DETAILED ACTION
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
2. The Amendment filed on January 27, 2026, has been entered. The examiner acknowledges the amendments to claims 1-9, 11-19.
Rejections under 35 U.S.C. § 101: Applicant argues the invention provides a concrete improvement to cloud-based telemetry ingestion and insight generation. Examiner notes that telemetry protocols are extremely common for network operations and network telemetry tools frequently run on generic processors (general-purpose CPUs). A specific, innovative and novel improvement to the telemetry ingestion and insight generation process is not apparent. Metrics or objective measures indicating improvements do not appear to have been disclosed. Arguments in favor of a practical application do not clearly describe new integration of additional elements, enhancements to processors or an improvement to the technological area. Given these observations, the argument of a concrete improvement to cloud-based telemetry ingestion and insight generation is not compelling and the invention describes software run on a generic processor. As a result, rejections under 35 U.S.C. § 101 will not be withdrawn.
Rejections under 35 U.S.C. § 102 and 103: Applicant’s amendments to the claims do provide additional technical limitations to the disclosure of the invention, but the additional limitations did not overcome a combination of new and prior art. For example, the disclosure of a specific scan scheduler listed 4 named scans. The naming of scans does not provide a specific usage rationale for employment or technical benefit, and are reasonably interpreted as equivalent to the function of the “scanning phase” of discovery in prior art [0099]. Specific novel elements were not apparent in the amendments and as a result, arguments that prior art does not anticipate amended claims are not compelling. As a result, the rejections under 35 U.S.C. § 102 and 103 will not be withdrawn.
Claim Rejections – 35 U.S.C. § 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 non-statutory subject matter. The claims, 1-20 are directed to a judicial exception (i.e., law of nature, natural phenomenon, abstract idea) without providing significantly more.
Step 1
Step 1 of the subject matter eligibility analysis per MPEP § 2106.03, required the claims to be a process, machine, manufacture or a composition of matter. Claims 1-20 are directed to a process (method), and product/article of manufacture (medium), which are statutory categories of invention.
Step 2A
Claims 1-20 are directed to abstract ideas, as explained below.
Prong one of the Step 2A analysis requires identifying the specific limitation(s) in the claim under examination that the examiner believes recites an abstract idea, and determining whether the identified limitation(s) falls within at least one of the groupings of abstract ideas of mathematical concepts, mental processes, and certain methods of organizing human activity.
Step 2A-Prong 1
The claims recite the following limitations that are directed to abstract ideas, which can be summarized as being directed to a method, the abstract idea, of determining insights on the use of applications, infrastructure, and on employees based on data from application usage in a cloud-based environment.
Claim 1 discloses a method, comprising: A method comprising steps of:
obtaining data from any of applications, infrastructure, and employees of a tenant organization, including a plurality of tenant organizations with the applications, infrastructure, and employees each assigned thereto, wherein the obtaining comprises initiating at least one of validation scans, periodic scans, or catchup scans for the organization and to return scan results including usage metrics tagged with metadata including at least user identity, time, and application identifier, and further comprising obtaining identity-provider login data for applications (following rules or instructions, observation, evaluation, judgement, opinion)
processing the data associated with the tenant organization to determine a plurality of insights; wherein, when multiple sources of usage data are available the processing comprises selecting a data source having a highest confidence as a default data source, and wherein at least one insight comprises determining active users and inactive users for the given application over a selected time period based on the identity-provider login data; and (economic principles and practices calculating costs, following rules or instructions, observation, evaluation, judgement, opinion, organizing human activity), and displaying the plurality of insights on a per-tenant organization basis based on the processing, including providing an engagement chart comprising a time-series graphical representation of the active users and the inactive users over the selected time period, (following rules or instructions, observation, evaluation, judgement, opinion).
Additional limitations employ the method to include usage data for tenant organizations, and selecting the usage data source having the highest confidence, (claim 2), providing a graphical representation of application usage in the tenant organization, (following rules or instructions, observation, evaluation, judgement, opinion- claim 3), wherein the usage data includes application license data, and wherein the application license data includes a number of active and inactive users associated with the one or more applications of the tenant organization (following rules or instructions, observation, evaluation, judgement, opinion - claim 4), displaying a savings opportunity
associated with the one or more applications of the tenant organization based on the application license data, (economic principles and practices calculating costs, following rules or instructions, observation, evaluation, judgement, opinion – claim 5), wherein the savings opportunity is associated with a specific application, or all applications associated with the tenant organization, (claim 6), wherein the data includes certification and compliance data associated with one or more applications of the tenant organization, following rules or instructions, observation, evaluation, judgement, opinion – claim 7), wherein the data includes productivity data associated with the employees of the tenant organization, and wherein the displaying includes providing a graphical representation of productivity for different locations associated with the tenant organization, (economic principles and practices calculating costs, following rules or instructions, observation, evaluation, judgement, opinion – claim 8), wherein the data includes location data associated with the employees of the tenant organization, and wherein the displaying includes providing a graphical representation of office utilization for the tenant organization, based on usage and distance information, (economic principles and practices calculating costs, following rules or instructions, observation, evaluation, judgement, opinion – claim 9), and wherein the data is obtained from any of an application connector and an identity provider associated with one or more applications, (claim 10).
Each of these claimed limitations employ abstract ideas including methods of organizing human activity to include fundamental economic principles or practices, calculating costs; managing personal behavior, including following rules or instructions and employing mental processes involving observation, evaluation, judgement, and opinion.
Claims 11-20 recite similar abstract ideas as those identified with respect to claims 1-10.
Thus, the concepts set forth in claims 1-20 recite abstract ideas.
Step 2A-Prong 2
As per MPEP § 2106.04, while the claims 1-20 recite additional limitations which are hardware or software elements such as a cloud-based system associated with any of applications, infrastructure, and employees of an organization, a non-transitory computer-readable medium, one or more processors, a display, by a scan scheduler, an application connector, these limitations are not sufficient to qualify as a practical application being recited in the claims along with the abstract ideas since these elements are invoked as tools to apply the instructions of the abstract ideas in a specific technological environment. The mere application of an abstract idea in a particular technological environment and merely limiting the use of an abstract idea to a particular technological field do not integrate an abstract idea into a practical application (MPEP § 2106.05 (f) & (h)).
Evaluated individually, the additional elements do not integrate the identified abstract ideas into a practical application. Evaluating the limitations as an ordered combination adds nothing that is not already present when looking at the elements taken individually.
The claims do not amount to a “practical application” of the abstract idea because they neither (1) recite any improvements to another technology or technical field; (2) recite any improvements to the functioning of the computer itself; (3) apply the judicial exception with, or by use of, a particular machine; (4) effect a transformation or reduction of a particular article to a different state or thing; (5) provide other meaningful limitations beyond generally linking the use of the judicial exception to a particular technological environment.
Accordingly, claims 1-20 are directed to abstract ideas.
Step 2B
Claims 1-20 do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional elements when considered both individually and as an ordered combination, do not amount to significantly more than the abstract idea.
The analysis above describes how the claims recite the additional elements beyond those identified above as being directed to an abstract idea, as well as why identified judicial exception(s) are not integrated into a practical application. These findings are hereby incorporated into the analysis of the additional elements when considered both individually and in combination.
For the reasons provided in the analysis in Step 2A, Prong 1, evaluated individually, the additional elements do not amount to significantly more than a judicial exception. Thus, taken alone, the additional elements do not amount to significantly more than a judicial exception.
Evaluating the claim limitations as an ordered combination adds nothing that is not already present when looking at the elements taken individually. In addition to the factors discussed regarding Step 2A, prong two, there is no indication that the combination of elements improves the functioning of a computer or improves any other technology. Their collective functions merely amount to instructions to implement the identified abstract ideas on a computer.
Therefore, since there are no limitations in the claims 1-20 that transform the exception into a patent eligible application such that the claims amount to significantly more than the exception itself, the claims are directed to non-statutory subject matter and are rejected under 35 U.S.C. § 101.
Claim Rejections 35 U.S.C. §102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form
the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claims 1-3, 11-13, are rejected under 35 U.S.C. § 102(a)(1) as being taught by Davies,
(US 20220327172 A1), hereafter Davies, “Evaluation And Recommendation Engine For A Remote Network Management Platform.”
Regarding Claim 1, A method comprising steps of:
obtaining data from a cloud-based system associated with any of applications,
infrastructure, and employees of a tenant organization, Davies teaches, (providing mechanisms through which telemetry data can be collected from a computational instance of a cloud-based remote network management platform that is used by an enterprise. The data sources for the telemetry data might be one or more database tables, configuration files, log files, and/or user profiles, and may represent key performance indicators (KPIs) and/or metrics of the computational instance, [0002]),
wherein the cloud-based system includes a plurality of organizations with the applications, infrastructure, and employees each assigned thereto; (a cloud-based remote network management platform that is used by an enterprise [0002], often has dozens or hundreds of these custom software applications. Nonetheless, the advantages provided by the embodiments herein are not limited to large enterprises and may be applicable to an enterprise, or any other type of organization, of any size. [0030], the concept of Application Platform as a Service (aPaaS) is introduced, to intelligently automate workflows throughout the enterprise. An aPaaS system is hosted remotely from the enterprise, but may access data, applications, and services, [0033], and may support development and execution of model-view-controller (MVC) applications. [ ] These applications may be web-based, and offer create, read, update, and delete(CRUD) capabilities. This allows new applications to be built on a common application infrastructure, [0034] and the enterprise data platform API may allow the telemetry application to request this usage data per application. For a given period of time (e.g., an hour, day, week, or month) and application, the usage data may include a number of users that accessed the application, [0132]),
wherein the obtaining comprises initiating by a scan scheduler, (discovery may proceed in four logical phases: scanning, classification, identification, and exploration. Each phase of discovery involves various types of probe messages being transmitted by proxy servers 312 to one or more devices in managed network, [0099], at least one of validation scans, periodic scans, (virus scanning, [0066]), or catchup scans for the organization and invoking an application connector (there are a number of areas that the enterprise can focus on to improve its digital maturity, such as increasing user utilization of the computational instance(s) and developing more custom applications and integrations,[0188]), to execute the at least one scan via an application programming interfaced of an application associated with the organization and to return scan results including usage metrics tagged with metadata including at least user identity, time, and application identifier, (Model designer 802 is a set of user interfaces and calculation models that allow the user to identify KPIs and/or metrics that ultimately can be used as input for calculation of one or more of metrics 708. These KPIs and/or metrics may be stored in KPI library 804. Examples of KPIs and/or metrics may include those related to application usage, application performance, process usage, process performance, how users employ applications, and so on, [0129]), and further comprising obtaining identity-provider login data for applications lacking the application connector,
processing the data associated with the organization to determine a plurality of
insights; (Machine learning engine 816 may use artificial intelligence to derive insights regarding application usage and trends that might not be practical to obtain in other manners. For example, determination of a trend involving millions of data elements collected over several months might be best performed by one or more machine learning algorithms, [0136], and FIG 8), wherein, when multiple sources of usage data are available the processing comprises selecting a data source (The data sources for the telemetry data might be one or more database tables, configuration files, log files, and/or user profiles, and may represent key performance indicators (KPIs) and/or metrics of the computational instance, [0002], having a highest confidence as a default data source, (The KPIs and/or metrics may be arranged in various ways (e.g., into feature vectors) and processed by statistical algorithms, [ ], The output of these algorithms may include one or more indicators that measure the present value, potential value, and/or digital maturity of the enterprise's use of the computational instance, [0002], and wherein at least one insight comprises determining active users and inactive users for the given application over a selected time period based on the identity-provider login data; (For a given period of time (e.g., an hour, day, week, or month) and application, the usage data may include a number of users that accessed the application, an average session length of these users, an amount of data generated by the application (e.g., written to database tables or log files), and/or a number of transactions served, [0132]), and displaying the plurality of insights on a per-tenant organization basis based on the processing, including providing an engagement chart comprising a time-series graphical representation of the active users, and the inactive users over the selected time period, (providing mechanisms through which telemetry data can be collected from a computational instance [ ] may represent key performance indicators (KPIs) and/or metrics of the computational instance, [0002]; notably, the data representing present value, potential value, digital maturity, and various combinations thereof may be visually displayed in a number of ways using graphs, charts, tables, heatmaps, and so on. These displays may depict the current values of the data and/or trends indicating how the data has changed over time, [0147] and FIG 8; and weight may be assigned to computational instances based on the number of user accounts thereon, number of active users (e.g., users who have logged on at least once in the last week or month), actual use (e.g., CPU usage, main memory usage, disk usage), etc. [0127].
Regarding claim 2, The method of claim 1, wherein the data includes
usage data associated with one or more applications of the tenant organization, Davies teaches, (providing mechanisms through which telemetry data can be collected from a computational instance [ ] may represent key performance indicators (KPIs) and/or metrics of the computational instance, [0002]), Wherein the usage data is collected by the scan scheduler (discovery may proceed in four logical phases: scanning, classification, identification, and exploration, [0099]), initiating the at least one of the validation scan, the periodic scan, (virus scanning, [0066]), or the catchup scan for the one or more applications and invoking the application connector (there are a number of areas that the enterprise can focus on to improve its digital maturity, such as increasing user utilization of the computational instance(s) and developing more custom applications and integrations,[0188]), to execute the at least one scan via the application programming interface of the one or more applications and to return scan results including the usage data, (Model designer 802 is a set of user interfaces and calculation models that allow the user to identify KPIs and/or metrics that ultimately can be used as input for calculation of one or more of metrics 708. These KPIs and/or metrics may be stored in KPI library 804. Examples of KPIs and/or metrics may include those related to application usage, application performance, process usage, process performance, how users employ applications, and so on, [0129]), and wherein when multiple sources of usage data are available for a given application the method further comprises selecting as a default the usage data source having the highest confidence. (The KPIs and/or metrics may be arranged in various ways (e.g., into feature vectors) and processed by statistical algorithms, [ ], The output of these algorithms may include one or more indicators that measure the present value, potential value, and/or digital maturity of the enterprise's use of the computational instance, [0002]).
Regarding Claim 3, The method of claim 2, wherein the displaying includes providing a graphical representation of application usage associated with the one or more applications of the tenant organization. Davies teaches, (providing mechanisms through which telemetry data can be collected from a computational instance of a cloud-based remote network management platform that is used by an enterprise, [0002], and model designer 802 is a set of user interfaces and calculation models that allow the user to identify KPIs and/or metrics that ultimately can be used as input for calculation of one or more of metrics 708. These KPIs and/or metrics may be stored in KPI library 804. Examples of KPIs and/or metrics may include those related to application usage, application performance, process usage, process performance, how users employ applications, and so on, [0129], and such an aPaaS system may represent a GUI in various ways. For example, a server device of the aPaaS system may generate a representation of a GUI using a combination of HTML and JAVASCRIPT®. The JAVASCRIPT® may include client-side executable code, server-side executable code, or both. The server device may transmit or otherwise provide this representation to a client device for the client device to display on a screen according to its locally-defined look and feel. Alternatively, a representation of a GUI may take other forms, such as an intermediate form (e.g., JAVA® byte-code) that a client device can use to directly generate graphical output therefrom, [0044]).
Claims 11-13 are rejected for reasons corresponding to those provided for Claims 1-3. In these claims, the addition of a non-transitory computer-readable medium does not change the rational for the rejections under 35 U.S.C § 103 or the referenced prior art (Davies teaches the system may include a non-transitory computer-readable medium [0005]).
Claim Rejections 35 U.S.C. §103
The following is a quotation of 35 U.S.C. § 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under
35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 4-6, 8, 14-16, 18, are rejected under 35 U.S.C. § 103 as being taught by Davies,
(US 20220327172 A1), hereafter Davies, “Evaluation And Recommendation Engine For A Remote Network Management Platform,” in view of Deodhar, (US 20170116552 A1), hereafter Deodhar, “System And Method To Measure, Aggregate And Analyze Exact Effort And Time Productivity.”
Regarding claim 4, The method of claim 2, wherein the usage data includes application
license data, and wherein the application license data includes a number of active and inactive users associated with the one or more applications of the tenant organization. Davies does not teach, Deodhar teaches, (optimizing cost of software assets by knowing exact usage of software licenses:—The organizations invest significantly in software license fees, as a recurring cost of subscription or annual maintenance fees. They are usually able to keep track of licenses purchased and deployed, but often cannot verify the actual usage. The users may stop using particular software for various reasons, including because they left the company, they may uninstall without informing the administrator, and PC's where the software is installed may be re-formatted. This report fills that gap and enables the organization to know the exact usage of licenses, [1036], and The system captures all the work effort put on by the users. The system tracks the daily time spent by employees. This is mapped to activities and objectives that are automatically inferred based on the applications and artifacts being used, the source of offline time usage, and the employee's position in the organization and role therein. The captured individual work effort is mapped to the organization's hierarchy and business attributes. As a result, Work Patterns and trends within each sub-unit/operational dimension of the business are identified, [Abstract]).
Davies and Deodhar are both considered to be analogous to the claimed invention
because they are both in the field of cloud operations analysis and efficiency. It would have been obvious to one of ordinary skill in the art before the effective filing date to combine the management insights of Davies with the expansive data collection of Deodhar to generate intelligent reports for improving (optimizing workforce and operational efficiency) operational effectiveness and talent management in each sub-unit, Deodhar, [0949].
Regarding claim 5, The method of claim 4, wherein the steps further comprise:
displaying a savings opportunity associated with the one or more applications of
the tenant organization based on the application license data. Davies does not teach, Deodhar teaches, (Intelligent reports [0949], (section) VII. Optimizing cost of software assets by knowing exact usage of software licenses. [ ] This report fills that gap and enables the organization to know the exact usage of licenses, and thereby reduce costs by only renewing the required number of licenses each year, [1036] on quarterly basis, or as requested, [1037] at organization level, [1038] for each user for past three months; get all application names (pc and web) being used and usage times; [1039] analyze the above and generate a table consisting of one row per application name; [1040] for each application name, determine total count of users and total time usage; compare total count of users against the paid licenses; if (paid licenses>count of users) then reduce the licenses renewed; [1041]; Work Patterns and trends within each sub-unit/operational dimension of the business are identified, [Abstract]).
Davies and Deodhar are both considered to be analogous to the claimed invention because they are both in the field of cloud operations analysis and efficiency. It would have been obvious to one of ordinary skill in the art before the effective filing date to combine the management insights of Davies with the expansive data collection of Deodhar to generate intelligent reports for improving (optimizing workforce and operational efficiency) operational effectiveness and talent management in each sub-unit, Deodhar, [0949].
Regarding claim 6, The method of claim 5, wherein the savings opportunity is
associated with a specific application, or all applications associated with the tenant organization. Davies does not teach, Deodhar teaches, (optimizing cost of software assets by knowing exact usage of software licenses:—The organizations invest significantly in software license fees, as a recurring cost of subscription or annual maintenance fees. They are usually able to keep track of licenses purchased and deployed, but often cannot verify the actual usage. The users may stop using particular software for various reasons, including because they left the company, they may uninstall without informing the administrator, and PC's where the software is installed may be re-formatted. [ ] Analyze the above and generate a table consisting of one row per application name; [1040] for each application name, determine total count of users and total time usage; compare total count of users against the paid licenses; if (paid licenses>count of users) then reduce the licenses renewed; [1041]; Work Patterns and trends within each sub-unit/operational dimension of the business are identified, [Abstract]).
Davies and Deodhar are both considered to be analogous to the claimed invention because they are both in the field of cloud operations analysis and efficiency. It would have been obvious to one of ordinary skill in the art before the effective filing date to combine the management insights of Davies with the expansive data collection of Deodhar to generate intelligent reports for improving (optimizing workforce and operational efficiency) operational effectiveness and talent management in each sub-unit, Deodhar, [0949].
Regarding claim 8, The method of claim 1, wherein the data includes productivity data
associated with the employees of the tenant organization, and wherein the displaying includes providing a graphical representation of productivity for different locations associated with the tenant organization. Davies does not teach, Deodhar teaches, (a system that provides for exact effort and time productivity measurement at organization level without any manual definition or configuration of employee groups or attributes, [0045], and derives a per-employee Daily Average of Work Pattern, as part of the built-in analytics, specifically to allow for meaningful comparison between two or more organization sub-units, irrespective of the nature of business and role, [0051], where ‘organization sub-units’ [ ] relates to the entire organization or any part thereof, including business units, projects, teams, locations, and individual employees. [0013], displaying the organization trends, reports, alerts, goals and administrative functions depending upon user's position and role in the organization hierarchy; and generating user-defined custom reports from the n-dimensional effort data cube, [claim 16], and Work Patterns and trends within each sub-unit/operational dimension of the business are identified, [Abstract].
Davies and Deodhar are both considered to be analogous to the claimed invention because they are both in the field of cloud operations analysis and efficiency. It would have been obvious to one of ordinary skill in the art before the effective filing date to combine the management insights of Davies with the expansive data collection of Deodhar to generate intelligent reports for improving (optimizing workforce and operational efficiency) operational effectiveness and talent management in each sub-unit, Deodhar, [0949].
Claims 14-16, 18, are rejected for reasons corresponding to those provided for Claims 4-6, 8. In these claims, the addition of a non-transitory computer-readable medium does not change the rational for the rejections under 35 U.S.C § 103 or the referenced prior art (Davies teaches the system may include a non-transitory computer-readable medium [0005]).
Claims 7, and 17 are rejected under 35 U.S.C. § 103 as being taught by Davies, (US
20220327172 A1), hereafter Davies, “Evaluation And Recommendation Engine For A Remote Network Management Platform,” in view of Dimmler, (US 20240195838 A1), hereafter Dimmler, “Security Policy Compliance Verification and Certification as a Service Leveraging Systems Providing Access Management as a Service.”
Regarding claim 7, The method of claim 1, wherein the data includes certification and
compliance data associated with one or more applications of the tenant organization. (Davies does not teach, Dimmler teaches, (systems and methods for leveraging a data and service access management system architecture to provide security policy compliance verification and
certification as a service, [0001], aggregating, receiving (e.g., via push) and/or otherwise obtaining usage data generated in respect of a particular organization across multiple software products and/or multiple software product vendors (e.g., by the access management service
106).
Davies and Dimmler are both considered to be analogous to the claimed invention because they are both in the field of cloud operations analysis and efficiency. It would have been obvious to one of ordinary skill in the art before the effective filing date to combine the management insights of Davies with the security considerations of Dimmler to prevent exposing organizations, Managed Service Providers, and software vendors alike to
unknowable security risks, Dimmler, [0003]).
Claim 17 is rejected for reasons corresponding to those provided for Claim 7. In this claim, the addition of a non-transitory computer-readable medium does not change the rational for the rejections under 35 U.S.C § 103 or the referenced prior art (Davies teaches the system may include a non-transitory computer-readable medium [0005]).
Claims 9 and 19 are rejected under 35 U.S.C. § 103 as being taught by Davies, (US
20220327172 A1), hereafter Davies, “Evaluation And Recommendation Engine For A Remote Network Management Platform,” in view of Ray, (CN 110709786 A), hereafter Ray, “Has a Spatial Profile of a Building Management System,” in further view of Matlick, (US 20220230078 A1), hereafter Matlick, “Machine Learning Techniques for Associating Network Addresses with Information Object Access Locations.”
Regarding claim 9, The method of claim 1, wherein the data includes location data
associated with the employees of the tenant organization, and wherein the displaying includes providing a graphical representation, Davies teaches, (Notably, the data representing present value, potential value, digital maturity, and various combinations thereof may be visually displayed in a number of ways using graphs, charts, tables, heatmaps, and so on. These displays may depict the current values of the data and/or trends indicating how the data has changed over time, [0147], of office utilization for the tenant organization, Davies does not teach, Ray teaches, (the space utilization circuit 200 (including by aggregator 220, the device volume space utilization space utilization circuits 226 and circuit 228) spatial profile using a variety of space to determine the optimal is defined using the data types of each space. Then, the space utilization circuit 200 identifies each available in the space of a preferred sensor provides data of this type and/or other data source, and receiving data from these sensors and data source. Then, the space utilization circuit 200 for generating space utilization attribute of the algorithm and calculating the space using the attribute. space utilization circuit 200 at a certain time (e.g., current time) tracking space utilization, and storing historical data to provide historical time space use, Ray, [p.44]),
Davies and Ray are both considered to be analogous to the claimed invention because they are both in the field of analysis and efficiency. It would have been obvious to one of ordinary skill in the art before the effective filing date to combine the operational management insights of Davies with the building management techniques of Ray to show the operation data for the device of the physical space of each other so as to minimize the operation cost, Ray, [p.28],
wherein the location data comprises inferred office location data determined by analyzing Internet Protocol (IP) addresses observed for users of the tenant organization over a predefined period to identify candidate office IP addresses, (Matlick teaches, (ML/AI techniques for associating network addresses with locations from which content and/or information objects is/are accessed, [0002]), based on a threshold number or fraction of users observed from a same IP address on a day and recurrence of a same IP pattern over the predefined period, removing from the candidate office IP addresses any IP address owned by a public cloud provider, (In one example, the feature generator 1602 generates a feature
1604E that identifies the percentage of information objects accessed by users with mobile devices, such as cell phones, tablets, laptops, wearables, and/or the like, the feature 1604E may help distinguish private org locations where users mostly use personal computers or laptops from public org locations where users may more frequently access content with cell phones, [0247]), generating a confidence score for a candidate office IP address, (The network address location predictions can be used to more efficiently process events, more accurately calculate consumption scores, and more accurately detect associated surges such as organization (org) surges (also referred to as “company surges” or the like). The more accurate intent data and consumptions scores allow third party service providers to conserve computational and network resources by providing a means for better targeting users so that unwanted and seemingly random content is not distributed to users that do not want such content, [0032]). based on a digital experience monitoring data including multi-traceroute (MTR) data and based on whether at last a threshold percentage of users observed through the candidate office IP address share a same or similar WiFi access point name, (FIG. 16 shows how a network address classification system (NACS) 1600 (also referred to as “network classifier 1600”) according to various embodiments. The NACS 1600 identifies different types of entities associated with various network addresses 954. To do so, the NACS 1600 leverages the fact that network addresses 954 may be associated with different physical locations. [0210]), and clustering candidate office IP addresses into an inferred office location based on geo distance being less than a distance threshold, (a content consumption monitor (CCM), may be provided by (or operated by) a cloud computing service and/or a cluster of machines, [0046], and in particular, to associating network addresses with the locations of organizations from which information objects are accessed. According to various embodiments, a network address classification system (NACS) uses various machine learning (ML) techniques to determine physical locations associated with network addresses, [0028].
Davies and Matlick are both considered analogous to the claimed invention because they are both in the field of analysis and efficiency. It would have been obvious to one of ordinary skill in the art before the effective filing date to combine the operational management insights of Davies with the network asset location techniques of Matlick to target an org's engagement with the content, which may be based on an aggregate of engagement of individual users associated with the org, [0151]).
Claim 19 is rejected for reasons corresponding to those provided for Claim 9. In this claim, the addition of a non-transitory computer-readable medium does not change the rational for the rejections under 35 U.S.C § 103 or the referenced prior art (Davies teaches the system may include a non-transitory computer-readable medium [0005]).
Claims 10, 20 are rejected under 35 U.S.C. § 103 as being taught by Davies, (US
20220327172 A1), hereafter Davies, “Evaluation And Recommendation Engine For A Remote Network Management Platform,” in view of Monro (US 20210377252 A1), hereafter Monro, “Application Integration using Multiple User Identities.”
Regarding claim 10, The method of claim 1, wherein the data is obtained from any of
an application connector, and an identity provider associated with one or more applications.
Davies does not teach, Monro teaches, (the first set of authentication tokens is issued by a first identity provider that the server application recognizes as having authority to authenticate the user, [0009] and receiving, by an application connector, from a client application, a first set of authentication tokens that authorize a user of the client application to acquire target data provided by a server application. The method further comprises receiving, by the application connector, from the client application, a second set of authentication tokens that authorize the same user to access a connected application. The method further comprises sending, from the application connector, to the server application, a first request to acquire the target data provided by the server application, the first request including the first set of authentication tokens and an identifier of the target data, [0004]).
Davies and Monro are both considered to be analogous to the claimed invention because they are both in the field of cloud-based systems and application management. It would have been obvious to one of ordinary skill in the art before the effective filing date to combine the operational management insights of Davies with the data extraction techniques of Monro to provide users the ability to search for relevant information across files and applications, [0053]).
Claim 20 is rejected for reasons corresponding to those provided for Claim 10. In this claim, the addition of a non-transitory computer-readable medium does not change the rational for the rejections under 35 U.S.C § 103 or the referenced prior art (Davies teaches the system may include a non-transitory computer-readable medium [0005]).
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.
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure or directed to the state of the art is listed on the enclosed PTO-892.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to MICHAEL BOROWSKI whose telephone number is (703)756-1822. The examiner can normally be reached M-F 8-4:30.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Jerry O’Connor can be reached on (571) 272-6787. The fax phone number for the organization where this application or proceeding is assigned is (571) 273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at (866) 217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call (800) 786-9199 (IN USA OR CANADA) or (571) 272-1000.
/MB/
Patent Examiner, Art Unit 3624
/MEHMET YESILDAG/Primary Examiner, Art Unit 3624
Prosecution Insights