DETAILED ACTION
Notice of 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 .
Information Disclosure Statement
The information disclosure statement submitted on 4/17/2023 has been considered.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Regarding Step 1 of the Alice/Mayo framework, Claims 1-7 are directed to a method (a process), Claims 8-14 are directed to a non-transitory processor-readable storage medium (an article of manufacture), and Claims 15-20 are directed to an apparatus (a machine), which each fall within one of the four statutory categories of inventions.
Regarding Claim 1
Step 2A, prong 1 (Is the claim directed to a law of nature, a natural phenomenon or an abstract idea).
Claim 1 recites the following mental processes, that in each case under the broadest reasonable interpretation, covers performance of the limitation in the mind (including an observation, evaluation, judgment, opinion) or with the aid of pencil and paper but for the recitation of generic computer components (e.g., “computer-implemented”, “processor”, “memory”, “machine learning-based process”).
determining that a value of a first parameter associated with the request varies over the particular period of time; (under the broadest reasonable interpretation, this limitation can be performed mentally, for example, a human such as a salesperson can mentally determine that the price for cloud services will vary over a particular period of time)
predicting, ... the value of the first parameter over the particular period of time based at least in part on historical time-series data for the first parameter; (under the broadest reasonable interpretation, this limitation can be performed mentally, for example, a human such as a salesperson can look at historical time-series data for the previous year for the same time period, and use such historical data to predict pricing for the following year)
generating a plurality of configurations for the request based at least in part on the predicted value of the first parameter, wherein each of the plurality of configurations corresponds to a different time interval within the particular period of time and comprises: (under the broadest reasonable interpretation, this limitation can be performed mentally, for example, a human such as a salesperson can determine different configurations (or bundles) of services over different periods of time)
(i) a second parameter, associated with a type of resource, that is fixed over the corresponding time interval and (under the broadest reasonable interpretation, this limitation can be performed mentally, for example, a human such as a salesperson can identify second parameters, such as cloud storage space, where the price is going to be fixed per GB over a time interval)
(ii) one or more uncertainty criteria corresponding to the second parameter, wherein the one or more uncertainty criteria are based at least in part on at least one location associated with the at least one user and at least one type of the one or more of the hardware components and the software components; (under the broadest reasonable interpretation, this limitation can be performed mentally, for example, a human such as a salesperson can determine “uncertainty criteria” such as the amount of storage actually used, and such criteria can be based at least in part on the location of the user because different locations may have different prices due to currency differences)
Claim 1 further recites the following limitations that each pertain to a method of organizing human activity, specifically “managing personal behavior or relationships or interactions between people,” which is another type of judicial exception as set forth by MPEP 2106.04(a). Such limitations also pertain to “commercial or legal interactions” which is another type of judicial exception as set forth by MPEP 2106.04(a).
obtaining a request from at least one user for one or more of hardware components and software components over a particular period of time (under the broadest reasonable interpretation, this pertains to the social activity of “providing information to a person” and also to the commercial interactions related to “marketing or sales activities or behaviors”)
obtaining, from the at least one user, a selection of one of the plurality of configurations; and (under the broadest reasonable interpretation, this pertains to the social activity of “providing information to a person” and also to the commercial interactions related to “marketing or sales activities or behaviors”)
Step 2A, prong 2 (Does the claim recite additional elements that integrate the judicial exception into a practical application?).
The judicial exception is not integrated into a practical application. In particular, the claim recites the additional elements (e.g., “computer-implemented”, “processor”, “memory”, “machine learning-based process”) which are recited at a high-level of generality such that they amount to no more than mere instructions to apply the exception using a generic computer component (See MPEP 2106.05(f)).
Regarding the “using a machine learning-based process” limitation, such limitation is recited at a high-level of generality and amounts to no more than adding the words “apply it” (or an equivalent) with the judicial exception. In particular, the claim only recites the additional element of a machine-learning based process. This additional element is recited at a high-level of generality and amounts to no more than mere instructions to apply the exception using a generic computer component (a machine-learning based process). Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea (See MPEP 2106.05(f)).
Regarding the “initiating one or more automated actions in response to at least one of the one or more uncertainty criteria of the selected configuration being satisfied during the corresponding time interval” limitation, such limitation is recited at a high-level of generality and amounts to no more than adding the words “apply it” (or an equivalent) with the judicial exception, because the limitation attempts to cover a solution to an identified problem with no restriction on how the result is accomplished, or provides no description of the mechanism for accomplishing the result. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea (See MPEP 2106.05(f)). Moreover, such limitation is recited at a high-level of generality and amounts to no more than adding the words “apply it” (or an equivalent) with the judicial exception. In particular, the claim only recites the additional element of an “automated action”. This additional element is recited at a high-level of generality and amounts to no more than mere instructions to apply the exception using a generic computer component (“automated action”). Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea (See MPEP 2106.05(f)).
Regarding the “wherein the method is performed by at least one processing device comprising a processor coupled to a memory” limitation, such limitations are recited at a high-level of generality and amount to no more than adding the words “apply it” (or an equivalent) with the judicial exception. In particular, the claim only recites the additional elements of a processor and a memory. These additional elements are recited at a high-level of generality and amount to no more than mere instructions to apply the exception using generic computer components (a processor and a memory). Accordingly, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea (See MPEP 2106.05(f)).
Step 2B (Does the claim recite additional elements that amount to significantly more than the judicial exception?)
In accordance with Step 2B, the claim does not include additional elements that are sufficient to amount to significantly more that the judicial exception. As discussed above, the additional elements (e.g., “computer-implemented”, “processor”, “memory”, “machine learning-based process”) are recited at a high-level of generality such that they amount to no more than mere instructions to apply the exception using a generic computer component (See MPEP 2106.05(f)).
Regarding the “using a machine learning-based process” limitation, such limitation is recited at a high-level of generality and amounts to no more than adding the words “apply it” (or an equivalent) with the judicial exception, because the limitation merely provides instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea. Accordingly, this additional element does not add significantly more than the judicial exception. (See MPEP 2106.05(f)).
Regarding the “initiating one or more automated actions in response to at least one of the one or more uncertainty criteria of the selected configuration being satisfied during the corresponding time interval” limitation, such limitation is recited at a high-level of generality and amounts to no more than adding the words “apply it” (or an equivalent) with the judicial exception, because the limitation attempts to cover a solution to an identified problem with no restriction on how the result is accomplished, or provides no description of the mechanism for accomplishing the result. Accordingly, this additional element does not add significantly more than the judicial exception. (See MPEP 2106.05(f)). Moreover, such limitation is recited at a high-level of generality and amounts to no more than adding the words “apply it” (or an equivalent) with the judicial exception, because the limitation merely provides instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea. Accordingly, this additional element does not add significantly more than the judicial exception. (See MPEP 2106.05(f)).
Regarding the “wherein the method is performed by at least one processing device comprising a processor coupled to a memory” limitation, such limitation is recited at a high-level of generality and amounts to no more than adding the words “apply it” (or an equivalent) with the judicial exception, because the limitation merely provides instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea. Accordingly, this additional element does not add significantly more than the judicial exception. (See MPEP 2106.05(f)).
Regarding Claim 2
Step 2A, Prong 1
generating one or more additional configurations for the request for a remaining portion of the particular period of time in response to the at least one of the one or more uncertainty criteria of the selected configuration being satisfied. (under the broadest reasonable interpretation, this limitation can be performed mentally, for example, a human such as a salesperson can generate additional configurations for a remaining period of time in response to one or more uncertainty criteria of the selected configuration being satisfied)
Regarding Step 2A, Prong 2, the claim does not include any additional elements that integrate the judicial exception into a practical application and regarding Step 2B, there are no additional elements recited that amount to significantly more than the judicial exception.
Regarding Claim 3
Step 2A, Prong 1
sending an alert to the at least one user; and (under the broadest reasonable interpretation, this pertains to the social activity of “providing information to a person” under the “managing personal behavior or relationships or interactions between people” category of abstract ideas)
adjusting at least a portion of the one or more of the hardware components and the software components associated with the request. (under the broadest reasonable interpretation, this limitation can be performed mentally, for example, a human such as a salesperson can adjust the hardware and/or software components used to fulfill the request)
Regarding Step 2A, Prong 2, the claim does not include any additional elements that integrate the judicial exception into a practical application and regarding Step 2B, there are no additional elements recited that amount to significantly more than the judicial exception.
Regarding Claim 4
Step 2A, Prong 1
obtaining additional time-series data for the first parameter over at least a portion of the time interval corresponding to the selected configuration; and (under the broadest reasonable interpretation, this limitation can be performed mentally, for example, a human can mentally predict time-series prices over a portion of the time interval corresponding to the selected configuration)
updating the predicted value of the first parameter based at least in part on the additional time-series data. (under the broadest reasonable interpretation, this limitation can be performed mentally, for example, a human such as a salesperson can mentally update total pricing predictions)
Regarding Step 2A, Prong 2, the claim does not include any additional elements that integrate the judicial exception into a practical application and regarding Step 2B, there are no additional elements recited that amount to significantly more than the judicial exception.
Regarding Claim 5
Step 2A, Prong 2
Regarding the “wherein the machine learning-based process comprises at least one of a linear regression model and an autoregressive model” limitation, such limitation is recited at a high-level of generality and amounts to no more than adding the words “apply it” (or an equivalent) with the judicial exception. In particular, the claim only recites the additional element of a machine learning based process using a particular type of model. This additional element is recited at a high-level of generality and amounts to no more than mere instructions to apply the exception using a generic computer component (a machine learning based process). Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea (See MPEP 2106.05(f)). Moreover, such limitation amounts to no more than generally linking the use of a judicial exception to a particular technological environment or field of use. As explained by the Supreme Court, a claim directed to a judicial exception cannot be made eligible "simply by having the applicant acquiesce to limiting the reach of the patent for the formula to a particular technological use." Diamond v. Diehr, 450 U.S. 175, 192 n.14, 209 USPQ 1, 10 n. 14 (1981). Thus, limitations that amount to merely indicating a field of use or technological environment in which to apply a judicial exception do not integrate a judicial exception into a practical application.
Step 2B
Regarding the “wherein the machine learning-based process comprises at least one of a linear regression model and an autoregressive model” limitation, such limitation is recited at a high-level of generality and amounts to no more than adding the words “apply it” (or an equivalent) with the judicial exception, because the limitation merely provides instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea. Accordingly, this additional element does not add significantly more than the judicial exception. (See MPEP 2106.05(f)). Moreover, such limitation amounts to no more than generally linking the use of a judicial exception to a particular technological environment or field of use as explained above, which does not amount to significantly more than the judicial exception. MPEP 2106.05(h).
Regarding Claim 6
Step 2A, Prong 1
wherein the at least one location is located within a first country, and wherein at least one second location associated with an entity providing the one or more of the hardware components and the software components is located within a different, second country. (under the broadest reasonable interpretation, this limitation can be performed mentally, for example, a human such as a salesperson can mentally consider pricing constraints based on the user being in a first country and the hardware/software components being in a different country)
Regarding Step 2A, Prong 2, the claim does not include any additional elements that integrate the judicial exception into a practical application and regarding Step 2B, there are no additional elements recited that amount to significantly more than the judicial exception.
Regarding Claim 7
Step 2A, Prong 1
wherein the one or more uncertainty criteria are further based on a type of the at least one user. (under the broadest reasonable interpretation, this limitation can be performed mentally, for example, a human such as a salesperson can mentally base uncertainty criteria on a type of user, such as a large corporation vs. a university user)
Regarding Step 2A, Prong 2, the claim does not include any additional elements that integrate the judicial exception into a practical application and regarding Step 2B, there are no additional elements recited that amount to significantly more than the judicial exception.
Regarding Claim 8
Step 2A, Prong 1
Claim 8 recites a non-transitory processor-readable storage medium that corresponds to the method of claim 1, and therefore the analysis under Step 2A, Prong 1 with respect to claim 1 also applies to this claim 8. While claim 8 recites additional generic computing components (“non-transitory processor-readable storage medium”, “processing device”, “program code”, and “machine learning-based process”), such additional generic computing components do not change the analysis under Step 2A, Prong 1.
Step 2A, Prong 2
Claim 8 recites a non-transitory processor-readable storage medium that corresponds to the method of claim 1, and therefore the analysis under Step 2A, Prong 2 with respect to claim 1 also applies to this claim 8. While claim 8 recites additional generic computing components (“non-transitory processor-readable storage medium”, “processing device”, “program code”, and “machine learning-based process”), such additional generic computing components do not change the analysis under Step 2A, Prong 2.
Step 2B
Claim 8 recites a non-transitory processor-readable storage medium that corresponds to the method of claim 1, and therefore the analysis under Step 2B with respect to claim 1 also applies to this claim 8. While claim 8 recites additional generic computing components (“non-transitory processor-readable storage medium”, “processing device”, “program code”, and “machine learning-based process”), such additional generic computing components do not change the analysis under Step 2B.
Claims 9-14 depend from claim 8 and correspond to the methods of claims 2-7, respectively, and are therefore rejected for the same reasons above with respect to claim 8 and claims 2-7, respectively.
Regarding Claim 15
Step 2A, Prong 1
Claim 15 recites an apparatus that corresponds to the method of claim 1, and therefore the analysis under Step 2A, Prong 1 with respect to claim 1 also applies to this claim 15. While claim 15 recites additional generic computing components (“processing device”, “processor”, “memory”, and “machine learning-based process”), such additional generic computing components do not change the analysis under Step 2A, Prong 1.
Step 2A, Prong 2
Claim 15 recites an apparatus that corresponds to the method of claim 1, and therefore the analysis under Step 2A, Prong 2 with respect to claim 1 also applies to this claim 15. While claim 15 recites additional generic computing components (“processing device”, “processor”, “memory”, and “machine learning-based process”), such additional generic computing components do not change the analysis under Step 2A, Prong 2.
Step 2B
Claim 15 recites an apparatus that corresponds to the method of claim 1, and therefore the analysis under Step 2B with respect to claim 1 also applies to this claim 15. While claim 15 recites additional generic computing components (“processing device”, “processor”, “memory”, and “machine learning-based process”), such additional generic computing components do not change the analysis under Step 2B.
Claims 16-20 depend from claim 15 and correspond to the methods of claims 2-6, respectively, and are therefore rejected for the same reasons above with respect to claim 15 and claims 2-6, respectively.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries 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 1-6, 8-13, and 15-20 are rejected under 35 U.S.C. 103 as being unpatentable over US 20140278807 A1, hereinafter referenced as BOHACEK, in view of US 20170213266 A1, hereinafter referenced as BALESTRIERI, and further in view of Baldominos Gómez, Alejandro, et al. "AWS PredSpot: Machine learning for predicting the price of spot instances in AWS cloud." (2022), hereinafter referenced as BALDOMINOS.
A computer-implemented method comprising: (BOHACEK, para. 0062: “The user can change TPL on the Cloudamize dashboard and get cloud configuration recommendation that meets the selected TPL for a node and/or for an asset through an iteractive user interface. Specifically, the Cloudamize dashboard provides methods of accepting user input and visuals of predicted performance and/or cost of the recommended or selected Cloud configuration as well as the current configuration. This level of optimization is achievable for a single node and a group of nodes.”)
obtaining a request from at least one user for one or more of hardware components and software components ...; (BOHACEK, para. 0032: “Administrative server 110 performs several functions to help the user select sets of cloud services and understand the relationship between cost and performance for one or a set of cloud-based systems. ... The administrative server provides outputs in terms of a graphical user interface that might be available to users via a web interface. ... The user can adjust the configuration through a graphical interface. Also, the user can adjust the configuration and observe the results in an interactive way.”;
BOHACEK, para. 0037: “The disclosed system helps the cloud services consumer to select cloud services and therefore design their cloud-based systems. Specifically, the disclosed system predicts the performance (in terms of specific metrics) for different cloud services and computes the cost of these configurations. Described embodiments collect a wide range of data that is relevant to the performance, cost, and design options of the cloud consumer's cloud-based system and, based on the collected data, predicts the performance, in terms of specific metrics, of the cloud consumer's system for different set of cloud services and versions of services. Described embodiments also predict the cost of different types of cloud services and versions of services. Thus, described embodiments allow the cloud consumer to explore the relationship between cost and performance and utilize the cost and performance predictions to design the cloud-based system.”;
BOHACEK, para. 0062: “The user can change TPL on the Cloudamize dashboard and get cloud configuration recommendation that meets the selected TPL for a node and/or for an asset through an iteractive user interface. Specifically, the Cloudamize dashboard provides methods of accepting user input and visuals of predicted performance and/or cost of the recommended or selected Cloud configuration as well as the current configuration. This level of optimization is achievable for a single node and a group of nodes.”;
Examiner’s Note: BOHACEK pertains to a system for a user evaluating and subscribing to cloud-based resources (corresponding to recited “one or more of hardware components and software components”))
determining that a value of a first parameter associated with the request varies ...; (BOHACEK, para. 0003: “Also, computing resources can be purchased from a "spot market" where the prices vary according to demand and other factors. Beyond computation, Amazon offers other services such as databases, load balancing, and DNS. The result is that the consumer of Amazon's cloud services has an overwhelming number of options.”;
BOHACEK, para. 0043: “Also, the cloud service provider might provide a spot market, where the prices of services varying according to current supply and demand and other factors chosen by the cloud service provider.”;
Examiner’s Note: BOHACEK teaches that price for cloud services (corresponding to recited “value of a first parameter”) varies over time due to demand and other factors)
predicting, ... the value of the first parameter ...; (BOHACEK, para. 0051: “ Based on the past observations, administrative server 110 might predict the performance and cost of the cloud services and versions of services that might be employed by a cloud system in the future. An example of a cost predictor is a linear extrapolation of observed usage and the predicted costs of the extrapolated usage. More sophisticated predictors consider that the cost of some services vary according to diurnal or seasonal patterns.”)
generating a plurality of configurations for the request ..., wherein each of the plurality of configurations ... comprises: (BOHACEK, para. 0061: “Administrative server 110 determines the system configuration that will meet the user's TPL at a minimal cost from all possible choices available. The recommendation of the cloud configuration is made available to the user on the Cloudamize dashboard. The cloud configuration recommendations are available for an individual node or an asset that meets that selected TPL.”;
Examiner’s Note: BOHACEK teaches that cloud configuration recommendations (plural) from multiple choices are generated for the user’s request)
(i) a second parameter, associated with a type of resource, that is fixed ... (BOHACEK, para. 0043: “Cost metrics include metrics such as the cost of using a cloud service, the cost of using a virtual machine, the cost of using a database, the cost of sending data over the network to a particular destination, the cost of receiving data over the network from a particular destination, the cost of using a type of storage, and the cost of using a cloud service such as a load balancer, preconfigured virtual machine, or DNS service. Other cost metrics include the cost of using different versions of a service such as a faster version, a more reliable version, a version located in different locations, a version that gives different performance, options, or tools, cost changes based on time of use or duration of use, and the like. The cost metrics, like any of the metrics, might vary over time.”
Examiner’s Note: BOHACEK discloses that individual costs (corresponding to recited “second parameter”) are associated with a service type, such as a particular virtual machine, database, type of storage, load balancer, DNS service, etc., where such cost does not have to vary over time, e.g., can be fixed)
(ii) one or more uncertainty criteria corresponding to the second parameter, wherein the one or more uncertainty criteria are based at least in part on at least one location associated with the at least one user and at least one type of the one or more of the hardware components and the software components; (BOHACEK, para. 0043: “Cost metrics include metrics such as the cost of using a cloud service, the cost of using a virtual machine, the cost of using a database, the cost of sending data over the network to a particular destination, the cost of receiving data over the network from a particular destination, the cost of using a type of storage, and the cost of using a cloud service such as a load balancer, preconfigured virtual machine, or DNS service. Other cost metrics include the cost of using different versions of a service such as a faster version, a more reliable version, a version located in different locations, a version that gives different performance, options, or tools, cost changes based on time of use or duration of use, and the like. The cost metrics, like any of the metrics, might vary over time.”
Examiner’s Note: BOHACEK discloses that individual costs vary according to various criteria such as a faster version, more reliable version, or versions located in different locations (corresponding to recited “one or more uncertainty criteria”))
obtaining, from the at least one user, a selection of one of the plurality of configurations; and (BOHACEK, para. 0062: “The user can change TPL on the Cloudamize dashboard and get cloud configuration recommendation that meets the selected TPL for a node and/or for an asset through an iteractive user interface. Specifically, the Cloudamize dashboard provides methods of accepting user input and visuals of predicted performance and/or cost of the recommended or selected Cloud configuration as well as the current configuration. This level of optimization is achievable for a single node and a group of nodes.”;
Examiner’s Note: BOHACEK discloses that a user selects a recommended cloud configuration, which is one of the plurality of configurations evaluated by the Administrative server 110)
initiating one or more automated actions in response to at least one of the one or more uncertainty criteria of the selected configuration being satisfied ...; (BOHACEK, para. 0063: “FIGS. 6-10 show exemplary images of various dashboard views for presenting data to the user. For example, FIG. 6 shows an exemplary dashboard image 600 as rendered on a video display of the administration server 110. Dashboard image 600 is a user interface that enables a user to view a plurality of cloud service performance and cost predictions, confidence and health indicators, and other similar data regarding the status of the cloud system. FIG. 7 shows an exemplary "health" dashboard image 700 as rendered on a video display of the administration server 110. FIG. 8 shows an exemplary "asset optimization" dashboard image 800 as rendered on a video display of the administration server 110. FIG. 9 shows an exemplary "cost computation" dashboard image 900 as rendered on a video display of the administration server 110. FIG. 10 shows an exemplary individual node dashboard image 1000 as rendered on a video display of the administration server 110.”;
Examiner’s Note: BOHACEK discloses providing users with dashboards to monitor their use of the cloud services, where such provisioning of the dashboards corresponds to recited “initiating one or more automated actions”, and where the uncertainty criteria related to costs is satisfied because the user has subscribed to the services, and is now receiving the services and receiving the dashboard updates)
wherein the method is performed by at least one processing device comprising a processor coupled to a memory. (BOHACEK, para. 0073: “Described embodiments might also be embodied in the form of methods and apparatuses for practicing those methods. Described embodiments might also be embodied in the form of program code embodied in non-transitory tangible media, such as magnetic recording media, optical recording media, solid state memory, floppy diskettes, CD-ROMs, hard drives, or any other non-transitory machine-readable storage medium, wherein, when the program code is loaded into and executed by a machine, such as a computer, the machine becomes an apparatus for practicing described embodiments. ... When implemented on a general-purpose processor, the program code segments combine with the processor to provide a unique device that operates analogously to specific logic circuits.”)
However, BOHACEK fails to explicitly teach:
over a particular period of time;
over the particular period of time
using a machine learning-based process
based at least in part on historical time-series data for the first parameter;
based at least in part on the predicted value of the first parameter,
corresponds to a different time interval within the particular period of time
that is fixed over the corresponding time interval and
during the corresponding time interval
However, in a related field of endeavor (purchasing computer resources from a cloud system for deploying an interactive application, see para. 0011), BALESTRIERI teaches the general concept of a user selecting cloud service resources over a particular period of time, and further customizing usage during different intervals of time, such as times of higher or lower demand (e.g., holiday season). In particular, BALESTRIERI teaches:
over a particular period of time; (Examiner’s Note: Addressed together with next limitation)
over the particular period of time (BALESTRIERI, para. 0039: “At operation 308, the interactive purchasing menu interface 202 receives the customer selections and the evaluation engine 204 may generate a price quote based on any combination of the cost information determined at operation 304, the time periods selected by the customer, and other information, such as, for example, information regarding customers willingness to pay for services offered. The price quote may be communicated to the customer at operation 310. If the customer accepts the price quote, method 300 may continue to operation 312.”;
Examiner’s Note: BALESTRIERI discloses that a customer of cloud services can select time periods for such services, and receive quotes for such time periods)
corresponds to a different time interval within the particular period of time (Examiner’s Note: Addressed together with next limitations)
over the corresponding time interval and (Examiner’s Note: Addressed together with next limitation)
during the corresponding time interval (BALESTRIERI, para. 0047: “In some cases, the performance metric and performance variance combination may be expressed as an interval of possible outcomes for each metric, such as response times (e.g., 1-2 seconds, or any other suitable interval of a time period).”;
BALESTRIERI, para. 0049: “Still further, each of the performance offerings 510-512, 520-522 may include a customer modifiable field for specifying a time period in which the corresponding performance metric/performance variance may apply. For example, the performance offering 510 includes the customer modifiable field 530 which allows the customer to specify a timer period in which the customer would like the cloud system to provide service bounded by the performance metric and performance variance, in light of the traffic scenario. Linking a performance offering to a time period may provide a number of advantages. For example, by way of example and not limitation, some applications may have different patterns for request traffic. Such may be seen in mobile games or other applications that have a ramp up period. Different patterns may also be caused by periodic changes in demand experienced by an application. For example, a mobile commerce application may experience seasonal shifts in request patterns based on holidays or a product being in higher demand during a season.”;
Examiner’s Note: BALESTRIERI discloses the concept of a user more granularly specifying particular time periods where different performance metrics may apply, such as periods expected to have increased demand, where these time periods of increased or lower demand correspond to the received “time intervals” within the selected period of time for the services)
The combination of BOHACEK and BALESTRIERI makes obvious:
obtaining a request from at least one user for one or more of hardware components and software components over a particular period of time; (Examiner’s Note: the BOHACEK-BALESTRIERI combination now modifies the system for providing cloud computing services of BOHACEK with the teachings of BALESTRIERI (para. 0039) so that cloud computing subscribers can request services for a particular time period, e.g., to receive a price quote for such period of time)
determining that a value of a first parameter associated with the request varies over the particular period of time; (Examiner’s Note: the BOHACEK-BALESTRIERI combination now modifies the system for providing cloud computing services of BOHACEK with the teachings of BALESTRIERI (para. 0039) so that cloud computing subscribers can request services for a particular time period, e.g., to receive a price quote for such period of time, where the price will vary over the period of time as taught by BOHACEK, such as busier time periods with higher demand will cost more)
predicting, ... the value of the first parameter over the particular period of time; (Examiner’s Note: the BOHACEK-BALESTRIERI combination now modifies the system for providing cloud computing services of BOHACEK with the teachings of BALESTRIERI (para. 0039) so that cloud computing subscribers can request services for a particular time period, e.g., to receive a price quote for such period of time, where the price is predicted over the period of time as in BOHACEK)
generating a plurality of configurations for the request based at least in part on the predicted value of the first parameter, wherein each of the plurality of configurations corresponds to a different time interval within the particular period of time and comprises: (Examiner’s Note: the BOHACEK-BALESTRIERI combination now modifies the system for providing cloud computing services of BOHACEK with the teachings of BALESTRIERI (paras. 0047 and 0049) so that different cloud computing configurations can be generated for different time periods, such as additional processing power and virtual machines being provided during a higher demand season, such as the holidays, as taught by BALESTRIERI)
(i) a second parameter, associated with a type of resource, that is fixed over the corresponding time interval; (Examiner’s Note: the BOHACEK-BALESTRIERI combination now modifies the system for providing cloud computing services of BOHACEK with the teachings of BALESTRIERI (paras. 0047 and 0049) so that different cloud computing configurations can be generated for different time periods, such as additional processing power and virtual machines being provided during a higher demand season, such as the holidays, as taught by BALESTRIERI, where a maximum charge is assessed for such interval (e.g., the maximum charge is a fixed charge))
initiating one or more automated actions in response to at least one of the one or more uncertainty criteria of the selected configuration being satisfied during the corresponding time interval; (Examiner’s Note: the BOHACEK-BALESTRIERI combination now modifies the system for providing cloud computing services of BOHACEK with the teachings of BALESTRIERI (paras. 0047 and 0049) so that different cloud computing configurations can be generated for different time periods, such as additional processing power and virtual machines being provided during a higher demand season, such as the holidays, as taught by BALESTRIERI, where a maximum charge is assessed for such interval (e.g., the maximum charge is a fixed charge), and such maximum charge satisfies the uncertainty criteria)
Before the effective filing date of the present application, it would have been obvious to one of ordinary skill in the art to modify the system for providing cloud computing services of BOHACEK with the teachings of BALESTRIERI as explained above. As disclosed by BALESTRIERI, one of ordinary skill would have been motivated to do so in order to provide price quotes for a particular period of time, to provide potential subscribers with additional information when making a decision as to whether to accept a quote or not. (para. 0024).
However, BOHACEK and BALESTRIERI fail to explicitly teach:
using a machine learning-based process
based at least in part on historical time-series data for the first parameter;
based at least in part on the predicted value of the first parameter,
However, in a related field of endeavor (predicting spot prices for Amazon web services, see p. 65, section I), BALDOMINOS teaches:
using a machine learning-based process (BALDOMINOS, p. 69, section VI: “In this section we will explain the machine learning techniques used to train the regression models for instance price prediction.”)
based at least in part on historical time-series data for the first parameter; (BALDOMINOS, p. 68, section IV.B.2: “Spot Price Archive is a historic data archive of EC2 spot instances prices provided by Western Sydney University, Australia. ... This dataset is much more complete regarding the length of time series, since it provides data for all years comprised between 2009 and 2016.”)
based at least in part on the predicted value of the first parameter, (BALDOMINOS, p. 65, section I: “In this paper, we aim at designing and developing a system able to predict the future price of a spot instance in EC2, with the final objective of easing the optimization of the bidding procedure. To do so, we will rely on historic information in the spot instances prices”)
The combination of BOHACEK, BALESTRIERI, and BALDOMINOS makes obvious:
predicting, using a machine learning-based process, the value of the first parameter over the particular period of time based at least in part on historical time-series data for the first parameter; (Examiner’s Note: the BOHACEK-BALESTRIERI-BALDOMINOS combination now modifies the system for providing cloud computing services of BOHACEK with the teachings of BALESTRIERI (paras. 0047 and 0049) so that different cloud computing configurations can be generated for different time periods, together with predicted prices that have been predicted by a machine learning model trained on historical time series data as taught by BALDOMINOS (p. 68, section IV.B.2 and p. 69, section VI)).
generating a plurality of configurations for the request, based at least in part on the predicted value of the first parameter, (Examiner’s Note: the BOHACEK-BALESTRIERI-BALDOMINOS combination now modifies the system for providing cloud computing services of BOHACEK with the teachings of BALDOMINOS (p. 65, section I) so that predicted prices are taken into account when generating possible configurations)
Before the effective filing date of the present application, it would have been obvious to one of ordinary skill in the art to modify the system for providing cloud computing services of BOHACEK with the teachings of BALESTRIERI and BALDOMINOS as explained above. As disclosed by BALDOMINOS, one of ordinary skill would have been motivated to do so in order to enable “companies of different sizes to work on optimal bidding strategies that can optimize economic resources spent on cloud computing infrastructure.” (p. 66, section III).
Regarding Claim 2
BOHACEK, BALESTRIERI, and BALDOMINOS disclose the method of claim 1 as explained above. BOHACEK further teaches:
wherein the one or more automated actions comprises: generating one or more additional configurations for the request ... in response to the at least one of the one or more uncertainty criteria of the selected configuration being satisfied. (BOHACEK, para. 0034: “Accordingly, each performance offering may include a combination of average (or maximum, or any other suitable statistic) response-time speed, expected traffic level, and a maximum traffic which the cloud provider can meet the performance metric in light of the performance variance. In some cases, the evaluation engine 204 may filter out performance offerings that do not bear a significant difference in costs from each other. This type of filtering may be performed based on a cost threshold that is definable by a cloud provider.”;
BOHACEK, para. 0061: “Administrative server 110 determines the system configuration that will meet the user's TPL at a minimal cost from all possible choices available. The recommendation of the cloud configuration is made available to the user on the Cloudamize dashboard. The cloud configuration recommendations are available for an individual node or an asset that meets that selected TPL.”;
Examiner’s Note: BOHACEK teaches that cloud configuration recommendations are system-generated, where such recommendations are only provided if the pricing meets a cost threshold (corresponding to recited “one or more uncertainty criteria of the selected configuration being satisfied”)
However, BOHACEK fails to explicitly teach:
for a remaining portion of the particular period of time
However, in a related field of endeavor, BALESTRIERI teaches and makes obvious:
wherein the one or more automated actions comprises: generating one or more additional configurations for the request for a remaining portion of the particular period of time in response to the at least one of the one or more uncertainty criteria of the selected configuration being satisfied. (BALESTRIERI, para. 0047: “In some cases, the performance metric and performance variance combination may be expressed as an interval of possible outcomes for each metric, such as response times (e.g., 1-2 seconds, or any other suitable interval of a time period).”;
BALESTRIERI, para. 0049: “Still further, each of the performance offerings 510-512, 520-522 may include a customer modifiable field for specifying a time period in which the corresponding performance metric/performance variance may apply. For example, the performance offering 510 includes the customer modifiable field 530 which allows the customer to specify a timer period in which the customer would like the cloud system to provide service bounded by the performance metric and performance variance, in light of the traffic scenario. Linking a performance offering to a time period may provide a number of advantages. For example, by way of example and not limitation, some applications may have different patterns for request traffic. Such may be seen in mobile games or other applications that have a ramp up period. Different patterns may also be caused by periodic changes in demand experienced by an application. For example, a mobile commerce application may experience seasonal shifts in request patterns based on holidays or a product being in higher demand during a season.”;
Examiner’s Note: BALESTRIERI discloses the concept of a user more granularly specifying particular time periods where different performance metrics may apply, such as periods expected to have increased demand, where these time periods of increased or lower demand correspond to the received “time intervals” within the selected period of time for the services; the BOHACEK-BALESTRIERI-BALDOMINOS combination now modifies the cloud service providing system of BOHACEK such that a price quote can be tailored for a period of less demand (corresponding to a remaining portion of the particular period of time) as taught by BALESTRIERI)
Before the effective filing date of the present application, it would have been obvious to one of ordinary skill in the art to modify the system for providing cloud computing services of BOHACEK with the teachings of BALESTRIERI and BALDOMINOS as explained above. As disclosed by BALESTRIERI, one of ordinary skill would have been motivated to do so in order to provide price quotes for a particular period of time, to provide potential subscribers with additional information when making a decision as to whether to accept a quote or not. (para. 0024).
Regarding Claim 3
BOHACEK, BALESTRIERI, and BALDOMINOS disclose the method of claim 1 as explained above. BOHACEK further teaches:
wherein the one or more automated actions comprises at least one of: sending an alert to the at least one user; and adjusting at least a portion of the one or more of the hardware components and the software components associated with the request. (BOHACEK, para. 0064: “The alert message might typically include generating an alert message on the dashboard display, or notifying a designated user, for example, by email, automated call, text message, or a combination thereof”;
Examiner’s Note: BOHACEK discloses sending alerts to subscribers via a dashboard, and the automatic provisioning of the dashboards can provide such an automatic alert to the user that the provisioned cloud services are operational)
Regarding Claim 4
BOHACEK, BALESTRIERI, and BALDOMINOS disclose the method of claim 1 as explained above. However, BOHACEK and BALESTRIERI fail to explicitly teach:
obtaining additional time-series data for the first parameter over at least a portion of the time interval corresponding to the selected configuration; and
updating the predicted value of the first parameter based at least in part on the additional time-series data.
However, in a related field of endeavor (predicting spot prices for Amazon web services, see p. 65, section I), BALDOMINOS teaches and makes obvious:
obtaining additional time-series data for the first parameter over at least a portion of the time interval corresponding to the selected configuration; and (BALDOMINOS, p. 70, section VI.A: “The second approach, which we have deemed more interesting in this work, is to introduce as input the parameters that were previously described: year, month, day of month, day of week and hour, as well as availability zone, instance type and operating system. In this case, knowing the values in the time series immediately before the desired prediction time instant is irrelevant, since they are not used for the prediction. As a consequence of this, the approach has the advantage that the quality of the prediction is not affected by how far in the future the desired value is”;
Examiner’s Note: BALDOMINOS teaches the ability to predict spot prices at future points in time, where predictions over a period of time correspond to “obtaining additional time-series data for the first parameter”; the BOHACEK-BALESTRIERI-BALDOMINOS combination now modifies the cloud service providing system of BOHACEK such that predictions for future spot prices for a particular time period are predicted as in BALDOMINOS)
updating the predicted value of the first parameter based at least in part on the additional time-series data. (BALDOMINOS, p. 70, section VI.A: “The second approach, which we have deemed more interesting in this work, is to introduce as input the parameters that were previously described: year, month, day of month, day of week and hour, as well as availability zone, instance type and operating system. In this case, knowing the values in the time series immediately before the desired prediction time instant is irrelevant, since they are not used for the prediction. As a consequence of this, the approach has the advantage that the quality of the prediction is not affected by how far in the future the desired value is”;
Examiner’s Note: BALDOMINOS teaches the ability to predict spot prices at future points in time, where predictions over a period of time correspond to “obtaining additional time-series data for the first parameter”; the BOHACEK-BALESTRIERI-BALDOMINOS combination now modifies the cloud service providing system of BOHACEK such that predictions for future spot prices for a particular time period are predicted as in BALDOMINOS and are used to update the predicted price of services)
Before the effective filing date of the present application, it would have been obvious to one of ordinary skill in the art to modify the system for providing cloud computing services of BOHACEK with the teachings of BALESTRIERI and BALDOMINOS as explained above. As disclosed by BALDOMINOS, one of ordinary skill would have been motivated to do so in order to enable “companies of different sizes to work on optimal bidding strategies that can optimize economic resources spent on cloud computing infrastructure.” (p. 66, section III).
Regarding Claim 5
BOHACEK, BALESTRIERI, and BALDOMINOS disclose the method of claim 1 as explained above. However, BOHACEK and BALESTRIERI fail to explicitly teach:
wherein the machine learning-based process comprises at least one of a linear regression model and an autoregressive model.
However, in a related field of endeavor (predicting spot prices for Amazon web services, see p. 65, section I), BALDOMINOS teaches and makes obvious:
wherein the machine learning-based process comprises at least one of a linear regression model and an autoregressive model (BALDOMINOS, p. 70, section VI.B: “Because of this, we will test different machine learning techniques with each type of instance. In particular, these techniques are the following: • Linear regression: a standard algorithm that will learn a hyperplane fitting input data with the least square error. • Linear regression with ridge regularization: same as the previous one yet imposing a penalty in the size of the regression coefficients. • Linear regression with lasso regularization: same as the first one, but preferring solutions with fewer parameters.”;
Examiner’s Note: the BOHACEK-BALESTRIERI-BALDOMINOS combination now modifies the system for providing cloud computing services of BOHACEK with the teachings of BALDOMINOS so that the linear regression machine learning model of BALDOMINOS is used to predict spot prices for cloud computing services)
Before the effective filing date of the present application, it would have been obvious to one of ordinary skill in the art to modify the system for providing cloud computing services of BOHACEK with the teachings of BALESTRIERI and BALDOMINOS as explained above. As disclosed by BALDOMINOS, one of ordinary skill would have been motivated to do so in order to enable “companies of different sizes to work on optimal bidding strategies that can optimize economic resources spent on cloud computing infrastructure.” (p. 66, section III).
Regarding Claim 6
BOHACEK, BALESTRIERI, and BALDOMINOS disclose the method of claim 1 as explained above. However, BOHACEK and BALESTRIERI fail to explicitly teach:
wherein the at least one location is located within a first country, and wherein at least one second location associated with an entity providing the one or more of the hardware components and the software components is located within a different, second country.
However, in a related field of endeavor (predicting spot prices for Amazon web services, see p. 65, section I), BALDOMINOS teaches and makes obvious:
wherein the at least one location is located within a first country, and wherein at least one second location associated with an entity providing the one or more of the hardware components and the software components is located within a different, second country. (BALDOMINOS, p. 66, section II: “Region: it refers to the Amazon datacenter where the instance will be launched. Some examples of regions are the following: North Virginia (us-east-1), Ohio (us-east-2), North California (us-west-1), Canada (ca-central-1), Ireland (eu-west-1), etc.”;
Examiner’s Note: the BOHACEK-BALESTRIERI-BALDOMINOS combination now modifies the system for providing cloud computing services of BOHACEK with the teachings of BALDOMINOS so that user (at the first location) can be in a country that does not have a datacenter, and therefore the hardware and software will be provisioned from the nearest datacenter in a different country, for example, the first location can be the Faroe Islands and the second location can be Ireland)
Before the effective filing date of the present application, it would have been obvious to one of ordinary skill in the art to modify the system for providing cloud computing services of BOHACEK with the teachings of BALESTRIERI and BALDOMINOS as explained above. As disclosed by BALDOMINOS, one of ordinary skill would have been motivated to do so in order to enable “companies of different sizes to work on optimal bidding strategies that can optimize economic resources spent on cloud computing infrastructure.” (p. 66, section III).
Regarding Claim 8
BOHACEK teaches:
A non-transitory processor-readable storage medium having stored therein program code of one or more software programs, wherein the program code when executed by at least one processing device causes the at least one processing device: (BOHACEK, para. 0073: “Described embodiments might also be embodied in the form of methods and apparatuses for practicing those methods. Described embodiments might also be embodied in the form of program code embodied in non-transitory tangible media, such as magnetic recording media, optical recording media, solid state memory, floppy diskettes, CD-ROMs, hard drives, or any other non-transitory machine-readable storage medium, wherein, when the program code is loaded into and executed by a machine, such as a computer, the machine becomes an apparatus for practicing described embodiments. ... When implemented on a general-purpose processor, the program code segments combine with the processor to provide a unique device that operates analogously to specific logic circuits.”)
The remaining limitations of claim 8 correspond to the method of claim 1, and claim 8 is therefore rejected for the same reasons explained with respect to claim 1.
Claim 9 depends from claim 8 and claims a non-transitory processor-readable storage medium that corresponds to the method of claim 2, and is therefore rejected for the same reasons explained above with respect to claims 2 and 8.
Claim 10 depends from claim 8 and claims a non-transitory processor-readable storage medium that corresponds to the method of claim 3, and is therefore rejected for the same reasons explained above with respect to claims 3 and 8.
Claim 11 depends from claim 8 and claims a non-transitory processor-readable storage medium that corresponds to the method of claim 4, and is therefore rejected for the same reasons explained above with respect to claims 4 and 8.
Claim 12 depends from claim 8 and claims a non-transitory processor-readable storage medium that corresponds to the method of claim 5, and is therefore rejected for the same reasons explained above with respect to claims 5 and 8.
Claim 13 depends from claim 8 and claims a non-transitory processor-readable storage medium that corresponds to the method of claim 6, and is therefore rejected for the same reasons explained above with respect to claims 6 and 8.
Regarding Claim 15
BOHACEK teaches:
An apparatus comprising: at least one processing device comprising a processor coupled to a memory; the at least one processing device being configured: (BOHACEK, para. 0073: “Described embodiments might also be embodied in the form of methods and apparatuses for practicing those methods. Described embodiments might also be embodied in the form of program code embodied in non-transitory tangible media, such as magnetic recording media, optical recording media, solid state memory, floppy diskettes, CD-ROMs, hard drives, or any other non-transitory machine-readable storage medium, wherein, when the program code is loaded into and executed by a machine, such as a computer, the machine becomes an apparatus for practicing described embodiments. ... When implemented on a general-purpose processor, the program code segments combine with the processor to provide a unique device that operates analogously to specific logic circuits.”)
The remaining limitations of claim 15 correspond to the method of claim 1, and claim 15 is therefore rejected for the same reasons explained with respect to claim 1.
Claim 16 depends from claim 15 and claims an apparatus that corresponds to the method of claim 2, and is therefore rejected for the same reasons explained above with respect to claims 2 and 15.
Claim 17 depends from claim 15 and claims an apparatus that corresponds to the method of claim 3, and is therefore rejected for the same reasons explained above with respect to claims 3 and 15.
Claim 18 depends from claim 15 and claims an apparatus that corresponds to the method of claim 4, and is therefore rejected for the same reasons explained above with respect to claims 4 and 15.
Claim 19 depends from claim 15 and claims an apparatus that corresponds to the method of claim 5, and is therefore rejected for the same reasons explained above with respect to claims 5 and 15.
Claim 20 depends from claim 15 and claims an apparatus that corresponds to the method of claim 6, and is therefore rejected for the same reasons explained above with respect to claims 6 and 15.
Claims 7 and 14 are rejected under 35 U.S.C. 103 as being unpatentable over BOHACEK, BALESTRIERI, and BALDOMINOS in view of US 20070156529 A1, hereinafter referenced as WALKER.
Regarding Claim 7
BOHACEK, BALESTRIERI, and BALDOMINOS disclose the method of claim 1 as explained above. However, BOHACEK, BALESTRIERI, and BALDOMINOS fail to explicitly teach:
wherein the one or more uncertainty criteria are further based on a type of the at least one user.
However, in a related field of endeavor (sales of products or services, see para. 0004), WALKER teaches and makes obvious:
wherein the one or more uncertainty criteria are further based on a type of the at least one user. (WALKER, para. 0010: “Accordingly, it is an object of the present invention to provide a method and apparatus for allowing a customer to indicate his or her brand indifference by selecting, indicating or modifying a product or service category, and then receive a benefit for purchasing one or more products or services chosen by a third party or controller from within the selected, indicated or modified product or service category. This method and apparatus is particularly useful in differentiating between brand-sensitive or brand-loyal customers and brand-indifferent customers, and allows manufacturers to price-discriminate between these two types of customers while providing an opportunity to capture brand-indifferent customers or entice such brand-indifferent customers to try selected products and/or services. In some embodiments the method and apparatus will allow a customer to designate or select multiple product and/or service categories, thereby creating a "shopping list" of product and/or service categories.”;
Examiner’s Note: the BOHACEK-BALESTRIERI-BALDOMINOS-WALKER combination now modifies the system for providing cloud computing services of BOHACEK with the teachings of WALKER so that the price uncertainty is based on the type of customer as in WALKER, for example, customers that want Windows vs. Linux machines)
Before the effective filing date of the present application, it would have been obvious to one of ordinary skill in the art to modify the system for providing cloud computing services of BOHACEK with the teachings of BALESTRIERI, BALDOMINOS, and WALKER as explained above. As disclosed by WALKER, one of ordinary skill would have been motivated to do so in order to permit “manufacturers to price-discriminate between these two types of customers while providing an opportunity to capture brand-indifferent customers or entice such brand-indifferent customers to try selected products and/or services.” (para. 0010).
Claim 14 depends from claim 8 and claims a non-transitory processor-readable storage medium that corresponds to the method of claim 7, and is therefore rejected for the same reasons explained above with respect to claims 7 and 8.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
US 20220405775 A1 (Siebel). “This disclosure is generally directed to machine learning and other artificial intelligence (AI) systems. More specifically, this disclosure describes methods, processes, and systems to deploy an AI-based customer relationship management (CRM) system using a model-driven software architecture, such as one that uses internal data sources and/or exogenous data sources.” (para. 0002).
US 20200410418 A1 (Martynov). “Disclosed herein are methods and systems for assisting an enterprise user or customer with assessing and evaluating different cloud service providers to which backups of the customer enterprise may be migrated to. In a specific embodiment, a utility is provided that compares and rates cloud backup service options based on the enterprise's prior data protection activities and cloud backup chargeback policies.” (para. 0018).
Any inquiry concerning this communication or earlier communications from the examiner should be directed to MICHAEL C LEE whose telephone number is (571)272-4933. The examiner can normally be reached M-F 12:00 pm - 8:00 pm ET.
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/MICHAEL C. LEE/Examiner, Art Unit 2128