DETAILED ACTION
This Office Action is in response to claims filed on 04/15/2026.
Claims 1-20 are pending.
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Response to Arguments
Applicant’s arguments, see page 6 of applicant's remarks, filed 04/15/2026, with respect to 35 USC 112(b) rejection have been fully considered and are persuasive. The rejection of 02/25/2026 has been withdrawn.
Applicant’s arguments, see page, filed 04/15/2026, with respect to 35 U.S.C. 101 rejection of claims 1-20 have been fully considered and are persuasive. The rejection of 02/25/2026 has been withdrawn.
Applicant’s arguments with respect to claims 1, 6, 11, and 20 have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1, 5, and 16-19 are rejected under 35 U.S.C. 103 as being unpatentable over Morita et al. Pub. No. US 2019/0138964 A1 (hereinafter Morita) in view of Choudhury et al. Pub. No. US 2018/0103052 A1 (hereinafter Choudhury) in view of Ding et al. Pub. No. US 2015/0309840 A1 (hereinafter Ding).
With regard to claim 1, Morita teaches a system comprising ([0028], The present invention may be a system):
a non-transitory computer readable storage medium ([0028], and/or a computer program product at any possible technical detail of integration);
a processor to retrieve and execute instructions on the storage medium, the instructions to ([0028], The computer program product may include a computer readable storage medium (or media) having computer readable program instructions thereon for causing a processor to carry out aspects of the present invention):
receive user usage data from telemetry agent ([0075], Still referring to FIG. 4, the device monitoring server 122 may comprise one or more servers that collect and store structured monitoring data about each user device 110a-n. For example, the device monitoring severs 122 may comprise a server running SysTrack® software, or similar monitoring software, that collects data related to application usage, application performance, application faults, application latency, resource utilization, …, etc.);
create a knowledge base based on a profile ([0079], According to aspects of the invention, the refresh server 105 is configured to utilize a cognitive computing system 130 (knowledge base) to analyze the unstructured data obtained from the at least one unstructured data source 119. In embodiments, the cognitive computing system 130 comprises one or more servers that are programmed to apply at least one of natural language understanding (NLU), semantic text analysis, and machine learning techniques to analyze the unstructured data to extract meaning from the unstructured data), wherein the knowledge base is implemented as a knowledge graph defining relationships between users, job families, computing devices, and software applications;
segment the knowledge base based on a profile; ([0089], Still referring to FIG. 5, in accordance with aspects of the invention, the refresh server 105 determines the device health score 525 in the manner described herein, e.g., by obtaining unstructured data and unstructured data, classifying the unstructured data, normalizing each component of the structured and the classified unstructured data, and using a scoring function to determine the device health score based on the normalized data. As described herein, the scoring function may take into account user-defined weightings of the data and/or profile inputs that define, for example, a device role for the device and a persona role for a user of the device. For example, the scoring function may include priority data such as a device role parameter and a persona role parameter, and values for these parameters may be defined by the admin for each of the user devices 110a-n)
determine an optimal computing device recommendation based on the profile; ([0017], The present invention generally relates to computer device management and, more particularly, to determining optimal device refresh cycles and device repairs through cognitive analysis of unstructured data and device health scores … In embodiments, a system is configured to determine an optimal time to refresh (e.g., replace) a computer device, which may be ahead or behind the traditional time-based lifecycle replacement date. In this manner, implementations of the invention provide a novel approach to determining a device refresh recommendation)
determine an optimal software application recommendation based on the profile ([0077, [0083], [0084], In embodiments, the business layer 145 is configured such that each user device 110a-c starts with a device health score of 100%, and this device health score diminishes over time with usage, events, etc. Moreover, the business layer 14 is configured to: provide recommendation of when to initiate device refreshes; utilize predictive analytics to device those device most likely to experience failure based on hardware characteristics where data is available; utilize predictive analysis to identify those device most likely to experience issues with software failures where a new device would resolve; avoid disruption to critical business functions (when data is available); identify those devices that have security gaps and issues and should be replaced (e.g., tied to operating systems and inability to upgrade, etc.); and identify those devices that have compliance gaps and issues (e.g., lack of encryption on device due to age, OS, etc.));
transmit the optimal computing device recommendation and the optimal software application recommendation to a provisioning system ([0087], According to aspects of the invention, the refresh server 105 provides data to the admin device 115 in the form of user interfaces, such as those shown in FIGS. 5 and 6, which are described in greater detail herein. The refresh server 105 may be configured to generate a user interface that displays details associated with a single one of the use device 110a-n and its determined device health score, e.g., as depicted in FIG. 5. The refresh server 105 may be configured to generate a user interface that simultaneously displays plural determined device health scores for plural device 110a-n, e.g., as depicted in FIG. 6) configured to allocate a computing device on the transmitted recommendation.
However, Morita does not explicitly teach that the knowledge base is implemented as a knowledge graph defining relationships between users, job families, computing devices, and software applications.
Choudhury teaches wherein the knowledge base is implemented as a knowledge graph defining relationships between users, job families, computing devices, and software applications ([0060],We illustrate the impact of the disclosure in multiple cyber application scenarios as follows: Consider John, an employee working on a Department of Energy project who launches a job in his organizational cloud-based environment … FIG. 6 shows how graph-based models can be effective in determining a profile for John and establish normative behavior. We first develop a behavioral profile for John, which is obtained by aggregating multiple data sources into a single knowledge graph. As FIG. 6 shows each edge between nodes may indicate a different relation, potentially learned from a different data source. FIG. 6 is a knowledge graph capturing an employee’s profile; [0061], Starting with a basic job description, we use the graph structure to infer the type of software or system he is likely to use for daily work. For example, researchers working on machine-learning techniques are more likely to use graphical processing unit based systems. We extend such inference abilities to reason about the network resources he should use.)
It would have been obvious to one of ordinary skill in the art at the time the invention was filed to apply the teachings of Choudhury with the teachings of Morita in order to provide a system that teaches a knowledge base comprising a knowledge graph maintaining enterprise-level relationships associated with users, job families, and associated hardware and software. The motivation for applying Choudhury teaching with Morita teaching is to provide a system that allows for multiple data sources to be aggregated and organized into a single unified model that enables improved decision-making (Choudhury, [0061]). Morita and Choudhury are analogous art directed towards arrangements using knowledge-based models. Therefore, it would have been obvious for one of ordinary skill in the art to combine Choudhury with Morita to teach the claimed invention in order to provide enterprise-level knowledge graphs.
However, Morita and Choudhury do not explicitly teach provisioning system configured to allocate a computing device on the transmitted recommendation
Ding teaches provisioning system configured to allocate a computing device on the transmitted recommendation ([0016], FIG. 1 illustrates one embodiment of an automated capacity provisioning system for a computer system 100. The provisioning system includes a recommendation tool 300, a provisioning tool 160, a data collection tool 170, and a data repository 180; [0018], Provisioning tool 160 receives the provisioning policies 304 from recommendation tool 300 and automatically provisions the computer system 100 accordingly)
It would have been obvious to one of ordinary skill in the art at the time the invention was filed to apply the teachings of Ding with the teachings of Morita and Choudhury in order to provide a system that teaches device provisioning associated with a recommendation. The motivation for applying Ding teaching with Morita and Choudhury teaching is to provide a system that allows for the known method of automated resource allocation such that enables a resource provisioning system to receive and act upon resource recommendation, with reasonable expectation of success. Morita and Choudhury and Ding are analogous art directed towards allocation arrangements. Therefore, it would have been obvious for one of ordinary skill in the art to combine Ding with Morita and Choudhury to teach the claimed invention in order to provide automated resource allocation system associated with resource recommendations.
With regard to claim 5, Morita teaches the system of claim 1 wherein the usage data corresponds to a software usage pattern of a user ([0075], the device monitoring server 122 may comprises one or more servers that collect and store structured monitoring data about each user device 110a-n … that collects data related to application usage).
With regard to claim 16, Morita teaches wherein the processor receives hardware information of a computing device from the telemetry agent ([0098], As shown in FIG. 7, at step 705 the refresh server 106 extracts service desk data. This step may comprise, for example, obtaining unstructured data from the unstructured data source 119 as described with respect to FIG. 4. In embodiments, this step includes extract hardware … related ticket data), the hardware information comprising a processor model, installed memory, graphics adapter, display resolution and type peripherals, and network connection ([0075], the device monitoring server 122 may comprise one or more servers that collect and store structured monitoring data … that collects data related to … average memory usage, average CPU usage, hours powered on, CPU utilization, memory utilization, network bandwidth per application; [0077], hardware issues (e.g., general device issues, battery issues, memory issues, keypad issues) … and non-device issues (e.g., accessory issues, etc.)).
With regard to claim 17, Morita teaches wherein the processor receives software information of the computing device from the telemetry agent ([0098], As shown in FIG. 7, at step 705 the refresh server 106 extracts service desk data. This step may comprise, for example, obtaining unstructured data from the unstructured data source 119 as described with respect to FIG. 4. In embodiments, this step includes extract … software related ticket data), comprising information about the operating system, applications installed, and applications used including frequency and duration ([0075], the device monitoring server 122 may comprise one or more servers that collect and store structured monitoring data … that collects data related to application usage, application performance, application faults … errors logged in in the operating system, power average, time since the specified system’s installation, clock speed, memory type, OS install date; [0077], software issues (e.g., operating system, business applications, communication applications, etc.)).
With regard to claim 18, Morita teaches wherein the knowledge base corresponds to a defined ontology utilized to store complex structured and unstructured information ([0020], In embodiments, the scoring function is based on parameters that correspond to categories of structured data that is obtained from at least one structured data source. In embodiments, the refresh system also obtains unstructured data from at least one unstructured data source, and analyzes the unstructured data using cognitive computing techniques to classify the unstructured data into one or more of the categories of the scoring function.).
With regard to claim 19, Morita teaches wherein the optimal computing device is a statistical mode in a set of computing devices associated with the profile or a mode of number of features consistent with a majority of computing devices in the profile ([0083], The components and/or sub-components may correspond to parameters of the scoring function as described herein. In embodiments, a weighting may be configured for each component, and this weighting is utilized by the scoring function into the calculation of the sub-component and overall device health score. The device health scores for plural user devices 110a-n are then used in conjunction with logical sorting and processing of devices under management to provide a recommendation and prioritized list devices to be refreshed).
Claims 2 and 4 are rejected under 35 U.S.C. 103 as being unpatentable over Morita in view of Choudhury in view of Ding, as applied to claim 1 above, further in view of April et al. Pub. No. US 2011/0015958 A1 (hereinafter April).
With regard to claim 2, Morita does not explicitly teach that a profile corresponds to a job family.
April teaches the system of claim 1 wherein the profile corresponds to a job family ([0057], A forecast of talent requirements given likely business scenarios may be defined, translating business plans into a specific workforce profile or staffing plan – number of positions, types of skills, timing, location, etc. – and identifying those factors that could change the required profile so that contingency plans can be developed; [0078], Attributes associated with each employee may include their level within the organization, which may be defined generically for the entire organization or by defined career paths by job family).
It would have been obvious to one of ordinary skill in the art at the time the invention was filed to apply the teachings of April with the teachings of Morita, Choudhury, and Ding in order to provide a system that teaches a profile corresponding to a job family. The motivation for applying April teaching with Morita, Choudhury, and Ding teaching is to provide a system that allows for role-based policies and business plans to be implemented tailored to a user’s occupation in an organization thereby enabling predictive workforce planning (April, [0078]-[0079]). Morita, Choudhury, Ding and April are analogous art directed towards resource planning. Therefore, it would have been obvious for one of ordinary skill in the art to combine April with Morita, Choudhury, and Ding to teach the claimed invention in order to provide relevant policies by incorporating defined, role-based patterns.
With regard to claim 4, Morita teaches the system of claim 2, the instructions to segment comprise instructions to group … from the knowledge base ([0089], Still referring to FIG. 5, in accordance with aspects of the invention, the refresh server 105 determines the device health score 525 in the manner described herein, e.g., by obtaining structured data and unstructured data, classifying the unstructured data, normalizing each component of the structured and the classified unstructured data, and using a scoring function to determine the device health score based on the normalized data)
Morita teaches the instructions to segment comprising instructions to group data from the knowledge base. However, Morita does not explicitly teach the grouped data from the knowledge base comprise the job family exclusive of user node data.
April teaches the job family … exclusive of a user node ([0059], In some embodiments, a workforce requirement module may define specific job requirements (e.g., knowledge/skills/abilities, education and experience, certifications). The requirements may be taken from existing job descriptions or job postings. FIG. 4 illustrates an example table 400 of workforce requirements for an engineering services company, although this may take a variety of forms in other embodiments; [0060], Column 1 410 includes the different job categories (i.e., job families, job types, roles, etc.) to be included in the workforce planning simulation … The number and type of requirements may depend on each organization, and various combinations may be accommodated).
It would have been obvious to one of ordinary skill in the art at the time the invention was filed to apply the teachings of April with the teachings of Morita, Choudhury, and Ding in order to provide a system that teaches instructions to segment knowledge base exclusive of a user node comprises instructions to group the job family. The motivation for applying April teaching with Morita, Choudhury, and Ding teaching is to provide a system that allows for role-based segmentation of knowledge, enabling the system to organize and retrieve data relevant to a particular job family (April, FIG. 4 and [0059]). Morita, Choudhury, Ding and April are analogous art directed towards resource planning. Therefore, it would have been obvious for one of ordinary skill in the art to combine April with Morita, Choudhury, and Ding to teach the claimed invention in order to provide a system with the capabilities to structure data in accordance to occupational roles enabling the creation of job family specific policies.
Claim 3 is rejected under 35 U.S.C. 103 as being unpatentable over Morita in view of Choudhury in view of Ding in view of April as applied to claim 2 above, and further in view of Cao et al. Pub. No. US 2016/0373377 A1 (hereinafter Cao).
With regard to claim 3, the combination does not explicitly teach the claim.
Cao teaches the system of claim 2 wherein the optimal software application recommendation corresponds to a set of software applications commonly used by the job family ([0052], For instance, activity by the use may be monitored/tracked/obtained across one or more cloud environments. As such, if the user is actively using a specific resource on a first virtual machine of a cluster but not on other virtual machines of the cluster, then aspects of the disclosure could use such asset activity data to make informed decisions (e.g., relative to looking at each virtual machine individually when installing an application). For example, a single developer/administrator (job families) could be monitored/tracked across a group of instances involved in a group of web applications (set of applications) to determine for new virtual machine ‘which applications are needed when.’ (commonly associated with the job families) Accordingly, a group of user may have different types of virtual machines with different software/licenses loaded in different orders (e.g., an accountant as compared with a programmer)).
It would have been obvious to one of ordinary skill in the art at the time the invention was filed to apply the teachings of Cao with the teachings of Morita, Choudhury, Ding, and April in order to provide a system that teaches an optimal software application recommendation corresponds to a set of software application used by the job family. The motivation for applying Cao teaching with Morita, Choudhury, Ding, and April teaching is to provide a system that allows for recommendations that are optimal in both functionality and occupationally relevant providing performance benefits directed to speed, flexibility, responsiveness and resource usage (Cao, [0015]). Morita, Choudhury, Ding, April and Cao are analogous art directed towards resource planning and allocation. Therefore, it would have been obvious for one of ordinary skill in the art to combine Cao with Morita, Choudhury, Ding, and April to teach the claimed invention in order to provide optimal recommendations in accordance to a job family to achieve system efficiency improvements of proper deployments.
Claims 6-7 and 9-10 are rejected under 35 U.S.C. 103 as being unpatentable over Morita et al. Pub. No. US 2019/0138964 A1 (hereinafter Morita) in view of Choudhury et al. Pub. No. US 2018/0103052 A1 (hereinafter Choudhury) in view of Ding et al. Pub. No. US 2015/0309840 A1 (hereinafter Ding) in view of April et al. Pub. No. US 2011/0015958 A1 (hereinafter April).
With regard to claim 6, Morita teaches a method comprising ([0028], The present invention may be …, a method):
receiving user usage data from a telemetry agent ([0075], Still referring to FIG. 4, the device monitoring server 122 may comprise one or more servers that collect and store structured monitoring data about each user device 110a-n. For example, the device monitoring severs 122 may comprise a server running SysTrack® software, or similar monitoring software, that collects data related to application usage, application performance, application faults, application latency, resource utilization, …, etc.);
…
creating a knowledge base based in part of the usage data ([0107], At step 815, the refresh system obtains unstructured data about the at least one computer device. In embodiments, step 815 comprises the refresh server 105 obtaining unstructured data about the user devices 110a-n from the at least one unstructured data source 119, e.g., as described with respect to FIG. 4) …;
correlating the usage data, …, and a user based on the knowledge base ([0109], At step 825, the refresh system normalizes and correlates the data. In embodiments, the refresh serves 105 normalizes each data item obtained at steps 805, 810, and 820, e.g., by assigning a 0-100 score to the data item, e.g., as described with respect to FIG. 4. In embodiments, the refresh server 105 also correlates the data items obtained at steps 805, 810, and 820 into components (or parameters) of the scoring function that is used to determine the device health score of the user devices);
creating a profile based on the correlation ([0110], At step 830, the refresh system determines a device health score for the at least one computer device based on the normalized and correlated data from step 830. In embodiments, the refresh server 105 determines a respective device health score for each one of the user device 110a-n in the manner described with respect to FIG. 4);
determining an optimal computing device recommendation based on the profile ([0017], The present invention generally relates to computer device management and, more particularly, to determining optimal device refresh cycles and device repairs through cognitive analysis of unstructured data and device health scores … In embodiments, a system is configured to determine an optimal time to refresh (e.g., replace) a computer device, which may be ahead or behind the traditional time-based lifecycle replacement date. In this manner, implementations of the invention provide a novel approach to determining a device refresh recommendation);
determining an optimal software application recommendation based on the profile ([0077, [0083], [0084], In embodiments, the business layer 145 is configured such that each user device 110a-c starts with a device health score of 100%, and this device health score diminishes over time with usage, events, etc. Moreover, the business layer 14 is configured to: provide recommendation of when to initiate device refreshes; utilize predictive analytics to device those device most likely to experience failure based on hardware characteristics where data is available; utilize predictive analysis to identify those device most likely to experience issues with software failures where a new device would resolve; avoid disruption to critical business functions (when data is available); identify those devices that have security gaps and issues and should be replaced (e.g., tied to operating systems and inability to upgrade, etc.); and identify those devices that have compliance gaps and issues (e.g., lack of encryption on device due to age, OS, etc.)); and
transmitting the optimal computing device recommendation and the optimal software application recommendation to a provisioning system ([0087], According to aspects of the invention, the refresh server 105 provides data to the admin device 115 in the form of user interfaces, such as those shown in FIGS. 5 and 6, which are described in greater detail herein. The refresh server 105 may be configured to generate a user interface that displays details associated with a single one of the use device 110a-n and its determined device health score, e.g., as depicted in FIG. 5. The refresh server 105 may be configured to generate a user interface that simultaneously displays plural determined device health scores for plural device 110a-n, e.g., as depicted in FIG. 6).
However, Morita does not explicitly teach that the knowledge base is implemented as a knowledge graph defining relationships between users, job families, computing devices, and software applications.
Choudhury teaches wherein the knowledge base is implemented as a knowledge graph defining relationships between users, job families, computing devices, and software applications ([0060],We illustrate the impact of the disclosure in multiple cyber application scenarios as follows: Consider John, an employee working on a Department of Energy project who launches a job in his organizational cloud-based environment … FIG. 6 shows how graph-based models can be effective in determining a profile for John and establish normative behavior. We first develop a behavioral profile for John, which is obtained by aggregating multiple data sources into a single knowledge graph. As FIG. 6 shows each edge between nodes may indicate a different relation, potentially learned from a different data source. FIG. 6 is a knowledge graph capturing an employee’s profile; [0061], Starting with a basic job description, we use the graph structure to infer the type of software or system he is likely to use for daily work. For example, researchers working on machine-learning techniques are more likely to use graphical processing unit based systems. We extend such inference abilities to reason about the network resources he should use.)
Rationale to claim 1 applied here.
However, Morita and Choudhury do not explicitly teach provisioning system configured to allocate a computing device on the transmitted recommendation
Ding teaches provisioning system configured to allocate a computing device on the transmitted recommendation ([0016], FIG. 1 illustrates one embodiment of an automated capacity provisioning system for a computer system 100. The provisioning system includes a recommendation tool 300, a provisioning tool 160, a data collection tool 170, and a data repository 180; [0018], Provisioning tool 160 receives the provisioning policies 304 from recommendation tool 300 and automatically provisions the computer system 100 accordingly)
Rationale to claim 1 applied here.
Morita teaches a method for generating optimal computing device and software application recommendation to a provisioning system. However, Morita does not explicitly teach receiving job family data from a third-party system, creating a knowledge base based in part of using job family data or the correlation of such data.
April teaches receiving a job family from a third-party system ([0047], Workforce planning system 300 may include externalities module 325. Externalities module 325 may be configured to identify externalities such as economic factors that may have an impact on employee decision to remain with a company, practices an organization may adopt, and/or the ability of an organization to recruit new employees merely by way of example)
creating a knowledge base based in part of the … the job family ([0055], Tools and templates may be provided for data collection, external data to support model assumptions (Examiner notes: such model can include a knowledge base) (e.g., correlation between a specific practice and the corresponding retention rates based on demographics), recruiting channel effectives in recruiting employees with specific attributes (job family), guidance in determining relevant inputs to the model, and seasoned judgment in the formulation of components of the model which are more subjective, either by nature or due to the lack of historical data when the model is first developed; [0072], This model may be populated with available published data on common channels (e.g., universities, job sites, etc.), but parameters related to effectiveness and cost will vary by organization, so the model will be enhanced by historical company-specific data);
correlating … the job family ([0069], Some embodiments may determine the impact (correlation) of each practice on an employee’s behavior based on relevant employee attributes (job family) … Historical data, external benchmark data and anecdotal data, and informed judgment as to the expected impact of different practices on employees with specific attributes may be considered)
It would have been obvious to one of ordinary skill in the art at the time the invention was filed to apply the teachings of April with the teachings of Morita, Choudhury, and Ding in order to provide a method that teaches job family data contributing to the creation of a knowledge base and profiles associated. The motivation for applying April teaching with Morita, Choudhury, and Ding teaching is to provide a method that allows for the enabling of simulations and optimization technologies to be used to manage human capital, thereby optimizing for the best allocation of resources to enable the achievement of specific goals (April, [0032]). Morita, Choudhury, Ding and April are analogous art directed towards resource planning. Therefore, it would have been obvious for one of ordinary skill in the art to combine April with Morita, Choudhury, and Ding to teach the claimed invention in order to provide a method that enables predictive planning and optimization of resources based on job families.
With regard to claim 7, April teaches the method of claim 6 wherein the profile corresponds to a job family ([0057], A forecast of talent requirements given likely business scenarios may be defined, translating business plans into a specific workforce profile or staffing plan – number of positions, types of skills, timing, location, etc. – and identifying those factors that could change the required profile so that contingency plans can be developed; [0078], Attributes associated with each employee may include their level within the organization, which may be defined generically for the entire organization or by defined career paths by job family) which is substantially similar to claim 2, and therefore rejected with similar rationale.
Examiner notes: It would be obvious for one of ordinary skill in the art to recognize that claim 7 is being substantially recited again as a method for the system of claim 2.
With regard to claim 9, the method of claim 6, the creating further comprising grouping … from the knowledge base ([0110], At step 830, the refresh system determines a device health score for the at least one computer device based on the normalized and correlated data from step 830. In embodiments, the refresh server 105 determines a respective device health score for each one of the user device 110a-n in the manner described with respect to FIG. 4)
Morita teaches creating comprising grouping data from the knowledge base. However, Morita does not explicitly teach the grouped data from the knowledge base comprise the job family exclusive of user node data.
April teaches the job family … exclusive of a user node ([0059], In some embodiments, a workforce requirement module may define specific job requirements (e.g., knowledge/skills/abilities, education and experience, certifications). The requirements may be taken from existing job descriptions or job postings. FIG. 4 illustrates an example table 400 of workforce requirements for an engineering services company, although this may take a variety of forms in other embodiments; [0060], Column 1 410 includes the different job categories (i.e., job families, job types, roles, etc.) to be included in the workforce planning simulation … The number and type of requirements may depend on each organization, and various combinations may be accommodated) which is substantially similar to claim 4, and therefore rejected with similar rationale.
Examiner notes: It would be obvious for one of ordinary skill in the art to recognize that claim 9 is being substantially recited again as a method for the system of claim 4.
With regard to claim 10, Morita teaches the method of claim 6, wherein the usage data corresponds to a software usage pattern of a user ([0075], the device monitoring server 122 may comprises one or more servers that collect and store structured monitoring data about each user device 110a-n … that collects data related to application usage).
Claim 8 are rejected under 35 U.S.C. 103 as being unpatentable over Morita in view of Choudhury in view of Ding in view of April as applied to claim 6 above, respectively, and further in view of Cao et al. Pub. No. US 2016/0373377 A1 (hereinafter Cao).
With regard to claim 8, Cao teaches the method of claim 6 wherein the optimal software application recommendation corresponds to a set of software applications commonly used by the job family ([0052], For instance, activity by the use may be monitored/tracked/obtained across one or more cloud environments. As such, if the user is actively using a specific resource on a first virtual machine of a cluster but not on other virtual machines of the cluster, then aspects of the disclosure could use such asset activity data to make informed decisions (e.g., relative to looking at each virtual machine individually when installing an application). For example, a single developer/administrator (job families) could be monitored/tracked across a group of instances involved in a group of web applications (set of applications) to determine for new virtual machine ‘which applications are needed when.’ (commonly associated with the job families) Accordingly, a group of user may have different types of virtual machines with different software/licenses loaded in different orders (e.g., an accountant as compared with a programmer)) which is substantially similar to claim 3, and therefore rejected with similar rationale.
Examiner notes: It would be obvious for one of ordinary skill in the art to recognize that claim 8 is being substantially recited again as a method for the system of claim 3.
Claims 11, 13-15 are rejected under 35 U.S.C. 103 as being unpatentable over Morita et al. Pub. No. 2019/0138964 A1 (hereinafter Morita) in view of in view of Korzunov Pub. No. US 2021/0191701 A1 (hereinafter Korzunov) in view of Choudhury et al. Pub. No. US 2018/0103052 A1 (hereinafter Choudhury).
With regard to claim 11, Morita teaches a non-transitory computer readable medium comprising instructions executable by a processor to ([0028], The present invention may be … a computer program product … The computer program product may include a computer readable storage medium (or media) having computer readable program instructions thereon for causing a processor to carry out aspects of the present invention):
receive user software application usage data from telemetry agent ([0075], Still referring to FIG. 4, the device monitoring server 122 may comprise one or more servers that collect and store structured monitoring data about each user device 110a-n. For example, the device monitoring severs 122 may comprise a server running SysTrack® software, or similar monitoring software, that collects data related to application usage, application performance, application faults, application latency, resource utilization, …, etc.);
However, Morita does not explicitly teach extraction of software application profiles corresponding to job profiles, comparing the usage data to the application profiles, and recommending a job profile to the third-party system.
Korzunov teaches receive a first job profile and a second job profile from a third-party system ([0062], In some embodiments, the monitoring system 106 is external to application system 102, Therefore, the deployment engine 108 may request and receive usage information about an application or user from an external source. In some cases, the application system 102 may supplement usage information about an application with externally provided usage information; [0085], A user profile categories one or more users by their usage information. That is, a user profile may be associated with a single user or multiple users where each of the multiple users have the same or similar usage information in at least one dimension or metric);
extract a first software application profile from a knowledge base implemented as a knowledge graph defining relationships between users, job families, computing devices, and software applications, where the first software application profile corresponds to the first job profile ([0064], At step 310 the profile module creates an application profile. An application profile defines different ways of using the same application. The application profiles can be associated with user profiles are used to determine (as described below) one or more application deployments; [0089], In one example embodiment, the deployment module may determine a range of potential user profiles based on application profiles. The deployment engine can then select the user profile in the range of potential user profile that best matches a user; [0090], In this example, the potential user profiles are AP1, AP2, AP3. AP1 has an average CPU usage of 30% over the relevant period and has an average memory usage of 50% over the same period. AP2 has an average CPU usage of 15% over the relevant period and has an average memory usage of 20% over the same period)
extract a second software application profile from the knowledge base, wherein the second application profile corresponds to the second job profile ([0091], If a user were determined to have a user profile with an average CPU usage of 20% and an average memory average CPU usage of 20% and an average memory usage of 20%, then the deployment engine would determine which of the potential user profiles best match this specific usage. In this case, it is likely that AP2 would be the best match as it has similar CPU usage and memory usage characteristics);
compare the user software application usage data to the first software application profile and the second application profile ([0019], In some embodiments, the deployment engine is configured to assign/match a user to a particular application profile based on the user’s individual usage data, That is, the deployment engine receives user usage data or characteristics of user usage are analyzed to produce user usage data, and a determination made by the deployment engine as to which application profile matches the user usage data. The user usage data typically has similar usage patterns to at least one application profile. If not, the deployment engine can use a default application profile; [0069], The deployment engine 108 can then determine 328 an application profile of the software application. This may be based on matching one or more user profiles (or user usage information) to one or more application profiles and determining the best match. For example, each user profile may be associated with an application profile);
determine a percentage likeness of the user software application usage to one of the two software application profiles, based on the comparison (FIG. 3, 326 Determine a user profile based on user usage information, 328 Determine an application profile based on the user profile; [0068], At step 326, the profile module 110 determines a user profile based on the user usage information. A user profile is information about a user that indicates what use cases of an application the user utilizes. This may involve statistical analysis of the usage information to determine which use cases are used frequently or infrequently); and
transmit a job profile recommendation comprising the percentage likeness of the user software application ([0069], The deployment engine 108 can then determine 328 an application profile of the software application. This may be based on matching one or more user profiles (or user usage information) to one or more application profiles and determining the best match) to the third-party system, based on the comparison ([0070], Once the application profile 328 has been determined, the deployment engine 108 can deploy 330 the application for the user. Deployment as a general term refers to the activities that make software available for use by a given user. Given this, different versions of the application might be deployed into production (that is for public use) for different users).
It would have been obvious to one of ordinary skill in the art at the time the invention was filed to apply the teachings of Korzunov with the teachings of Morita in order to provide a computer readable medium that teaches application profiles corresponding to job profiles, comparison to usage data, and recommendations based on the comparison. The motivation for applying Korzunov teaching with Morita teaching is to provide a non-transitory computer readable medium that allows for customization of application deployment associated with a particular profile (Korzunov [0005]), enabling the general benefits of improved resource efficiency and alignment (Korzunov, [0036]). Morita and Korzunov are analogous art directed towards monitoring arrangements of configurations and resource deployment. Therefore, it would have been obvious for one of ordinary skill in the art to combine Korzunov with Morita to teach the claimed invention in order to provide optimal deployments of tailored to users based on their usage patterns and job relevance.
However, Morita and Korzunov do not explicitly teach that the knowledge base is implemented as a knowledge graph defining relationships between users, job families, computing devices, and software applications.
Choudhury teaches wherein the knowledge base is implemented as a knowledge graph defining relationships between users, job families, computing devices, and software applications ([0060],We illustrate the impact of the disclosure in multiple cyber application scenarios as follows: Consider John, an employee working on a Department of Energy project who launches a job in his organizational cloud-based environment … FIG. 6 shows how graph-based models can be effective in determining a profile for John and establish normative behavior. We first develop a behavioral profile for John, which is obtained by aggregating multiple data sources into a single knowledge graph. As FIG. 6 shows each edge between nodes may indicate a different relation, potentially learned from a different data source. FIG. 6 is a knowledge graph capturing an employee’s profile; [0061], Starting with a basic job description, we use the graph structure to infer the type of software or system he is likely to use for daily work. For example, researchers working on machine-learning techniques are more likely to use graphical processing unit based systems. We extend such inference abilities to reason about the network resources he should use.)
It would have been obvious to one of ordinary skill in the art at the time the invention was filed to apply the teachings of Choudhury with the teachings of Morita and Korzunov in order to provide a system that teaches a knowledge base comprising a knowledge graph maintaining enterprise-level relationships associated with users, job families, and associated hardware and software. The motivation for applying Choudhury teaching with Morita and Korzunov teaching is to provide a system that allows for multiple data sources to be aggregated and organized into a single unified model that enables improved decision-making (Choudhury, [0061]). Morita, Korzunov, and Choudhury are analogous art directed towards arrangements using knowledge-based models. Therefore, it would have been obvious for one of ordinary skill in the art to combine Choudhury with Morita and Korzunov to teach the claimed invention in order to provide enterprise-level knowledge graphs.
With regard to claim 13, Morita teaches the computer readable medium of claim 11 wherein the user software application usage data corresponds to a set of software applications utilized by a user and a duration of the user executes each of the set of software applications ([0075], For example, the device monitoring server 122 may comprise a serve running SysTrack® software, or similar monitoring software, that collects data related to application usage, application performance, application faults, application latency, resource utilization, hard drive usage percent, number of hard drive errors, average memory usage (over a duration), average CPU usage (over a duration), hours powered on, CPU utilization, memory utilization, network bandwidth consumed per application, errors logged in the operating system, power average, time since the specified system’s installation, clock speed, memory type, OS install date, etc.).
With regard to claim 14, Korzunov teaches the computer readable medium of claim 11 wherein the first software application profile comprises a first set of common application utilized in the first job profile ([0089], In one example embodiment, the deployment module may determine a range of potential user profiles (job profiles) based on application profiles. The deployment engine can then select the user profile in the range of potential user profiles that best matches a user; [0090], In this example, the potential user profiles are AP1, AP2, AP3 (Application Profile ID, indicating plurality)… AP2 has an average CPU usage of 15% over the relevant period and has an average memory usage of 20% over the same period; [0091], If a user were determined to have a user profile with an average CPU usage of20% and an average memory usage of 20%, then the deployment engine would determine which of the potential user profiles best matches this specific usage. In this case, it is likely that AP2 would be the best match as it has similar usage and memory usage characteristics)
It would have been obvious to one of ordinary skill in the art at the time the invention was filed to apply the teachings of Kurzonov with the teachings of Morita and Choudhury in order to provide a computer readable medium that teaches software application profiles comprising applications utilized in a first job profile. The motivation for applying Kurzonov teaching with Morita and Choudhury teaching is to provide a computer readable medium that allows for improvement over monolithic deployment by enabling tailored deployments of applications in accordance to a user attribute, resulting in reduction of disk space and other computing resource utilization in monolithic deployment (Kurzonov, [0005]). Morita, Choudhury, and Kurzonov are analogous art directed towards monitoring arrangements of configurations and resource deployment. Therefore, it would have been obvious for one of ordinary skill in the art to combine Kurzonov with Morita and Choudhury to teach the claimed invention in order to provide an efficient deployment method that enables user-specific provisioning of relevant applications.
With regard to claim 15, Korzunov teaches the computer readable medium of claim 14, wherein the second software application profile comprises a second set of common applications utilized in the second job profile ([0089], In one example embodiment, the deployment module may determine a range of potential user profiles (job profiles) based on application profiles. The deployment engine can then select the user profile in the range of potential user profiles that best matches a user; [0090], In this example, the potential user profiles are AP1, AP2, AP3 (Application Profile ID, indicating plurality). AP1 has an average CPU usage of 30% over the relevant period and has an average memory usage of 50% over the same period) which is substantially similar to claim 14, and therefore rejected with similar rationale.
Examiner notes: It would be obvious for one of ordinary skill in the art to recognize that claim 15 is being substantially recited again with a second software application profile utilized in a second job profile, as evidenced in Korzunov, [0090].
Claim 12 is rejected under 35 U.S.C. 103 as being unpatentable over Morita in view of Korzunov as applied to claim 11 above, and further in view of April et al. Pub. No. US 2011/0015958 (hereinafter April).
With regard to claim 12, the combination does not explicitly teach that the job profile recommendation corresponds to a promotion.
April teaches the computer readable medium of claim 11 wherein the job profile recommendation corresponds to a promotion ([0045], Workforce planning system 300 may include promotion and mobility module 320. Promotion and mobility module 320 may identify job descriptions that have not been assigned to at least one employee for one or more times periods; [0078], Some embodiments may consider how a workforce planning model may relate to the mobility of employees within the organization – promotions, job changes, location changes. Some embodiments may utilize a promotion and mobility module, such as promotion and mobility module 320 of system 300, as part of this process. Attributes associated with each employee may include their level within the organization which may be defined either generically for the entire organization or by defined career paths by job family. Using historic data on mobility, a probability table may be developed. This table may predict the likelihood that employees with various combinations of attributes will move within the organization during the planning timeframe).
It would have been obvious to one of ordinary skill in the art at the time the invention was filed to apply the teachings of April with the teachings of Morita and Koruznov in order to provide a computer readable medium that teaches job profile recommendations correspond to occupational promotion. The motivation for applying April teaching with Morita and Korzunov teaching is to provide a computer readable medium that allows for predictive planning of resources, aiding resource allocation decisions in an organization (April, [0087] and [0089]). Morita, Korzunov, and April are analogous art directed towards resource planning and allocation. Therefore, it would have been obvious for one of ordinary skill in the art to combine April with Morita and Koruznov to teach the claimed invention in order to provide predictive planning and allocation of resources with respect to occupational promotions and mobility within an organization.
Claim 20 is rejected under 35 U.S.C. 103 as being unpatentable over Morita in view of Choudhury in view of Ding as applied to claim 1 above, and further in view of Eppstein et al. Patent No. US 8,234,650 B1 (hereinafter Eppstein).
With regard to claim 20, Morita teaches wherein the optimal computing device recommendation comprises a primary and secondary computing device recommendation, and the primary computing device recommendation is different from the secondary computing device recommendation ([0115],The method may further comprise ranking the hardware device and a second hardware device based on the overall health of the hardware device and an overall health of a second hardware device, and providing a recommendation for the hardware device and the second hardware device based on the ranking), and
However, the combination does not explicitly teach secondary computing device allocation with respect to primary device unavailability.
Eppstein teaches the provisioning system is configured to allocate the secondary computing device responsive to the primary computing device being unavailable (Col. 75, lines 13-25, If a database server is not available that satisfies the alternative resource requirement for database server A, then the alternative resource requirement for database server A is not fulfilled (e.g., allocation state module 1830 changes the allocation state to “NOT ALLOCATED”), and the requirement selection module 1850 selects the alternative resource requirement for database server B to be processed. If a database server that satisfies the child resource requirement for database server B is available, the identified database server is allocated to the IDC (E.g., allocated state module 1830 changes the allocation state for the alternative resource requirement for database server B to “ALLOCATED”).).
It would have been obvious to one of ordinary skill in the art at the time the invention was filed to apply the teachings of Eppstein with the teachings of Mortia, Choudhury, and Ding in order to provide a system that teaches secondary device allocation. The motivation for applying Eppstein teaching with Mortia, Choudhury, and Ding teaching is to provide a system that allows for allocation of alternative device recommendations such that enables high resource availability (Eppstein, Col. 66-Col. 67). Mortia, Choudhury, Ding and Eppstein are analogous art directed towards resource planning. Therefore, it would have been obvious for one of ordinary skill in the art to combine Eppstein with Mortia, Choudhury, and Ding to teach the claimed invention in order to provide alternative resource allocation associated with device availability.
Conclusion
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/I.A.C./Examiner, Art Unit 2195
/Aimee Li/Supervisory Patent Examiner, Art Unit 2195
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