DETAILED ACTION
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
The text of those sections of Title 35, U.S. Code not included in this action can be found in a prior Office action.
This Action is in response to communications filed 12/29/2025.
Claims 1-3, 9, 11-13, and 19 have been amended.
Claims 1-20 are pending.
Claims 1-20 are rejected.
Response to Amendment
In the Remarks filed 12/29/2025, Applicant has amended:
The language of claims 3 and 13 to distinguish that the steps further comprise the following limitations which are performed as independent steps. The Examiner therefore withdraws the corresponding 112(b) rejections for claims 3-6 and 13-16 made in the Office action dated 09/29/2025. The Examiner notes that the Applicant did not address the issue as recited in claims 10 and 20 and therefore those rejections remain and are reiterated herein.
Response to Arguments
In Remarks filed on 12/29/2025, Applicant substantially argues:
On Pages 7-8, the applied references, including Roberts, Dai and Rohde, fail to disclose the amended limitations of claim 1, and similarly amended claim 11, involving the access policy as being used in a zero trust network access (ZTNA) service and wherein the policy further identifies user-group access to associated app-segments via one or more ports. Specifically, the references do not address the policy usage specifying the user access in a ZTNA service as now claimed. Applicant’s arguments filed have been fully considered but they are moot in view of the current rejection made in response to Applicant’s amendments.
On Pages 8-9, the applied references, including Roberts, Dai and Rohde, fail to disclose the amended limitations of claim 2, and similarly amended claim 12, involving the machine learning model determining one or more sequential patterns of application access within a time period. Specifically, the references do not address the identifying access patterns by users to the plurality of applications. Applicant’s arguments filed have been fully considered but they are moot in view of the current rejection made in response to Applicant’s amendments.
On Pages 9-10, the applied references, including Roberts, Dai and Rohde, fail to disclose the amended limitations of claim 9, and similarly amended claim 19, involving the memory usage estimation as estimating a peak memory usage based on at least a number of unique users, unique applications, and transactions in the log data. Specifically, the references do not address the peak memory estimation involving the elements as now claimed. Applicant’s arguments filed have been fully considered but they are moot in view of the current rejection made in response to Applicant’s amendments.
All arguments by the applicant are believed to be covered in the body of the office action; thus, this action constitutes a complete response to the issues raised in the remarks dated December 29, 2025.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 10 and 20 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor, or for pre-AIA the applicant regards as the invention.
Claim 10 currently recites “wherein the steps comprise performing one or more memory surge protection processes…” As it is currently recited, it is unclear if the step is considered to be an independent step or an additional step to one of the previously recited steps. It is suggested to amend the language to “wherein the steps further comprise:” to more clearly indicate the step as independent if intended. For purposes of the current action, the step is interpreted as being independent from the other recited steps.
Claim 20 recites the same issue identified above
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 of this title, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied for establishing a background for determining obviousness under 35 U.S.C. 103(a) 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, 8, 10-11, 18, and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Roberts (US 2024/0069783) in view of Dai et al. (US 2022/0188016) and further in view of Rohde et al. (US 2020/0341779) and still further in view of Weingarten et al. (US 2019/0052659).
Regarding claim 1, Roberts discloses, in the italicized portions, a non-transitory computer-readable storage medium having computer readable code stored thereon for programming at least one processor to perform steps of: obtaining log data for a plurality of users of an enterprise where the log data relates to usage of a plurality of applications by the plurality of users and user metadata; determining a memory usage estimation based on the log data ([0092] Computer readable medium executing instructions; [0046-47] Scheduling component 123 obtaining log data reflecting memory usage statistic and calculating prediction data based on the memory usage statistic); determining i) app-segments that are groupings of application of the plurality of applications and ii) user-groups that are groupings of users of the plurality of users, based on the log data and the memory usage estimation; and providing access policy of the plurality of applications based on the user-groups and the app-segments ([0060] Using prediction data to implement scheduling), wherein the access policy is for a zero trust network access service and specifies which user-groups are allowed to access which app-segments on one or more ports. Herein Roberts discloses the process for analyzing log data to retrieve historical access information to memory in the storage system and deriving prediction data based on the log data to then determine future scheduled utilization of the storage system. Additionally identified in Paragraph [0088] of Roberts, the system may be implemented in a networked environment wherein the system functions as a client, peer or server. Roberts does not explicitly disclose determining groupings of applications and users based on the log data and memory usage estimation and the access policy is provided in view of the groupings which is used to specify access to a ZTNA and which user-groups access which app-segments on particular one or more ports. Regarding the application grouping, Dai discloses in Paragraph [0031] “For example, the memory power controlling circuitry can group different applications and/or categories of applications based on their latency sensitivity ... In some examples, the memory power controlling circuitry determines additional or alternative thresholds associated with other statistics corresponding to the memory usage of the user profile, such as power consumed by the memory at per channel granularity, a user's preference between power and performance, etc.” Herein Dai discloses categorizing applications into groups by the memory power controlling circuitry based on statistics including memory usage. In this manner, applications are organized in effort to improve hashing policies based on memory capacity usage. It would be obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to perform the application grouping as performed by Dai to anticipate memory capacity utilization when performing the scheduling as presented in Roberts in order to improve memory utilization as based on user performance (Dai [0028]). Dai does not explicitly discloses grouping users based on log data and memory usage estimation. Regarding this aspect of the limitation, Rohde discloses in Paragraph [0114] “Correlation module 1030, in the illustrated embodiment, generates one or more group(s) of users based on the correlation data. Note that information specifying a group of users may specify actual identifiers for specific users in the group or may specify attribute rules used to classify users into groups. In some embodiments, correlation module 1030 assigns a user interface design type to a group of users based on the correlation.” Herein Rohde identifies classifying users into groups based on retrieved interaction metric data and calculated correlations based on the metric data. The system uses the defined user groups to implement management operations. It would be obvious to one of ordinary skill in the art before the effective filing date of the claimed invention that the metrics used to classify users in Rohde may include the log and prediction data as recited in Roberts as Rohde is directed towards evaluating user interactions with the system and optimizing performance to service the user (Rohde Abstract). In combination, the scheduling as performed in the prior references cited may utilize the identified metrics in order to provide access policy based on the defined user-groups and app-segments. Regarding the access policy for the ZTNA and the policy specifying port access to app-segments for user-groups, Weingarten discloses in Paragraphs [0065] and [0071] “[0065] In some embodiments, the systems, devices, and methods disclosed herein can be designed around the concept of creating an initial “zero-trust” security architecture that can continuously collect endpoint data at various level of granularity within the system to persistently verify and establish the credibility of individual endpoints and the network as a whole... In some embodiments, this verification is established through artificial-intelligence (AI) driven analysis of individual endpoint, group, and network-wide usage patterns. In some embodiments, this analysis is centered on continuous monitoring and collection of data from endpoint devices, and the determination of a baseline usage of a user, endpoint device, group, subnetwork, network segment, or the network as a whole. Once established, baseline usage can be continuously updated and compared to current usage in order to authenticate users and endpoint devices on a persistent basis. [0071] In some embodiments, another central function of the system may be the creation, generation, and implementation of access policies, which can be based around determining a baseline usage or expectation of usage. In some embodiments, the generated access policies provide an outline for the endpoints, services, servers, programs, and the like, that any given user on any given endpoint can access at any given time.” Herein Weingarten discloses techniques for implementing access policies in a zero trust network architecture (ZTNA) based on monitored and estimated metrics. Furthermore, Weingarten discloses in Paragraph [0165] that user traffic may be further controlled on a port by port basis. In this manner, it would be obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the network access and policy implementation on a continuous basis to provide a ZTNA as discussed by Weingarten in combination with the techniques provided in Roberts, Dai and Rohde for grouping applications and users based on monitored metrics in order to enforce and verify network structure usage and access policy on the continual basis in response to network utilization (Weingarten [0065]). Roberts, Dai, Rohde, and Weingarten are analogous art because they are from the same field of endeavor of implementing system management techniques. The Examiner notes not all cited portions of the references are reproduced herein for brevity.
Regarding claim 8, Dai and Rohde further disclose the non-transitory computer-readable storage medium of claim 1, wherein the determining app-segments and user-groups is performed via one or more selective algorithms (Dai [0031] and Rohde [0107]). Herein both Dai and Rohde disclose employing selection algorithms to group applications and users.
Regarding claim 10, Roberts further discloses the non-transitory computer-readable storage medium of claim 1, wherein the steps comprise: performing one or more memory surge protection processes based on the memory usage estimation ([0062]). Herein Roberts disclose performing reactive operations based on memory usage data and prediction data. The discloses techniques included in the scheduling are interpreted as analogous to memory surge protection processes as they aim to optimize memory utilization based on both historical and predict memory statistics.
Regarding claim 11, Roberts discloses, in the italicized portions, a method comprising steps of: obtaining log data for a plurality of users of an enterprise where the log data relates to usage of a plurality of applications by the plurality of users and user metadata ([0046-47] Scheduling component 123 obtaining log data reflecting memory usage statistic and calculating prediction data based on the memory usage statistic); determining a memory usage estimation based on the log data; determining i) app-segments that are groupings of application of the plurality of applications and ii) user-groups that are groupings of users of the plurality of users, based on the log data and the memory usage estimation; and providing access policy of the plurality of applications based on the user-groups and the app-segments ([0060] Using prediction data to implement scheduling), wherein the access policy is for a zero trust network access service and specifies which user-groups are allowed to access which app-segments on one or more ports. Herein Roberts discloses the process for analyzing log data to retrieve historical access information to memory in the storage system and deriving prediction data based on the log data to then determine future scheduled utilization of the storage system. Roberts does not explicitly disclose determining groupings of applications and users based on the log data and memory usage estimation and the access policy is provided in view of the groupings which is used to specify access to a ZTNA and which user-groups access which app-segments on particular one or more ports. Regarding the application grouping, Dai discloses in Paragraph [0031] categorizing applications into groups by the memory power controlling circuitry based on statistics including memory usage. In this manner, applications are organized in effort to improve hashing policies based on memory capacity usage. Dai does not explicitly discloses grouping users based on log data and memory usage estimation. Regarding this aspect of the limitation, Rohde discloses in Paragraph [0114] classifying users into groups based on retrieved interaction metric data and calculated correlations based on the metric data. The system uses the defined user groups to implement management operations. Regarding the access policy for the ZTNA and the policy specifying port access to app-segments for user-groups, Weingarten discloses in Paragraphs [0065] and [0071] techniques for implementing access policies in a zero trust network architecture (ZTNA) based on monitored and estimated metrics. Furthermore, Weingarten discloses in Paragraph [0165] that user traffic may be further controlled on a port by port basis. Claim 11 is rejected on a similar basis as claim 1.
Regarding claim 18, Dai and Rohde further disclose the method of claim 11, wherein the determining app-segments and user-groups is performed via one or more selective algorithms (Dai [0031] and Rohde [0107]). Claim 18 is rejected on a similar basis as claim 8.
Regarding claim 20, Roberts further discloses the method of claim 11, wherein the steps comprise: performing one or more memory surge protection processes based on the memory usage estimation ([0062]). Claim 20 is rejected on a similar basis as claim 10.
Claims 2 and 12 are rejected under 35 U.S.C. 103 as being unpatentable over Roberts in view of Dai and further in view of Rohde and still in further view of Weingarten and Keen et al. (US 2020/0159894).
Regarding claim 2, Dai and Rohde in combination further disclose, the italicized portions, the non-transitory computer-readable storage medium of claim 1, wherein the determining app-segments and user-groups is performed via a machine learning model, and wherein an input to the machine learning model is based on the log data and the memory usage estimation (Dai [0169] and Rohde [0070]), wherein the machine learning model is configured to analyze the log data to determine one or more sequential patterns of application access that each include a sequence of accessing a plurality of application with a time period, and wherein the determining the app-segments and the user-groups is further based on the one or more sequential patterns of application access. Herein Dai and Rohde disclose implementation of techniques utilizing machine learning models. Roberts, Dai, Rohde, and Weingarten do not explicitly disclose analyzing the log data for sequential patterns of application access within a time period by the user-groups. Regarding this aspect of the limitation, Keen discloses in Paragraphs [0028-30] “[0028] An embodiment stores the data of the application usage, application switching and grouping, and contextual factors associated with a user's application usage in a usage repository. An embodiment stores the usage repository on the platform on which the monitored applications execute, in a remote location accessible using a network, or partially in both locations. [0029] An embodiment analyzes the data in the usage repository to derive application usage patterns associated with particular user contexts. To perform this analysis, an embodiment uses any pattern recognition technique, for example a clustering analysis or an association analysis. [0030] One type of usage pattern identifies a set of applications that a user uses concurrently, sequentially, or in some combined manner during a user activity. An embodiment uses this type of usage pattern to help identify which other applications a user might want to use concurrently.” Herein Keen discloses using analysis techniques to determine sequential application usage by a user based on monitored metrics. In this manner, it would be obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the application pattern usage as discussed in Keen with the teachings of Roberts, Dai, Rohde and Weingarten as Dai, Rohde, and Weingarten presently discuss managing and implementing access policies based on monitored usage and applying corresponding policies to groups of users for accessing particular applications in order to improve memory consumption. Roberts, Dai, Rohde, Weingarten and Keen are analogous art because they are from the same field of endeavor of implementing system management techniques.
Regarding claim 12, Dai and Rohde in combination further disclose, the italicized portions, the method of claim 11, wherein the determining app-segments and user-groups is performed via a machine learning model, and wherein an input to the machine learning model is based on the log data and the memory usage estimation (Dai [0169] and Rohde [0070]), wherein the machine learning model is configured to analyze the log data to determine one or more sequential patterns of application access that each include a sequence of accessing a plurality of application with a time period, and wherein the determining the app-segments and the user-groups is further based on the one or more sequential patterns of application access. Roberts, Dai, Rohde, and Weingarten do not explicitly disclose analyzing the log data for sequential patterns of application access within a time period by the user-groups. Regarding this aspect of the limitation, Keen discloses in Paragraphs [0028-30] using analysis techniques to determine sequential application usage by a user based on monitored metrics. Claim 12 is rejected on a similar basis as claim 2.
Claims 3-5 and 13-15 are rejected under 35 U.S.C. 103 as being unpatentable over Roberts in view of Dai and further in view of Rohde and still in further view of Weingarten and Flynn et al. (US 2012/0210041).
Regarding claim 3, Roberts, Dai, Rohde, and Weingarten do not explicitly disclose the non-transitory computer-readable storage medium of claim 1, wherein the steps further comprise performing purging of entries in the log data. Regarding this limitation, Flynn discloses in Paragraph [0084] that a log-based data structure may have data evicted based on various conditions and the entries wherein the data was stored is reused. In this manner, it would be obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to purge, or otherwise evict, entries in order to make space for new entries. Roberts, Dai, Rohde, Weingarten, and Flynn are analogous art because they are from the same field of endeavor of implementing system management techniques.
Regarding claim 4, Flynn further discloses the non-transitory computer-readable storage medium of claim 3, wherein the purging comprises purging entries associated with rarely used applications, and wherein rarely used applications are identified based on a transaction number threshold ([0280] Access frequency of entries compared to a threshold). Herein Flynn discloses data is evicted based on comparing the access frequency to a predefined threshold which is determined to be analogous to a transaction number threshold. It would be obvious to one of ordinary skill in the art that based on the access frequency, applications may be considered as rarely used compared to a threshold.
Regarding claim 5, Flynn further discloses the non-transitory computer-readable storage medium of claim 3, wherein the purging comprises purging entries associated with less interactive users, and wherein less interactive users are identified based on a transaction number threshold ([0280]). Claim 5 is rejected on a similar basis as claim 4. In this case, the access frequency may be representative of less interactive users based on the threshold.
Regarding claim 13, Roberts, Dai, Rohde, and Weingarten do not explicitly disclose the method of claim 11, wherein the steps further comprise performing purging of entries in the log data. Regarding this limitation, Flynn discloses in Paragraph [0084] that a log-based data structure may have data evicted based on various conditions and the entries wherein the data was stored is reused. Claim 13 is rejected on a similar basis as claim 3.
Regarding claim 14, Flynn further discloses the method of claim 13, wherein the purging comprises purging entries associated with rarely used applications, and wherein rarely used applications are identified based on a transaction number threshold ([0280]). Claim 14 is rejected on a similar basis as claim 4.
Regarding claim 15, Flynn further discloses the method of claim 13, wherein the purging comprises purging entries associated with less interactive users, and wherein less interactive users are identified based on a transaction number threshold ([0280]). Claim 15 is rejected on a similar basis as claim 5.
Claims 6 and 16 are rejected under 35 U.S.C. 103 as being unpatentable over Roberts in view of Dai and further in view of Rohde and still in further view of Weingarten, Flynn, and Singhal et al. (US 2024/0264866).
Regarding claim 6, Roberts, Dai, Rohde, and Weingarten do not explicitly disclose the non-transitory computer-readable storage medium of claim 3, wherein the purging comprises purging entries associated with less interactive users and rarely used applications based on transaction thresholds, and wherein the transaction thresholds are dynamic based on current memory usage. Regarding the purging of entries based on transaction thresholds, Flynn discloses in Paragraph [0280] that data is evicted based on comparing the access frequency to a predefined threshold which is determined to be analogous to a transaction number threshold. The threshold may be used to identify rarely used applications of less interactive users. Flynn does not explicitly disclose the thresholds are dynamic based on current memory usage. Regarding this aspect of the limitation, Singhal discloses in the Paragraph [0003] “The storage management system is configured to calculate a dynamic cache limit for an application automatically and independent of (e.g., without) user input, based on usage of the application at a computing device, conditions of the computing device, or a combination thereof. Example parameters considered in computing the dynamic cache limit include usage frequency of the application, minimum data retention requirements for the application, available computing device storage, a storage consumption rate of the application, storage consumption by the application relative to at least one other application, size of digital content created or consumed by the application, data download frequency, frequently used digital content, and so forth.” Herein Singhal explicitly identifies a dynamic cache limit for an application based on the storage usage by the application. Additionally, Singhal discloses in Paragraph [0021] that the limit may be based on the user utilization of the application. In both scenarios, it would be obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to dynamically adjust the transaction threshold based on the current memory usage in order to incorporate relative performance when considering the allowed utilization in order to not disrupt storage utilization through over or under allocation. Roberts, Dai, Rohde, Weingarten, Flynn, and Singhal are analogous art because they are from the same field of endeavor of implementing system management techniques.
Regarding claim 16, Roberts, Dai, Rohde, and Weingarten do not explicitly disclose the method of claim 13, wherein the purging comprises purging entries associated with less interactive users and rarely used applications based on transaction thresholds, and wherein the transaction thresholds are dynamic based on current memory usage. Regarding the purging of entries based on transaction thresholds, Flynn discloses in Paragraph [0280] that data is evicted based on comparing the access frequency to a predefined threshold which is determined to be analogous to a transaction number threshold. The threshold may be used to identify rarely used applications of less interactive users. Flynn does not explicitly disclose the thresholds are dynamic based on current memory usage. Regarding this aspect of the limitation, Singhal discloses in the Paragraph [0003] a dynamic cache limit for an application based on the storage usage by the application. Additionally, Singhal discloses in Paragraph [0021] that the limit may be based on the user utilization of the application. Claim 16 is rejected on a similar basis as claim 6.
Claims 7 and 17 are rejected under 35 U.S.C. 103 as being unpatentable over Roberts in view of Dai and further in view of Rohde and still in further view of Weingarten and Russell et al. (US 2023/0385267).
Regarding claim 7, Roberts, Dai, Rohde, and Weingarten do not explicitly disclose the non-transitory computer-readable storage medium of claim 1, wherein the determining app-segments and user-groups is performed via batch processing of the log data. Regarding this limitation, Russell discloses in Paragraph [0039] “To address at least the above-described problems relating to log data processing, log data can be asynchronously processed and/or enriched. Asynchronous processing of log data, for example processing log data upon receipt instead of (or in addition to) batch processing log data, can increase a maximum amount of log data that can be processed in a time period.” Herein Russell explicitly notes log data may be batched processed for validation. It would be obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to perform batch processing of the log data as a form of processing as a known technique in the art to achieve the predictable outcome of processing the data in such a manner. Roberts, Dai, Rohde, Weingarten, and Russell are analogous art because they are from the same field of endeavor of implementing system management techniques.
Regarding claim 17, Roberts, Dai, Rohde, and Weingarten do not explicitly disclose the method of claim 11, wherein the determining app-segments and user-groups is performed via batch processing of the log data. Regarding this limitation, Russell discloses in Paragraph [0039] log data may be batched processed for validation. Claim 17 is rejected on a similar basis as claim 7.
Claims 9 and 19 are rejected under 35 U.S.C. 103 as being unpatentable over Roberts in view of Dai and further in view of Rohde and still in further view of Weingarten and Hsu et al. (US 2022/0164118).
Regarding claim 9, Roberts further discloses, in the italicized portions, the non-transitory computer-readable storage medium of claim 1, wherein the memory usage estimation is based on historic memory usage data ([0060]), and wherein the memory usage estimation is a peak memory usage estimation computed based on at least a number of unique users, a number of unique applications, and a number of transactions represented in the log data. Roberts discloses herein that predicted memory usage is based on historical memory usage. Roberts, Dai, Rohde, and Weingarten do not explicitly disclose the memory usage estimation involving the number of unique users and applications and the number of transactions in the log data. Regarding this aspect of the limitation, Hsu discloses in Paragraph [0119] “[0119] For example, and as will be discussed in further detail below, the usage data compiler 512 can combine samples of the memory usage data in accordance with different access resolutions for the different accessing agents. For example, in one or more embodiments, the usage data compiler 512 can generate agent-specific memory usage records that include different combinations of the memory usage data. Indeed, in one or more implementations, the usage data compiler 512 can generate an agent-specific memory usage record for each accessing agent based on unique access resolutions for the respective accessing agents.” Herein Hsu discloses using analysis techniques to track agent, or user and/or application, specific accesses to resources. These specific trackings per agent basis are interpreted as being each respective of a marker of a unique user or application access. In this manner, it would be obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the application specific usage as discussed in Hsu with the teachings of Roberts, Dai, Rohde and Weingarten as Dai, Rohde, and Weingarten presently discuss managing and implementing access policies based on monitored usage and applying corresponding policies to groups of users for accessing particular applications in order to improve memory consumption. Roberts, Dai, Rohde, Weingarten and Hsu are analogous art because they are from the same field of endeavor of implementing system management techniques.
Regarding claim 19, Roberts further discloses, in the italicized portions, the method of claim 11, wherein the memory usage estimation is based on historic memory usage data ([0060]), and wherein the memory usage estimation is a peak memory usage estimation computed based on at least a number of unique users, a number of unique applications, and a number of transactions represented in the log data. Roberts, Dai, Rohde, and Weingarten do not explicitly disclose the memory usage estimation involving the number of unique users and applications and the number of transactions in the log data. Regarding this aspect of the limitation, Hsu discloses in Paragraph [0119] using analysis techniques to track agent, or user and/or application, specific accesses to resources. Claim 19 is rejected on a similar basis as claim 9.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ALEXANDER J YOON whose telephone number is (408)918-7629. The examiner can normally be reached on Monday-Friday 8am-3pm ET. The examiner’s email is alexander.yoon2@uspto.gov.
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/ALEXANDER YOON/
Examiner, Art Unit 2135
/JARED I RUTZ/Supervisory Patent Examiner, Art Unit 2135