, information about an organization seeking to engage a managed service provider to manage third-party software for the organization as input and provide as output a label corresponding to a managed service provider of the set of managed service providers to manage the third-party software for the organization as an output (see [0026]: The SPM 118 utilizes these historical utility values as training data 224 to train (and re-train 307) prediction models 326 for each service provider, [0065]: The SPM receives a user's service request. As discussed above, this request comprises a plurality of requirements that are to be satisfied by one or more service providers 108, 110, 112, 114 in the computing environment 104. The SPM 118, at step 806, applies one or more prediction models 226 to the requirements in the user's request for one or more service providers 108, 110, 112, 114 in the environment 104. The SPM 118, at step 808, predicts a satisfaction level (i.e., utility function) of the one or more service providers 108, 110, 112, 114 with respect to the user's request based on the prediction models 226. The SPM 118, at step 810, then selects at least one of the service providers 108, 110, 112, 114 for deploying an instance of the user's request based on the satisfaction level predicted for the service providers).
Doganata does not expressly disclose, information about each of a set of customers of each of the set of managed service providers. However, Kwatra which also discloses a system for selecting a service provider teaches, information about each of a set of customers of each of the set of managed service providers (see [0043]: identifying structured historical data corresponding to each service provider. Structured historical data may include provider quality of service (QoS) data provided by clients or captured by a system, wherein said data may include client attributes, workload attributes, structured client constraints, or other data indicative of the nature of the workload managed by the service provider with respect to the historical client(s).).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to include in the system of selecting a service provider, information about each of a set of customers of each of the set of managed service providers, as taught by Kwatra because it would “identify an optimal set of services corresponding to a client's requirements” (Kwatra, [0037]).
Doganata and Kwatra disclose, Resource provisioning provides dynamic procurement of computing resources and other resources that are utilized to perform tasks within the cloud computing environment. Metering and Pricing provide cost tracking as resources are utilized within the cloud computing environment, and billing or invoicing for consumption of these resources. In one example, these resources may include application software licenses (see Doganata [0094], Kwatra [0033]). Doganata and Kwatra do not expressly disclose the software licensing management information comprising: the information about each customer of the set of customers comprising a set of third-party software licensed by and deployed by the customer but Dawson in the same field of endeavor teaches the software licensing management information comprising: the information about each customer of the set of customers comprising a set of third-party software licensed by and deployed by the customer (see [0064]: in a typical embodiment, service subscription manager 116 manages all subscriptions in which service subscription log 110 logs such information about customer, services subscribed to, SLA, etc., and service subscription repository 112 provides details about current service subscription. Regardless, the identification of any licenses that the customer already has may be useful when determining the provider to choose. For example, the customer may be requesting a database service but unknowingly already has a database license that could be used. [0072] In still another embodiment, the invention provides a computer-implemented method for software license management).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the customer information of Doganata and Kwatra with the information about each customer of the set of customers comprising a set of third-party software licensed by and deployed by the customer as taught by Dawson because it would determine a requester's/customer's needs and match the customer with the most appropriate provider (Dawson, [0019]).
Claims 4, 12 and 18: The combination of Doganata, Kwatra and Dawson discloses the claimed invention as applied to claim 1, 9 and 15 respectively. Kwatra further teaches, wherein the information about each of the set of customers a revenue of the customer; a number of employees of the customer; an age of the customer; an information technology budget of the customer; and one or more types of electronic devices used by the customer (see [0042]: Another example of a constraint of a requirement would be a price range for each service or requirement. [0043]: client attributes, workload attributes, structured client constraints, or other data indicative of the nature of the workload managed by the service provider with respect to the historical client(s)).
Claims 5, 13 and 19: The combination of Doganata, Kwatra and Dawson discloses the claimed invention as applied to claim 1, 9 and 15 respectively. Dawson further teaches, wherein the information about the organization comprises one or more of: a location of the organization; an industry associated with the organization; a revenue of the organization; a number of employees of the organization; an age of the organization; an information technology budget of the organization; one or more types of electronic devices used by the organization; and one or more third-party software products used by the organization (see [0059] (1) Gathering the customer 106A-N's requirements for each application used (e.g., does the user have any license for the application; if the customer 106A-N is bringing in his/her own license, how many concurrent users would be accessing the application; is the license restricted by the number of physical machines).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the organization information of Doganata and Kwatra with the information about the organization comprising one or more of: a location of the organization; an industry associated with the organization; a revenue of the organization; a number of employees of the organization; an age of the organization; an information technology budget of the organization; one or more types of electronic devices used by the organization; and one or more third-party software products used by the organization as taught by Dawson because it would determine a requester's/customer's needs and match the customer with the most appropriate provider (Dawson, [0019]).
Claim 6: The combination of Doganata, Kwatra and Dawson discloses the claimed invention as applied to claim 1, 9 and 15 respectively. Doganata further discloses, wherein the instance of software is further configured to train the machine learning model using the generated training data (see [0026]: he SPM 118 utilizes these historical utility values as training data 224 to train (and re-train 307) prediction models 326. [0047]: the model generator 220 generates corresponding prediction models 526, 529, 531 using one or more machine learning techniques).
Claims 14 and 20: The combination of Doganata, Kwatra and Dawson discloses the claimed invention as applied to claim 1, 9 and 15 respectively. Doganata further discloses, the instance of software is a backend application instance configured to communicably couple to a frontend application instance instantiated by a client device communicably coupled to the server system; the information about the organization is received from the frontend application instance; and the selected MSP is provided to the frontend application instance to cause the frontend application instance to display, in a graphical user interface thereof, information about the managed service provider of the set of managed service providers to manage the third-party software of the organization (see In particular, FIG. 1 shows one or more client/user systems 102 communicatively coupled to one or more computing environments 104 via a public network 106 such as the Internet. The user systems 102 can include, for example, information processing systems such as desktop computers, laptop computers, servers, wireless devices (e.g., mobile phones, tablets, personal digital assistants, etc.), and the like. In some embodiments, the one or more computing environments 104 are cloud-computing environments. However, in other embodiments, these environments 104 are non-cloud computing environments. [0023] The user systems 102 access the computing environment 104 via one or more interfaces (not shown) such as a web browser, application, etc. to utilize computing resources/services 109, 111, 113, 115 provided by one or more service providers. [0025]: A service request 301, for example, is a set of service requirements demanded by the user. These requirements can be (but are not limited to) the desired quality of service attributes for services provisioned to satisfy the request, and the importance of these attributes. Based on these inputs, the service provider selector 322 of the SPM 118 selects one or more service providers 308, 310, 312. The SPM 118 deploys an instance of the service request on the selected service provider(s). In one embodiment, deploying an instance of the service request comprises provisioning a set of computing resources (e.g., services) at the selected service provider(s) that satisfies the requirements of the user service request. [0066]: As discussed above, this deployment comprises provisioning a set of computing resources (e.g., services) for the user at the identified service provider that satisfy the requirements in the request.
Claim 15: Doganata discloses a method for matching an organization to a managed service provider, the method comprising (see [0004]: an information processing system for selecting at least one service provider in a computing environment to satisfy at least one service request is disclosed):
collecting and storing software information comprising first structured data descriptive of each of a set of managed service providers (see [0026]: The SPM 118 calculates and/or records observed (actual) utility values 303 for a plurality of deployed instances, and stores this data in a history log 305. The SPM 118 utilizes these historical utility values as training data 224 to train (and re-train 307) prediction models 326 for each service provider [0063]: Measurements of the service provider include, architecture of a node on which the instance was deployed, notifications of its failure and recovery, and QoS parameters (e.g., runtime performance measurements such as throughput of various resources, delays, etc.).
receiving from a frontend instance of software instantiated at a client device, at a backend instance of software instantiated by cooperation of processing resources and memory resources (see [0023] The user systems 102 access the computing environment 104 via one or more interfaces (not shown) such as a web browser, application, etc. to utilize computing resources/services 109, 111, 113, 115 provided by one or more service providers. [0024] The computing environment 104 further comprises one or more information processing systems 116 comprising a service provider manager (SPM) 118. The SPM 118, in one embodiment, comprises a prediction model generator 220 and a service provider selector 222, as shown in FIG. 2), a request comprising third structured data descriptive of an organization seeking to engage a managed service provider for management of third-party software for the organization; in response to the request, generating a response to the request, the response comprising a selected managed service provider of the set of managed service providers for managing the third-party software of the organization, the selected managed service provider being generated as an output from a machine learning model provided the third structured data as input, the machine learning model being trained at least in part using training data generated from the first and second structured data (see [0065]: The SPM receives a user's service request. As discussed above, this request comprises a plurality of requirements that are to be satisfied by one or more service providers 108, 110, 112, 114 in the computing environment 104. The SPM 118, at step 806, applies one or more prediction models 226 to the requirements in the user's request for one or more service providers 108, 110, 112, 114 in the environment 104. The SPM 118, at step 808, predicts a satisfaction level (i.e., utility function) of the one or more service providers 108, 110, 112, 114 with respect to the user's request based on the prediction models 226. The SPM 118, at step 810, then selects at least one of the service providers 108, 110, 112, 114 for deploying an instance of the user's request based on the satisfaction level predicted for the service providers); and
transmitting the response to the frontend instance to cause a graphical user interface of the frontend instance to display information about the selected managed service provider (see [0066]: As discussed above, this deployment comprises provisioning a set of computing resources (e.g., services) for the user at the identified service provider that satisfy the requirements in the request. [0023] The user systems 102 access the computing environment 104 via one or more interfaces (not shown) such as a web browser, application, etc. to utilize computing resources/services 109, 111, 113, 115 provided by one or more service providers).
Doganata does not expressly disclose, second structured data descriptive of each of a set of customers of each of the set of managed service providers. However, Kwatra which also discloses a system for selecting a service provider teaches, information about each of a set of customers of each of the set of managed service providers (see [0043]: identifying structured historical data corresponding to each service provider. Structured historical data may include provider quality of service (QoS) data provided by clients or captured by a system, wherein said data may include client attributes, workload attributes, structured client constraints, or other data indicative of the nature of the workload managed by the service provider with respect to the historical client(s).); and
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to include in the system of selecting a service provider, information about each of a set of customers of each of the set of managed service providers, as taught by Kwatra because it would “identify an optimal set of services corresponding to a client's requirements” (Kwatra, [0037]).
Doganata and Kwatra disclose, Resource provisioning provides dynamic procurement of computing resources and other resources that are utilized to perform tasks within the cloud computing environment. Metering and Pricing provide cost tracking as resources are utilized within the cloud computing environment, and billing or invoicing for consumption of these resources. In one example, these resources may include application software licenses (see Doganata [0094], Kwatra [0033]). Doganata and Kwatra do not expressly disclose the software licensing management information comprising: the second structured data comprising a set of third-party software licensed by and deployed by the customer but Dawson in the same field of endeavor teaches the software licensing management information comprising: the software licensing management information comprising: the second structured data comprising a set of third-party software licensed by and deployed by the customer (see [0064]: in a typical embodiment, service subscription manager 116 manages all subscriptions in which service subscription log 110 logs such information about customer, services subscribed to, SLA, etc., and service subscription repository 112 provides details about current service subscription. Regardless, the identification of any licenses that the customer already has may be useful when determining the provider to choose. For example, the customer may be requesting a database service but unknowingly already has a database license that could be used. [0072] In still another embodiment, the invention provides a computer-implemented method for software license management).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the customer information of Doganata and Kwatra with the software licensing management information comprising: the second structured data comprising a set of third-party software licensed by and deployed by the customer as taught by Dawson because it would determine a requester's/customer's needs and match the customer with the most appropriate provider (Dawson, [0019]).
Claim(s) 2-3, 10-11 and 16-17 are rejected under 35 U.S.C. 103 as being unpatentable over Doganata/Kwatra/Dawson as applied to claims 1, 9 and 15 above, and further in view of Fernandez Garcia et al (US 2008/0235141 A1, hereinafter “Fernandez”).
Claims 2, 10 and 16: The combination of Doganata/Kwatra/Dawson discloses the claimed invention as applied to claims 1, 9 and 15 above. Doganata, Kwatra and Dawson do not disclose wherein the software licensing information is collected, at least in part, from one or more third-party application programming interfaces but Fernandez teaches wherein the software licensing information is collected, at least in part, from one or more third-party application programming interfaces (see [0021]: Data can be transferred between the organizational management platform and third-party applications (e.g., to and/or from) using various techniques such as application programming interfaces. [0026]: data from any of the third-party software applications, such as data related to user accounts and associated software licenses for the third-party software applications. The one or more computing systems can then match user accounts across the organizational management platform and the third-party software applications to associate user accounts on the organizational management platform with software licenses).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Doganata/Kwatra/Dawson with the system and method of collecting software licensing information from one or more third-party application programming interfaces as taught by Fernandez in order to gain the commonly understood benefits of such adaptation, such as seamless transfer of data. All this would be accomplished with no unpredictable results.
Claims 3, 11 and 17: The combination of Doganata/Kwatra/Dawson/Fernandez discloses the claimed invention as applied to claims 2, 10 and 16 above. Fernandez further teaches, wherein collecting and storing the software licensing information comprises: providing an application programming interface endpoint for communication with one or more third-party services; and receiving at least a portion of the software licensing management information from the one or more third-party services via the application programming interface endpoint (see [0142]: the computing system can operate to communicate (e.g., via an API) with one or more third-party applications. [0021]: Data can be transferred between the organizational management platform and third-party applications (e.g., to and/or from) using various techniques such as application programming interfaces. [0026]: data from any of the third-party software applications, such as data related to user accounts and associated software licenses for the third-party software applications. The one or more computing systems can then match user accounts across the organizational management platform and the third-party software applications to associate user accounts on the organizational management platform with software licenses).
Claim(s) 7-8 are rejected under 35 U.S.C. 103 as being unpatentable over Doganata/Kwatra/Dawson as applied to claims 1, 9 and 15 above, and further in view of Adibi (US 2021/0056599 A1).
Claim 7: The combination of Doganata/Kwatra/Dawson discloses the claimed invention as applied to claim 6 above. Doganata, Kwatra and Dawson do not disclose wherein training the machine learning model is unsupervised but Adibi which also discloses a service provider matching system teaches, wherein training the machine learning model is unsupervised (see [0023], [0033]: the unsupervised matching system trains the service provider match neural network to generate accurate service provider match predictions based on respective training data).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the matching system of Doganata/Kwatra/Dawson with the system wherein training the machine learning model is unsupervised as taught by Adibi to generate accurate service provider match (Adibi, [0033]).
Claim 8: The combination of Doganata/Kwatra/Dawson discloses the claimed invention as applied to claim 1 above. Doganata, Kwatra and Dawson do not disclose wherein the generated training data for the machine learning model is unlabeled but Adibi which also discloses a service provider matching system teaches, the generated training data for the machine learning model is unlabeled (see [0085]: the unsupervised matching system 102 can develop clusters of raw communication data and corresponding service provider match predictions. The unsupervised matching system 102 can then identify one or more characteristics of each data cluster. Then when a new communication is received, the unsupervised matching system 102 can determine the best data cluster with which to associated the new communication based on one or more characteristics of the raw communication data associated with the new communication, and can generate a service provider match prediction based on the service provider match predictions within the identified cluster).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the matching system of Doganata/Kwatra/Dawson with the system wherein the generated training data for the machine learning model is unlabeled as taught by Adibi because it would generate accurate service provider match (Adibi, [0033]).
Response to Arguments
Examiner withdraws the claim objections in view of the Applicant’s remarks.
Applicant's arguments with respect to the 35 USC 101 rejections have been fully considered but they are not persuasive. Applicant contends:
“More specifically, the claim limitations recite specific technical operations to generate particularly formatted structured data that the human mind is incapable of performing, practically or otherwise, even with pen and paper. As emphasized in the August memorandum, "the mental process grouping is not without limits. Examiners are reminded not to expand this grouping in a manner that encompasses claim limitations that cannot practically be performed in the human mind."
The MPEP itself provides a clear example in 2106.04(a)(1) at Example (vii) by stating that a method of training a neural network (i.e., a method of collect and forming data so as to be suitable for training a neural network) does not recite an abstract idea. As another example of eligible claims related to preparing data for machine learning see Ex parte Brush, Appeal 2025- 002376.”
However, the examiner respectfully disagrees. The claim limitations recite “generate training data” for a machine learning model but unlike the example in 2106.04(a)(1), the claims do not recite steps of training the machine learning model. The claim limitations are merely directed to the outcome of generating the trained data and fail to include any details about how the training data is generated. Examiner also points out that in Ex parte Brush, Appeal 2025- 002376, the claims were found to be eligible in Step 2A, Prong 2. The Board found the additional elements to reflect technology improvement of how the machine learning model itself operates by making predictions based on the correlations within the normalized population data; evaluating the accuracy of the prediction by comparing the prediction to historical data; and altering the predictive model based on the accuracy of the prediction. The Applicant’s claim limitations merely utilize the trained machine learning model to determine a managed service provider and therefore does not reflect an technological improvement in machine learning models.
Applicant contends:
“More specifically, the Assignee respectfully submits that the claim clearly recites retrieving data from specific data sources (e.g., license management databases), and structuring that data in a manner suitable for a particular machine learning training operation. The Assignee submits that claim 1 recites a specific manner of leveraging a structured data representation of specifically sourced data to generate training data for a machine learning model.
As emphasized in the August memorandum, "the [patent eligibility] analysis should take into consideration all the claim limitations and how these limitations interact and impact each other" when evaluating whether the claims integrate an exception into a practical application. The August memorandum further emphasizes that "Examiners are cautioned not to oversimplify claim limitations and expand the application of the 'apply it' consideration." The Assignee notes that the claims recite specific technical details for achieving a particular claimed digital presentation outcome; these claims are clearly integrated into a practical application under the meaning defined and clarified by the MPEP, the PEG, and the August memorandum.”
The examiner respectfully disagrees with the Applicant. Applicant’s claims do not recite any specific manner that data is structured for a particular machine learning training operation or even how the data is structured for a particular machine learning training. Selecting a specific type of data or particular data source amounts to mere insignificant extra-solution activity (see MPEP 2106.05(g)) and therefore does not integrate the judicial exception into a practical application.
Applicant argues that:
Notwithstanding the foregoing, even if the claim is found to not be integrated into a practical application under Step 2A, Prong Two, the Assignee submits that the claim includes additional elements that amount to significantly more than any allegedly recited abstract idea or judicial exception. The specific operations recited by claim 1 clearly directs the scope of the claim to the specific and practical technical application providing a data processing pipeline to generate specifically useful data. The Assignee submits that the claim clearly recites an inventive data use and preparation concept that cannot be achieved with conventional systems or manual effort at least for the reason that different individual software licensees and/or different software licensors have zero visibility of similar information in respect of other developers or software consumers.
The examiner disagrees with the Applicant. Even when considered in combination, the claimed additional elements represent mere instructions to implement the abstract idea on a computer and insignificant extra-solution activity, which do not provide an inventive concept.
Applicant's arguments with respect to the 35 USC 103 rejections have been fully considered but they are not persuasive. Applicant contends:
“Respectfully, the Assignee points out that Doganata does not reference in any sense any managed service provider information. Instead, Doganata is focused on providing load balancing by selecting between different infrastructure as a service companies. More specifically, Doganata is focused to improving quality of network-based or web-based services (0019). The Assignee respectfully submits that no person of skill in the art would understand load balancing infrastructure systems as generating similar or related (much less identical) data to a set of managed service provider ("MSP") businesses that each respectively support software installations and management between small business and large scale software licensors, as described in the application.”
However, the examiner disagrees with the Applicant. Dognata discloses at [0003]: In one embodiment, a method with an information processing system for selecting at least one service provider in a computing environment to satisfy at least one service request is disclosed. The method comprises receiving a service request from a user. The service request comprises at least a set of service requirements to be satisfied by at least one service provider. [0057]: The prediction modeling also derives insights about the performance of the service providers for planning purposes. This is particularly important for computing environment managers such as cloud managers. Service provider prediction models can identify the service providers that perform better for particular different application types. Dawson was relied upon to teach the limitations: the software licensing management information comprising: the software licensing management information comprising: the second structured data comprising a set of third-party software licensed by and deployed by the customer (see [0064]: in a typical embodiment, service subscription manager 116 manages all subscriptions in which service subscription log 110 logs such information about customer, services subscribed to, SLA, etc., and service subscription repository 112 provides details about current service subscription. Regardless, the identification of any licenses that the customer already has may be useful when determining the provider to choose. For example, the customer may be requesting a database service but unknowingly already has a database license that could be used. [0072] In still another embodiment, the invention provides a computer-implemented method for software license management).
Examiner maintains that the combination of the references discloses the features of a managed service provider.
Conclusion
THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to MAAME BALLOU whose telephone number is (571)270-1359. The examiner can normally be reached Monday-Friday 9am-5pm.
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MAAME BALLOU
Examiner
Art Unit 3629
/MAAME BALLOU/ Examiner, Art Unit 3629
/NATHAN C UBER/ Supervisory Patent Examiner, Art Unit 3626
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