Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
DETAILED ACTION
This action is response to the Remarks filed on 06/15/2026.
Claims 1-14 stand rejected, objected to and are pending in this Office Action. Claims 1, and 8 are independent claims.
Response to Arguments
Applicant's arguments filed 06/15/2026 have been fully and respectfully considered. As per the Examiner’s responses, please refer to below discussions:
The Applicant argued that “”Krishnamoorthy at paragraphs [0033] and [0066] purportedly
teaches this limitation. However, Krishnamoorthy does not disclose, teach,
or suggest "clustering the plurality of events into one or more clusters of events that have been
observed, across multiple user accounts, as repeatedly used in the same sequence,"” and
“Krishnamoorthy's paragraphs [0024]-[0025] as allegedly
relevant to this claim limitation. However, these paragraphs merely describe recording user
activity for a single user during a web browsing session, such as tab opening events, tab closing
events, tab switching events, web page element selection events, focus events, mouse click events,
and mouse roll-over events. In other words, the recorded user activity represents a sequential log
of events captured during one user's browsing session. Selecting events based on recency for a
single user is not equivalent to clustering events that have been observed, across multiple user
accounts, as repeatedly used in the same sequence”,
the Examiner respectfully agreed and further respectfully incorporated a new reference published to COBB for curing the deficiency of Bandaru and Krishnamoorthy on teaching the newly amended subject matter.
The Applicant further argued that “”The cited references also fail to disclose, teach, or suggest "re-training the one or more trained machine learning models based on the associations of user actions to produce one or more re-trained machine learning models”” and “”However, Van Durme merely describes re-training in the context of fine-tuning a large natural language model with "large numbers of trained parameters."”,
the Examiner respectfully submits that Van Durme extensively disclosed large language models on pre-training, training and re-training. As such, in addition to teaching re-training, Van Durme teaches the training that is performed after pre-training may also be interpreted as re-training. At [0064], Van Durme teaches
“Receive operation 512 receives predicted words and/or tokens as an output from the natural language model. In aspects, the output includes, sets of tokens with probability values associated with each token. The output from the natural language model is a response to receiving the prompt as an input.”. Therefore, Van Durme teaches the claim limitation as required.
Concerning “The cited references also fail to show or suggest "a browser extension," as recited by
claims 1 and 8. Although the Office action suggests that Bandaru describes a browser extension,
Bandaru is silent regarding a browser extension” as remarked by the Applicant,
The Examiner respectfully submits, as recited a browser extension can be (comprises) any one of
a browser extension program,
a browser plug-in,
an application program that is hosted on an agent computer or user computer, or
a browser that is natively programmed to, or executing browser-executable code programmed to, execute one or more programmatic calls to an application programming interface of a workflow automation application.
Accordingly, a browser extension is extremely broadly defined data structure or program that performs browsing function. Van Durme extensively disclosed browser, interactive browser or browser and internet browser programs that disclosed the browser extension as required.
Remarks
The instant application is the U.S. national stage of the Patent Cooperation Treaty (PCT) international application PCT/US2024/031896, filed under 35 U.S.C. § 371. The Written Opinion of the PCT application made reference to Bandaru (“CONTROL SYSTEM FOR LEARNING AND SURFACING FEATURE CORRELATIONS” (U.S. Patent 11100424), KRISHNAMOORTHY (“METHOD AND APPARATUS FOR DETERMINING USER BROWSING BEHAVIOR”, U.S. Patent Application Publication US 20150007065 A1) and VAN DURME (“SEMANTIC PARSING OF UTTERANCE USING CONTRACTIVE PARAPHRASING”, U.S. Patent Application Publication US 20220327288 A1), Novik (“QUERY TREES INCLUDING OR NODES FOR EVENT FILTERING” (U.S. Patent 7284245) and Arad (“SORTED-TREE-BASED EVENT QUEUE FOR DISCRETE EVENT SIMULATORS” (U.S. Patent 7562367).
The Written Opinion considered the PCT application of novelty and Industrial applicability to claims 1-14. The Written Opinion was thoroughly reviewed and the cited reference Bandaru, KRISHNAMOORTHY and VAN DURME are incorporated in the instant action.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 USC § 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 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 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 non-obviousness.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 1 -14 are rejected under 35 USC § 103 as being unpatentable over
Bandaru et al.: “CONTROL SYSTEM FOR LEARNING AND SURFACING FEATURE CORRELATIONS” (United States Patent 11100424 B2, DATE PUBLISHED 2021-08-24; and Date Filed 2017-08-23, hereafter “Bandaru”), in view of
KRISHNAMOORTHY et al.: “METHOD AND APPARATUS FOR DETERMINING USER BROWSING BEHAVIOR” (U.S. Patent Application Publication US 20150007065 A1, DATE PUBLISHED 2015-01-01; and DATE FILED 2014-06-30, hereafter “KRISHNAMOORTHY”), and further in view of
COBB et al.: INTER-TRAJECTORY ANOMALY DETECTION USING ADAPTIVE VOTING EXPERTS IN A VIDEO SURVEILLANCE SYSTEM” (U.S. Patent Application Publication US 20110044499 A1, DATE PUBLISHED 2011-02-24; and DATE FILED 2009-08-18, hereafter “COBB”) and
VAN DURME et al.: “SEMANTIC PARSING OF UTTERANCE USING CONTRACTIVE PARAPHRASING” (U.S. Patent Application Publication US 20220327288 A1, DATE PUBLISHED 2022-10-13; and DATE FILED 2021-04-13, hereafter “VAN DURME”).
As per claim 1, Bandaru teaches a workflow automation computer system comprising:
one or more hardware processors (See col. 3, lines 15-16, the computing system includes one or more processors or servers);
one or more network interfaces that are communicatively coupled to one or more internetworks and capable of network communication with a browser extension hosted on an agent computer, a relational database system, and a support ticket system (See col. 14, lines 2-15 and col. 7, lines 4-12 and 15-26, tenant computing systems or user devices or system that interacts with architecture. In the client device 16, a communications link 13 is provided that allows the handheld device to communicate with other computing devices and under some embodiments provides a channel for receiving information automatically allowing communication though one or more communication protocols which are wireless services used to provide cellular access to a network, as well as WI-FI protocols, and BLUETOOTH protocol, which provide local wireless connections to networks. The demographic data and usage data from the set of tenants reads on the database system; and the usage segment selection logic identifying usage segments based on data from different tenants that have different usage levels of the various features enabled by the hosted services, in which the identifying and selecting of usage segment based on different tenants of different usage levels of the various features reads on supporting a ticket system); and
one or more non-transitory computer-readable storage media coupled to the one or more hardware processors (See col. 14, lines 50-53, memory 21 stores computer readable instructions that, when executed by processor 17, cause the processor to perform computer-implemented steps or functions according to the instructions); and
storing: one or more trained machine learning models (See col. 6, lines 53-54, a machine learning logic 202 illustratively continues to refine any of the models that are generated by logic 144 and model output logic 204 outputs the model 145 for the selected segment.).
Bandaru does not explicitly teach the trained machine learning models having been trained to output predictions of actions of web-based applications based on input specifying a plurality of browser events from interactions with the web-based applications.
However, KRISHNAMOORTHY teaches the trained machine learning models having been trained to output predictions of actions of web-based applications based on input specifying a plurality of browser events from interactions with the web-based applications (See [0031], user browsing behavior may be determined for a plurality of users and the collated data may be subjected to a set of text mining & predictive models (hereinafter collectively referred to as prediction models) stored in the memory 204 for mining relevant information that drive the prediction of the user intent.).
It would have been obvious to one having ordinary skill in the art at the time the Applicant’s application was filed to combine KRISHNAMOORTHY’s teaching with Bandaru because Bandaru is dedicated to cloud-based systems that host cloud-based services for tenants and KRISHNAMOORTHY is dedicated to web browsing and more particularly to determining user browsing behavior, the combined teaching of S Bandaru and KRISHNAMOORTHY references would have allowed Bandaru to better provide cloud-based services for tenants based on an analysis of the tenant’s browsing behavior.
Bandaru in view of KRISHNAMOORTHY further teaches:
one or more sequences of instructions which, when executed using one or more processors, cause the one or more processors to execute:
receiving, from the browser extension, one or more browser event objects corresponding to user input signals arising from interactions of the agent computer with the web-based applications (See KRISHNAMOORTHY: [0024], by recording a URL (or web page) associated with each tab, the control file in effect records a web page access sequence, a web page switching sequence, a web page closing sequence, a time spent on each web page (for example, from the recorded tab active time), a time for which the web page is idle (for example, a time for which a web page was not attended to by the user), a last active web page (for example, last web page open before closing the browser), selection events like mouse clicks or roll-overs on each web page or time spent on individual web page or web page elements.),
extracting attribute values from the one or more browser event objects (See KRISHNAMOORTHY: [0030], determining user behavioral attributes like an order of accessing web pages related to the web domain, an order of web pages followed to lead into a web page associated with payment for a product or a service, an average time spent on the web domain, a visit frequency corresponding to the web domain and an average time spent on the one or more web pages corresponding to the web domain. Here determining values of the user behavioral attributes on web events reads on extracting the values), and
storing, in the relational database system, the one or more browser event objects and attribute values as a plurality of events of user action time series records (See KRISHNAMOORTHY: [0025] and [0061], the user activity is recorded on a series of event time instances for web page events; and numerical, text and/or categorical information in the data corresponding to the recorded user activity may be organized into a suitable structured format such as a tabular format (for example, a rectangle table or a row-by-column format) and stored);
clustering the plurality of events and performing action analysis on the plurality of events by executing a first inference stage of the one or more trained machine learning models over the one or more events to output suggestions of associations of user actions corresponding to the one or more events (See KRISHNAMOORTHY: [0033] and [0066], segmenting a plurality of users visiting the one or more tagged web pages into homogeneous groups based on commonality in at least one of location, user profile, purchasing history, device information, browser information, agent interaction information and user activity corresponding to the plurality of users. In an embodiment, at least one algorithm from among a probabilistic latent semantic analysis (PLSA) clustering method based algorithm or Self-organizing maps based algorithm may be used to segment the users into homogenous groups like those including users using particular browsers, users interested in certain products and the like).
Bandaru in view of KRISHNAMOORTHY does not explicitly teach the clustering into one or more clusters of events that have been observed, across multiple user accounts, as repeatedly used in the same sequence.
However, as an analogous art on events management COBB teaches clustering into one or more clusters of events that have been observed, across multiple user accounts, as repeatedly used in the same sequence (See Fig. 5D and [0008], the same sequence of events (6, 8, 2, 3, 1) in three clusters is repeated. The ngram trie may have been generated from a plurality of previously observed sequences, each storing an ordered string of labels assigned to clusters in the ART network for objects detected in the input stream of video frames. Upon determining the probability, the observed interaction between the first foreground object and the second foreground object falls below a specified threshold, an alert may be issued to a user of the video surveillance system. Here the trie teaches sequence of events previously observed and the “sequence” the tries belongs to has also been observed, repeated in the same sequence)
It would have been obvious to one having ordinary skill in the art at the time the Applicant’s application was filed to combine COBB’s teaching with Bandaru in view of KRISHNAMOORTHY because Bandaru is dedicated to cloud-based systems that host cloud-based services for tenants, KRISHNAMOORTHY is dedicated to web browsing and more particularly to determining user browsing behavior and COBB is dedicated to analyzing a sequence of video frames, specifically to observing and identifying anomalous behavior in a sequence of video frames using adaptive voting experts, the combined teaching of Bandaru, KRISHNAMOORTHY and COBB references would have allowed Bandaru in view of KRISHNAMOORTHY to use voting experts of machine-learning engine to segment adaptive resonance theory (ART) network label sequences for observing what happens to different objects in a scene over time.
Bandaru in view of KRISHNAMOORTHY and further in view of COBB teaches the following:
performing action analysis on the one or more clusters of events by executing a first inference stage of the one or more trained machine learning models over each of the one or more clusters of events to output suggestions of associations of user actions corresponding to the one or more events (See KRISHNAMOORTHY: [0038] and Page 11, claim 9, organizing categorical information related to the recorded user activity into a suitable structured format such as a tabular or a graphical format and for enabling application of various machine learning and data mining techniques to facilitate analysis of the recorded user activity; and analyzing the recorded user activity using one or more analytical algorithms upon conversion into the structured format for determining the user browsing behavior, wherein an algorithm from among the one or more analytical algorithms corresponds to one of a logistic regression model based algorithm, a decision tree based algorithm, artificial neural network based algorithm and support vector machines based algorithm.);
forming one or more training data records based on the associations of user actions corresponding to the one or more events (See Bandaru: col. 4, lines 33-36, Logic 144 then trains a usage model that identifies the commonality of usage (or similarity) of the different features among the various tenants from which the data were acquired; and KRISHNAMOORTHY: [0028], the structured format is chosen such that various machine learning and data mining techniques may be applied on the structured data, such as a logistic regression model, decision trees, artificial neural network, support vector machine and so on. In an illustrative example, converting the recorded user activity into graphical format may include generating a tab visit sequence configured to depict a sequence of accessing tabs from among the one or more tabs).
Bandaru in view of KRISHNAMOORTHY and further in view of COBB does not explicitly teach re-training the one or more trained machine learning models based on the associations of user actions to produce one or more re-trained machine learning models.
However, VAN DURME teaches re-training the one or more trained machine learning models based on the associations of user actions to produce one or more re-trained machine learning models (See [0030], A number of input/output pairs in the prompt may depend on a capacity of input allowed by the natural language model 150. Some natural language models may restrict the capacity of input based on a number of tokens. In aspects, a cost of re-training and/or fine-tuning the natural language model 150 with large numbers of trained parameters is prohibitively expensive. Re-training may include a preparation of a large amount of training data to influence prediction when there are already a very high number (e.g., millions or billions) of trained parameters.).
It would have been obvious to one having ordinary skill in the art at the time the Applicant’s application was filed to combine VAN DURME’s teaching with Bandaru in view of KRISHNAMOORTHY and further in view of COBB because Bandaru is dedicated to cloud-based systems that host cloud-based services for tenants, KRISHNAMOORTHY is dedicated to web browsing and more particularly to determining user browsing behavior, COBB is dedicated to analyzing a sequence of video frames, specifically to observing and identifying anomalous behavior in a sequence of video frames using adaptive voting experts, and VAN DURME is dedicated to automatically generating a program based on a natural language utterance using semantic parsing, the combined teaching of Bandaru, KRISHNAMOORTHY and VAN DURME references would have allowed Bandaru in view of KRISHNAMOORTHY and further in view of COBB to utilize the pre-trained, training and re-training natural language models to accurately predict and provide services and advices to the cloud-based tenants.
As per claim 2, Bandaru in view of KRISHNAMOORTHY and further in view of COBB and VAN DURME teaches the workflow automation computer system of claim 1, further comprising one or more sequences of instructions which, when executed using one or more processors, cause the one or more processors to deploy the one or more re-trained machine learning models, for access and use via the browser extension (See VAN DURME: [0030], Re-training may include a preparation of a large amount of training data to influence prediction when there are already a very high number (e.g., millions or billions) of trained parameters. Having a few examples may accomplish fine-tuning the natural language model 150 to be specific to a given task. Fine-tuning a large natural language model may result in developing a specialized version of the large natural language model for each task. Here data preparation and fine-tuning of the natural language model teach a deployment of the model).
As per claim 3, Bandaru in view of KRISHNAMOORTHY and further in view of COBB and VAN DURME teaches the workflow automation computer system of claim 1, further comprising one or more sequences of instructions which, when executed using one or more processors, cause the one or more processors to execute:
receiving a sequence of one or more user events (See KRISHNAMOORTHY: [0025], a web browser of the user device at time instances `T.sub.1` through `T.sub.6` are associated with user activity events on web pages);
executing an inference stage of one or more machine learning models over the sequence of one or more user events (See KRISHNAMOORTHY: [0025], the user activity is recorded at periodic time instances for opening, unloading, accessing, and switching web pages executed user events on which opening new web page executing inference stage of unloading currently open web page);
outputting a suggestion of a workflow that most accurately matches the sequence of one or more user events (See KRISHNAMOORTHY: [0025], the recorded user activity may be converted into a structured format (as will be explained later with reference to Tables 2 and 3) and analyzed to derive information, such as tab visit sequence, average time spent on tabs and the like to facilitate in determining a user browsing behavior. The facilitated determining a user browsing behavior suggested a most accurately matches the sequence of one or more user events);
receiving user input corresponding to user action in response to the suggestion (See KRISHNAMOORTHY: [0026], recording the user activity related to the web domain comprises recording the user activity related to one or more updated sections on the at least one web page comprising the dynamically updated content. More specifically, the control file may be configured to treat web pages including dynamic content as web pages with logical sections.);
when the user input indicates selecting the suggested workflow, generating reinforcement training data based on the user input (See VAN DURME: [0030], Re-training may include a preparation of a large amount of training data to influence prediction when there are already a very high number (e.g., millions or billions) of trained parameters. Having a few examples may accomplish fine-tuning the natural language model 150 to be specific to a given task. Fine-tuning a large natural language model may result in developing a specialized version of the large natural language model for each task. Re-training teaches an enforcement training);
when the user input corresponds to one or more additional user events, storing an updated sequence of user events as new training data (See KRISHNAMOORTHY: [0026], the control file may be configured to treat web pages including dynamic content as web pages with logical sections. In such cases, the changes in the web pages may be considered as addition/deletion to the logical sections and the user actions corresponding to each logical section may be recorded separately and recording the user activity related to the web domain comprises recording the user activity related to one or more updated sections on the at least one web page comprising the dynamically updated content.).
As per claim 4, Bandaru in view of KRISHNAMOORTHY and further in view of COBB and VAN DURME teaches the workflow automation computer system of claim 1, further comprising one or more sequences of instructions which, when executed using one or more processors, cause the one or more processors to execute:
storing, as a vocabulary, the associations of user actions corresponding to the one or more events in association with identifiers of websites with which the user actions occurred (See KRISHNAMOORTHY: Abstract and [0059], facilitating downloading of a control file on a user device upon detecting a first web page access event corresponding to a tagged web page from among the one or more tagged web pages. The control file is configured to facilitate recording of user activity related to a web domain on one or more tabs of a web browser associated with the user device. Furthermore, the method comprises receiving recorded user activity corresponding to at least one web browsing session and determining a user browsing behavior based on the recorded user activity; and the user activity related to each section may be recorded by retrieving the original content associated with the each section using the corresponding IDs and identifying the change therein.);
receiving first user input specifying a natural language instruction for an action or goal (See KRISHNAMOORTHY: [0029], [0029] The prompt manager 140 generates a prompt as input to the natural language model 150 and determines a canonical utterance. In particular);
forming a prompt for a large language model (LLM), the prompt comprising instructions to generate an output sequence of workflow steps, the vocabulary, and the user input (See KRISHNAMOORTHY: [0029], exemplary conversations include sentences that are relevant to the natural language utterance. Accordingly, the prompt provides the natural language model 150 (pre-trained) a set of examples for influencing predicted utterances to be consistent in context with the exemplary pairs of sentences. In aspects, the task-specific sample generator 142 generates an utterance based on existing instructions. In aspects, generating and providing the prompt as input to the natural language model 150 represent a dynamic “few-shot” learning technique. The natural language model 150 analyzes the prompt with a few examples that precede the natural language utterance before predicting a response to the natural language utterance. Thus, the natural language model 150 effectively “learns” exemplary input/output pairs representing the task before predicting utterances in response to the natural language utterance without performing a training process);
programmatically calling an application programming interface (API) of the LLM using the prompt (See VAN DURME: [0057], the natural language utterance 402 is the exemplary utterance “What did I set as my response status for the team meeting?” Based upon the received natural language utterance, the prompt generator generates the prompt 404 as input to the natural language model);
outputting a suggestion of a stored workflow that most accurately matches the natural language instruction and comprises the sequence of workflow steps and/or a new sequence of workflow steps (See VAN DURME: [0057], for example, the prompt 404 includes two exemplary pairs of input/output (i.e., a user utterance and computer output) pairs that represent a task, followed by the natural language utterance as the last input (as shown in bold text). Each pair includes an utterance by a user and another utterance by the computer. For example, the first pair indicates: “User: what is response statue of the meeting; computer: response status of the meeting.” The second pair indicate: “User: when is the meeting; computer: start time of the meeting.” In example, the two pairs are a part of the top-N relevant, task-specific example input/output pairs).
As per claim 5, Bandaru in view of KRISHNAMOORTHY and further in view of COBB and VAN DURME teaches the workflow automation computer system of claim 4, further comprising one or more sequences of instructions which, when executed using one or more processors, cause the one or more processors to execute:
receiving second user input corresponding to user action in response to the suggestion (See KRISHNAMOORTHY: [0026], recording the user activity related to the web domain comprises recording the user activity related to one or more updated sections on the at least one web page comprising the dynamically updated content. More specifically, the control file may be configured to treat web pages including dynamic content as web pages with logical sections);
when the user input indicates selecting the suggestion of a stored workflow, generating reinforcement training data based on the user input (See VAN DURME: [0030], Re-training may include a preparation of a large amount of training data to influence prediction when there are already a very high number (e.g., millions or billions) of trained parameters. Having a few examples may accomplish fine-tuning the natural language model 150 to be specific to a given task. Fine-tuning a large natural language model may result in developing a specialized version of the large natural language model for each task. Re-training teaches an enforcement training);
when the user input corresponds to one or more additional user events, storing an updated sequence of user events as new training data (See KRISHNAMOORTHY: [0026], the control file may be configured to treat web pages including dynamic content as web pages with logical sections. In such cases, the changes in the web pages may be considered as addition/deletion to the logical sections and the user actions corresponding to each logical section may be recorded separately and recording the user activity related to the web domain comprises recording the user activity related to one or more updated sections on the at least one web page comprising the dynamically updated content).
As per claim 6, Bandaru in view of KRISHNAMOORTHY and further in view of COBB and VAN DURME teaches the workflow automation computer system of claim 1, wherein the browser extension comprises any of a browser extension program, a browser plug-in, an application program that is hosted on an agent computer or user computer, or a browser that is natively programmed to, or executing browser-executable code programmed to, execute one or more programmatic calls to an application programming interface of a workflow automation application (See VAN DURME: [0024] The client device 102 connects with the client application server 110 via the network 130 to execute applications through the interactive browser 104. The client application server 110 interacts with the client device 102 and the semantic parsing server 120 via the network 130 to perform information queries and retrievals, calendar scheduling, task management, and/or interactions with various other applications.).
As per claim 7, Bandaru in view of KRISHNAMOORTHY and further in view of COBB and VAN DURME teaches the workflow automation computer system of claim 1, wherein each of the one or more trained machine learning models comprises a Transformer-based neural network (See KRISHNAMOORTHY: [0029], algorithms may include, but are not limited to, one of a logistic regression model based algorithm, a decision tree based algorithm, artificial neural network based algorithm and support vector machines based algorithm. In an embodiment).
As per claim 8-14, the claims recite one or more non-transitory computer-readable storage media storing one or more trained machine learning models having been trained to output predictions of actions of web-based applications based on input specifying a plurality of browser events from interactions with the web-based applications and one or more sequences of instructions which (See KRISHNAMOORTHY: [0028], , the processor 202 is configured to, with the content of the memory 204, cause the apparatus 200 to convert the recorded user activity to a structured format configured to facilitate determining of the user browsing behavior), when executed using one or more processors, the one or more processors being are communicatively coupled to one or more network interfaces that are communicatively coupled to one or more internetworks and capable of network communication with a browser extension hosted on an agent computer, a relational database system, and a support ticket system (See Bandaru: col. 14, lines 2-15 and col. 7, lines 4-12 and 15-26, tenant computing systems or user devices or system that interacts with architecture. In the client device 16, a communications link 13 is provided that allows the handheld device to communicate with other computing devices and under some embodiments provides a channel for receiving information automatically allowing communication though one or more communication protocols which are wireless services used to provide cellular access to a network, as well as WI-FI protocols, and BLUETOOTH protocol, which provide local wireless connections to networks. The demographic data and usage data from the set of tenants reads on the database system; and the usage segment selection logic identifying usage segments based on data from different tenants that have different usage levels of the various features enabled by the hosted services, in which the identifying and selecting of usage segment based on different tenants of different usage levels of the various features reads on supporting a ticket system) , cause the one or more processors to execute the steps of the operations as recited by the system of claims 1-7, above, respectively, and as rejected under 35 USC § 103 as being unpatentable over Bandaru in view of KRISHNAMOORTHY and further in view of COBB and VAN DURME.
Accordingly, claims 8-14 are rejected along the same rationale that rejected claims 1-7, respectively.
Related Prior Arts
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure can be found in the PTO-892 Notice of Reference Cited.
Conclusion
THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Examiner has cited particular columns and line numbers in the references applied to the claims above for the convenience of the applicant. Although the specified citations are representative of the teachings of the art and are applied to specific limitations within the individual claim, other passages and figures may apply as well. It is respectfully requested from the applicant in preparing responses, to fully consider the references in entirety as potentially teaching all or part of the claimed invention, as well as the context of the passage as taught by the prior art or disclosed by the Examiner. SEE MPEP 2141.02 [R-5] VI. PRIOR ART MUST BE CONSIDERED IN ITS ENTIRETY, INCLUDING DISCLOSURES THAT TEACH AWAY FROM THE CLAIMS: A prior art reference must be considered in its entirety, i.e., as a whole, including portions that would lead away from the claimed invention. W.L. Gore & Associates, Inc. v. Garlock, Inc., 721 F.2d 1540, 220 USPQ 303 (Fed. Cir. 1983), cert. denied, 469 U.S. 851 (1984) In re Fulton, 391 F.3d 1195, 1201, 73 USPQ2d 1141, 1146 (Fed. Cir. 2004). >See also MPEP §2123.
In the case of amending the Claimed invention, Applicant is respectfully requested to indicate the portion(s) of the specification which dictate(s) the structure relied on for proper interpretation and also to verify and ascertain the metes and bounds of the claimed invention.
Contact Information
Any inquiry concerning this communication or earlier communications from the examiner should be directed to KUEN S LU whose telephone number is (571)272-4114. The examiner can normally be reached on M-F, 8-19, Mid-Flex 2 hours.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Mr. Aleksandr Kerzhner can be reached on 571-270-1760. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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KUEN S LU /Kuen S Lu/
Art Unit 2165
Primary Patent Examiner
August 3, 2026
Prosecution Insights