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 .
Notice to Applicant
The following is a Non-Final Office action. In response to Examiner’s Final Rejection of 10/27/2025, Applicant, on 2/27/2026, amended claims 1, 9 and 17. Claims 1-20 are pending in this application and have been rejected below.
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this
application is eligible for continued examination under 37 CFR 1.114, and the fee set
forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action
has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on
2/27/2026 has been entered.
Response to Arguments
Applicant’s arguments filed February 27, 2026 have been fully considered but they are not persuasive and/or are moot in view of the revised rejections. Applicant’s arguments will be addressed herein below in the order in which they appear in the response filed February 27, 2026.
On Pg. 8-15 of the Remarks, regarding 35 U.S.C. § 101 rejections, Applicant states at Step 2A, prong two, the additional elements integrate any allegedly recited abstract ideas into specific and practical applications by providing meaningful limitations
on, among other things, insight quantization and state control (IQSC) for providing situational awareness and interaction specific guidance for customer relationship management (CRM) and/or enterprise systems. Applicant respectfully submits that the additional elements provide significant technical detail and meaningful limitations regarding how the claimed solutions improve CRM and/or enterprise systems in order to "provide situational awareness and interaction specific guidance and/or automated actions to avoid or diffuse user specific issues in dynamic and/or real-time situations." . In response, Examiner recommends expounding upon the mediating output action and how the interface is updated coupled with product offers and customer service. Examiner finds the elements in Example 40 are directed to improving network monitoring. Examiner finds no similar improvements to take into consideration here. Examiner maintains the claims are directed to an abstract idea of electronic communication context analysis.
On Pg. 16-17 of the Remarks, regarding 35 U.S.C. § 103 rejections. Applicant states the cited references, singly or in any proper combination, do not disclose and would not have rendered obvious the limitations of amended claim 1 . In response, new ground(s) of rejection is made necessitated by amendment see MPEP 706.07a where Kim is now applied. Regarding the 35 U.S.C. § 103 rejection, Applicant’s arguments with respect to claims has been considered but are moot in view of the new grounds of rejection.
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. Claims 1-20 are directed to insight quantization and state control.
Claim 1 recites a method for insight quantization and state control, Claim 9 recites an apparatus for insight quantization and state control and Claim 17 recites an article of manufacture for insight quantization and state control, which include :monitoring, in real-time or near-real-time, a channel of a communications network for an initial biometric indicator in order to determine a context of an electronic communication between a user and a user interface of an enterprise system; selecting a quantized insight index relevant to a current situation of the user based on the context of the electronic communication; monitoring the channel of the communications network to assign respective values to a set of indicators corresponding to the quantized insight index and the user; generating a current value of the quantized insight index for the user based on the respective values of the set of indicators corresponding to the quantized insight index determining that the current value is equal to, or greater than, a trigger threshold and in response; executing, state improvement code that causes the enterprise system, in real-time or near-real-time, to: input the current value of the quantized insight index into an issue forecaster model to identify an issue with the electronic communication between the user and the user interface of the enterprise system, generate a mediating output action comprising a change to the user interface of an enterprise system to select a communication channel for the user, (ii) indicating an offer for a discounted product, or (iii) connecting the user with a representative and implement the change to the user interface; monitoring the channel of the communications network to update the respective values of the set of indicators; generating an updated value of the quantized insight index based on updated values of the set of indicators corresponding to the quantized insight index; and determining that the updated value is less than the trigger threshold.
As drafted, this is, under its broadest reasonable interpretation, within the Abstract idea grouping of “Methods of Organizing Human Activity” – managing interactions. The recitation of “enterprise system”, “memory”, “processor”, “communications network”; “interface”; “multimedia I/O devices”; “computer-readable storage medium”; “computer-executable program code” and “computing apparatus”, provide nothing in the claim elements to preclude the step from being “Methods of Organizing Human Activity”- managing interactions. Accordingly, the claim recites an abstract idea.
This judicial exception is not integrated into a practical application. The claims primarily recite the additional element of using computer components to perform each step. The “enterprise system”, “memory”, “processor”, “communications network”; “interface”; “multimedia I/O devices”; “computer-readable storage medium”; “computer-executable program code” and “computing apparatus” is recited at a high-level of generality, such that it amounts no more than mere instructions to apply the exception using a computer component. See MPEP 2106.05(f). The additional elements of “microphone”, “camera” and “infrared sensor” are MPEP 2106.05(h)- field of use.
Accordingly, the additional elements do not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. The claims also fail to recite any improvements to another technology or technical field, improvements to the functioning of the computer itself, use of a particular machine, effecting a transformation or reduction of a particular article to a different state or thing, and/or an additional element applies or uses the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole is more than a drafting effort designed to monopolize the exception. See 84 Fed. Reg. 55. In particular, there is a lack of improvement to a computer or technical field in data processing.
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional elements when considered both individually and as an ordered combination do not amount to significantly more than the abstract idea. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements of “enterprise system”, “memory”, “processor”, “communications network”; “interface”; “multimedia I/O devices”; “computer-readable storage medium”; “computer-executable program code” and “computing apparatus” is insufficient to amount to significantly more. (See MPEP 2106.05(f) – Mere Instructions to Apply an Exception – “Thus, for example, claims that amount to nothing more than an instruction to apply the abstract idea using a generic computer do not render an abstract idea eligible.” Alice Corp., 134 S. Ct. at 235). Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept.
The claim fails to recite any improvements to another technology or technical field, improvements to the functioning of the computer itself, use of a particular machine, effecting a transformation or reduction of a particular article to a different state or thing, adding unconventional steps that confine the claim to a particular useful application, and/or meaningful limitations beyond generally linking the use of an abstract idea to a particular environment. See 84 Fed. Reg. 55. Viewed individually or as a whole, these additional claim element(s) do not provide meaningful limitation(s) to transform the abstract idea into a patent eligible application of the abstract idea such that the claim(s) amounts to significantly more than the abstract idea itself. With regards to receiving data and step 2B, it is M2106.05(d)- Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information) and Storing and retrieving information in memory, Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015). Regarding Step 2B and the additional elements of “microphone”, “camera” and “infrared sensor” are MPEP 2106.05(h)- field of use.
Examiner concludes that the additional elements in combination fail to amount to significantly more than the abstract idea based on findings that each element merely performs the same function(s) in combination as each element performs separately. The claim is not patent eligible. Thus, taken alone, the additional elements do not amount to significantly more than the above-identified judicial exception (the abstract idea). Looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements taken individually.
Dependent Claims 2-8, 10-16, and 18-20 recite the quantized insight index corresponds to a patience level of the user; the context of the user includes one or more of a product type, a service type, an interface type, a transaction type, and a front-end component type; the set of indicators that the quantized insight index is based on include one or more of a connection time, a noise level, grammar characteristics, a wait time, input characteristics, a temperature, facial expressions, body language, and voice characteristics; setting the trigger threshold based on the context of the user, the quantized insight index relevant to the current situation of the user, user data, and a knowledge base of historical situations, wherein the historical situations include historical occurrences of issues associated with the current situation of the user and historical values of the quantized insight index at the historical occurrences of the issues; the values associated with the user for the set of indicators that the quantized insight index is based on are monitored via one or more sensors including one or more of a temperature sensor, a camera, a timer, a key logger, and a microphone; implementing the mediating output action includes controlling one or more components of the enterprise system to produce an output directed to the user; a machine learning model trained on historical occurrences of issues associated with the current situation of the user and historical values of the quantized insight index at the historical occurrences of the issues utilizes the current value of the quantized insight index as input to forecast the issue ; and further narrowing the abstract idea. These recited limitations in the dependent claims do not amount to significantly more than the above-identified judicial exceptions in Claims 1, 9 and 17. Regarding Claims,7, 15 and the additional elements of “enterprise system” and “components” it is M2106.05(d)- Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information). Regarding claim 8 and claim 16 and the additional element of machine learning model - the specification discloses the machine learning at a high-level of generality, providing examples of different techniques that may be applied. The general use of a machine learning technique does not provide a meaningful limitation to transform the abstract idea into a practical application. Therefore, currently, the machine learning is solely used a tool to perform the instructions of the abstract idea.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1- 20 are rejected under 35 U.S.C. 103 as being unpatentable over Wachell et al. , US Publication No. 20220028001A1, [hereinafter Wachell], in view of Kim et al., US Publication No. 20210212581 A1, [hereinafter Kim] in view of Tortoriello et al., US Publication No. 20200265483 A1, [hereinafter Tortoriello], and in further view of Siebel et al., US Publication No. 20220405775A1, [hereinafter Siebel].
Regarding Claim 1,
Wachell teaches A computer-implemented method, comprising: monitoring… (Wachell Par. 60)
selecting a quantized insight index relevant to a current situation of the user based on the context of the electronic communication (Wachell Par. 23; Par. 208-“The advisor can use feedback mechanisms diagrammed in FIG. 2 and FIG. 5D to rate the event highly relevant (resolved, contact client), somewhat relevant (snooze) or not relevant. These labels could, for example, correspond to multinomial categories or values on the unit interval that could then be used in standard regression, supervised learning and or generalized linear models. The bias for false positives or false negatives, recall, precision, etc. could also be adjusted by supervisors for events deemed more or less important at an enterprise level.”; Par. 209-211; Par. 326- “LF=Life Facts-Life facts may include demographic details such as age, family status, employment status, etc. Life facts may also include sentiment indicators from derived from NLP processing of emails, sms or voice phone calls with the advisor.”);
monitoring the channel of the communications network to assign respective values to a set of indicators corresponding to the quantized insight index and the user (Wachell Par. 23-“In some embodiments, the processor updates the event rules and next best action data based on the feedback data by adjusting a priority or rank of the next best action data in terms of advisor relevance, wherein the feedback data is indicative of the advisor relevance.”; Par. 30-32; Par. 68-69; Par. 507-“ In some embodiments of the process 3000, the feedback data rates the Next Best Action as highly relevant, relevant, somewhat relevant or not relevant. The direct advisor feedback (relevant, not relevant), or implicit feedback (examined, unexamined) or (account action, no account action) or (communication initiated, no communication initiated) or (money moved, non money movement) can be used to attenuate either 1) the hyperplane for the event as a categorizer 2) the rank relevance of the Next Best Action (NBA) for the advisors priority dashboard.”);
generating a current value of the quantized insight index for the user based on the respective values of the set of indicators corresponding to the quantized insight index; (Wachell Par. 371-“A portfolio user 202, such as an advisor, may be concerned with managing and advising client strategies and accounts, and receives guidance from wealth management system 200 in the form of Next Best Actions and in a dashboard of enterprise application 304 in order to guide decision making and operations Client telemetry may provide evidence which supports the next best action. For a simple event, this might be a timeseries chart of the threshold univariate value. For a predictive model, this might be the most important input features, OR a curated list of timeseries that the researcher felt were relevant to the advisor”; Par. 507)
determining that the current value is equal to, or greater than, a trigger threshold and in response executing, by a processor, state improvement code that causes the enterprise in real time or near real-time to : (Wachell Par. 207-208-“Using a representation like FIG. 16 to randomly, parametrically, or manually build a binary univariate thresholding function on a single feature, or multivariate separating hyperplane on salient features from client state, it may be possible to build a model that will detect labelled events that are relevant to a wealth advisor. Based on some initialization of the threshold/hyperplane, an event will be detected and dispatched to the advisor. The advisor can use feedback mechanisms diagrammed in FIG. 2 and FIG. 5D to rate the event highly relevant (resolved, contact client), somewhat relevant (snooze) or not relevant.”
input the current value of the quantized insight index into an issue forecaster model to identify an issue with the electronic communication between the user and the user interface of the enterprise system (Wachell Par. 210-211; Par. 228-“In some embodiments, the supervised learning pipeline will consist of a “data pipeline”, consisting of (1) data loading, (2) preprocessing and feature estimation, (3) supervised variable screening to reduce data dimensionality, (4) classification (or probabilistic classification) to predict which elements in the target variable will be zero, and which nonzero (or the likelihood of each), (5) magnitude prediction for nonzero target variable predictions, (6) k-step ahead prediction using the model fit using the previous steps (and sometimes re-fit for each new time point).”; Par 233; Par./ 466), …;
monitoring the channel of the communications network to update the respective values of the set of indicators (Wachell Par. 39-“updating the event rules and the next best action data based on the feedback data using the processor; and automatically updating the display of the interface dashboard based on the updated event rules.”);
and determining that the updated value … the trigger threshold. (Wachell Par. 200-201-“ Quasiperiodicity/Periodicity Analysis focuses on the questions of “is it a cycle?” or “is it a habit?”. Payroll is by design a cycle. Groceries or “retail therapy” are to lesser or greater extents habits. Analysis of season, trend, cycle, and quasiperiodic components (e.g., inter-event intervals) may be achieved through windowed statistics describing periodic and quasiperiodic statistics (quasiperiodic refers to a regular pattern of recurrence with a random component that does not lend itself to precise, deterministic characterization) as well as multivariate approaches appropriate for periodic and quasiperiodic data (windowed cross-correlation, cross-recurrence, vector autoregressive, deep neural networks, recurrent neural networks). This is mostly in service of feature engineering for further predictive analysis.”; Par. 207-208-“Using a representation like FIG. 16 to randomly, parametrically, or manually build a binary univariate thresholding function on a single feature, or multivariate separating hyperplane on salient features from client state, it may be possible to build a model that will detect labelled events that are relevant to a wealth advisor. Based on some initialization of the threshold/hyperplane, an event will be detected and dispatched to the advisor. The advisor can use feedback mechanisms diagrammed in FIG. 2 and FIG. 5D to rate the event highly relevant (resolved, contact client), somewhat relevant (snooze) or not relevant.”).
Wachell teaches tracking client behavior and the feature is expounded upon by Kim
… monitoring, in real-time using a biometric monitor comprising a microphone and at least one of an infrared sensor or a camera, a channel of a communications network communicatively coupled to an interface of multimedia I/O devices for an initial biometric indicator …(Kim Par. 9-10- Various embodiments provide a method and apparatus for analyzing, based on various types of context information (e.g., a time, a place, a device usage log, etc.), biometric data collected by multiple electronic devices, storing related insight information, and recognizing a user context to provide coaching to the user through the insight information appropriate for corresponding context. An electronic device according to various embodiments of the disclosure may include: a sensor module, a display device, and a processor, wherein the processor: acquires, using the sensor module, biometric information of a user and place information related to the user; matches the biometric information with the place information; displays an interface including biometric information for a predetermined period of time through the display device; determines a place of a region selected by the user and a duration corresponding to the place in the interface; and specifies the duration and displays biometric information by highlighting the biometric information within the duration in the interface.”; Par. 62; )
… generate a mediating output action comprising a change to the user interface of an enterprise system to select a communication channel for the user, (ii) indicating an offer for a discounted product, or (iii) connecting the user with a representative, and implement the change to the user interface (Kim Par. 255-“ According to an embodiment, the function objects 1751, 1753, 1755, and 1760 may include one or more objects 1751, 1753, and 1755 related to users recommended by the electronic device 101, and a contact object 1760 which allows the user to directly select a person to talk to (or a person to talk on the phone), based on the contact information. According to an embodiment, the function objects 1751, 1753, and 1755 related to the recommended users may be provided based on users each having a high priority among recommended users, wherein as many objects as the number of the recommended users may be provided. The electronic device 101 may provide only the contact object 1760 when there is no recommended user.; Par. 97; Par. 230)
Wachell and Kim are directed to context monitoring analysis. It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have improve upon analysis of Wachell, as taught by Kim, by utilizing additional context analysis with a reasonable expectation of success of arriving at the claimed invention. One of ordinary skill in the art would have been motivated to make the modification to the teachings of Wachell with the motivation of using biometric information measured at the time point (Kim Par. 5).
Wachell in view of Kim teaches tracking client behavior and the feature is expounded upon by Tortoriello:
…in order to determine a context of an electronic communication between a user and a user interface of to an enterprise system Tortoriello- Par. 18-19“As described herein, a dialogue monitoring and communications system using artificial intelligence (AI) and analytics-based machine learning may provide a more robust approach and solution. The dialogue monitoring and communications system described herein my collect and monitor customer dialogues and better identify customer touchpoints before, during, and after a transaction. For example, the dialogue monitoring and communications system may better track customer interactions across all channels and dimensions. This may enable more focus on pattern identification for process optimization. Also, the dialogue monitoring and communications system, as described herein, may help identify customer groups based on omnichannel behaviors, which may further enable a merchant to shape and design a more dedicated and personalized dynamic experience for each identified customer group. The dialogue monitoring and communications system may also execute various customer experience strategies through real-time or near real-time personalization driven by customer-centric actions.”);
Wachell, Kim and Tortoriello are directed to insight analysis. It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have improve upon data analysis of Wachell in view of Kim, as taught by Tortoriello, by utilizing additional insight analysis with a reasonable expectation of success of arriving at the claimed invention. One of ordinary skill in the art would have been motivated to make the modification to the teachings of Wachell in view of Kim with the motivation of improve the overall customer journey and experience (Tortoriello Par. 5).
Wachell in view of Kim in further view of Tortoriello teach insight analysis and the feature is expounded upon by Siebel:
generating an updated value of the quantized insight index based on updated values of the set of indicators corresponding to the quantized insight index (Siebel Par. 115-“In addition, the model orchestrator function 242 may create actionable recommendations for representatives to improve their machine learning scores to thereby improve their chances of achieving specified objectives (such as closing an opportunity or mitigating churn risk). ... Par. 123; Par. 135-“ Also, in some embodiments, the continuous analytics processing may use map reduce, iterative processing, or any other processing paradigm to process the data when a change in data is detected. In addition, in some embodiments, continuous analytics processing may perform processing for only a sub-portion of an analytic. For instance, some calculations may be updated based only on a changed or new value and, thus, not all calculations that go into an analytic may need to be recalculated. Only those that are impacted by the change may be recalculated to save resources and time.”; Par. 226-227-“… For each contributing feature, the outputs from the opportunity-level machine learning model 402 may further include the following: a current value of the feature, a change in the value of the feature since the last output of the model 402 was generated, and a polarity of the change in the value of the feature (positive or negative) since the last output of model 402 was generated. Of course, these outputs of the model 402 are for illustration only and can vary as needed or desired.”; Par. 236);
… is less than the trigger threshold (Siebel Par. 173- “In some embodiments, a data flow event is a combination of an analytic defining what is being measured, a period defining the period of a time-series to be analyzed, and an interval that defines a granular for aggregation. In addition, analytics may specify a completeness threshold for a data flow event that defines how much of the potential data for a period has been collected so far. ; Par. 389);
Wachell, Kim, Tortoriello and Siebel are directed to market analysis. It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have improve upon data analysis of Wachell in view of Kim in further view of Tortoriello, as taught by Siebel, by utilizing additional insight analysis with a reasonable expectation of success of arriving at the claimed invention. One of ordinary skill in the art would have been motivated to make the modification to the teachings of Wachell in view of Kim in further view of Tortoriello with the motivation of improving performances and/or performing other remediating actions (Siebel Par. 105).
Regarding Claim 2, Claim 10 and Claim 18,
wherein the quantized insight index corresponds to a patience level of the user ( Wachell Par. 316- “Clients may provide their advisor with a plurality of goals. Each of the goals may have a risk profile (appetite) associated with it, i.e. clients may accept a higher risk for some goals and not for others. Reinforcement learning may be used to determine life facts of the client. The life facts may be used by an advisor to adjust the risk appetite for a certain goal the client has… Thus systems and methods herein may facilitate dynamic adjustment of risk profile of clients based on detected and predicted life facts, wherein the detection and prediction is based on reinforcement learning using large-scale granular data sets of clients transactions, demographic CRM data, banking data, behavioural insights (such as that based on website traffic), questionnaires provided to clients, or other data.”)
Regarding Claim 3 and Claim 11,
wherein the context of the user includes one or more of a product type, a service type, an interface type, a transaction type, and a front-end component type. ( Wachell Par. 25-27-“ In some embodiments, the event detector is a hyperplane based event detector, wherein the interface tools configure the event detector using a list of features and a hyperplane separator. In some embodiments, the interface tools configure an event predictor using a list of features, a dependent event indicator feature, a window size, forward period prediction length, and a supervised learning method. In some embodiments, the interface tools configure a hidden state or label predictor using a list of features, a dependent event indicator; Par. 200-201;Par. 324-325; Par. 368”)
Regarding Claim 4, Claim 12 and Claim 19,
wherein the set of indicators that the quantized insight index is based on include one or more of a connection time, a noise level, grammar characteristics, a wait time, input characteristics, a temperature, facial expressions, body language, and voice characteristics ( Wachell Par. 167-“ Symbiosis 1100 primarily is a consumer of PERSONA's data; particularly the Cashflow collection. Symbiosis takes PERSONA's Cashflow data for a given end client, further categorising any requested transactional data, and uses the provided data to inform insights and derive conclusions about end clients not as individuals but as demographics or subsets based on shared characteristics (financial or personal).”; Par. 326-“ LF=Life Facts Life facts may include demographic details such as age, family status, employment status, etc. Life facts may also include sentiment indicators from derived from NLP processing of emails, sms or voice phone calls with the advisor.”; Par. 529”)
Regarding Claim 5, Claim 13 and Claim 20,
further comprising setting the trigger threshold based on the context of the user, the quantized insight index relevant to the current situation of the user, user data, and a knowledge base of historical situations, wherein the historical situations include historical occurrences of issues associated with the current situation of the user and historical values of the quantized insight index at the historical occurrences of the issues ( Wachell Par. 207; Par. 226-“ Labelled events on historical client data may be used in a supervised learning context to predict future events, or if a client's state is not completely observable (we are missing a bank account or life facts like marital status or job status) determine whether it is probable an unobserved event has happened (missing data imputation by supervised learning). In this context a supervised learning craft may be applied to the historical client state using lagged historical data as our independent variables, and an advisor relevant label as the prediction target.”; Par. 326-“ LF=Life Facts Life facts may include demographic details such as age, family status, employment status, etc. Life facts may also include sentiment indicators from derived from NLP processing of emails, sms or voice phone calls with the advisor.”)
Regarding Claim 6 and Claim 14,
Wachell teaches insight analysis and the feature is expounded upon by Siebel:
wherein the values associated with the user for the set of indicators that the quantized insight index is based on are monitored via one or more sensors including one or more of a temperature sensor, a camera, a timer, a key logger, and a microphone. (Siebel Par. 212-“ The opportunity-level machine learning model 402 also processes customer engagement data 408 defining interactions of the company with actual or potential customers for individual opportunities. This data 408 may include, for example, information related to emails, text messages, voice calls, video conferences, calendar appointments, and other interactions of representatives or other personnel with the actual or potential customers.; Par. 342-“ For at least some of the CRM-related functions described above, natural language processing (NLP) can be applied to analyze text, voice, and other data sources. For example, natural language processing can be applied to various representative-customer communications and other information, such as call logs, e-mails, text messages, news, social media content, annual reports, and analyst reports, to identify features used as inputs to machine learning models.”; Par. 436);
Wachell and Siebel are directed to market analysis. It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have improve upon data analysis of Wachell, as taught by Siebel, by utilizing additional insight analysis with a reasonable expectation of success of arriving at the claimed invention. One of ordinary skill in the art would have been motivated to make the modification to the teachings of Wachell with the motivation of improving performances and/or performing other remediating actions (Siebel Par. 105).
Regarding Claim 7 and Claim 15,
wherein implementing the mediating output action includes controlling one or more components of the enterprise system to produce an output directed to the user. ( Wachell (Par. 74-“Enterprise application 304 monitors portfolios to ensure they are correctly implemented according to governance, and will notify the PM or Compliance of any exception”; Par. 277-“An action a may be defined as the choice to rebalance a portfolio to a particular model (but not necessarily the internal management of the model itself) of investment or insurance assets, and/or a decision to contribute or withdraw capital from a portfolio from or to a cash operating account. An action a can combine a model portfolio (MP) rebalance and goal plan (GP) as sub-actions.”)
Regarding Claim 8 and Claim 16,
wherein a machine learning model trained on historical occurrences of issues associated with the current situation of the user and historical values of the quantized insight index at the historical occurrences of the issues utilizes the current value of the quantized insight index as input to forecast the issue. ( Wachell (Par. 374-377- “some embodiments, event detection, and surfacing of client state to advisors is used towards constructing a learning model for wealth management. Event detection in client state may achieve two milestones: 1) A Next Best Action iterative improvement loop (A functioning auditable flow of insights from data to advisor and client back with an insight feedback mechanism); and 2) The construction of models which formally encode the decision making process of the advisor based on a well understood inputs and outputs.”; Par. 391-“ Modelling and training would require a historical data dump to be processed for training on a monthly or quarterly basis. No PII data or even silhouetted data would be preserved in the model, but under a very strict interpretation of “right to forget” in which a trained AI model retains “residual” memory of a user, it would be possible to purge the model of “residue” from withdrawers at the monthly or quarterly cadence.”)
Regarding Claim 9,
Wachell teaches An apparatus comprising: a processor; and memory storing instructions that, when executed by the processor, cause the processor to: monitor … (Wachell Par. 60; Par. 6)
select a quantized insight index relevant to a current situation of the user based on the context of the electronic communication (Wachell Par. 23; Par. 208-“The advisor can use feedback mechanisms diagrammed in FIG. 2 and FIG. 5D to rate the event highly relevant (resolved, contact client), somewhat relevant (snooze) or not relevant. These labels could, for example, correspond to multinomial categories or values on the unit interval that could then be used in standard regression, supervised learning and or generalized linear models. The bias for false positives or false negatives, recall, precision, etc. could also be adjusted by supervisors for events deemed more or less important at an enterprise level.”; Par. 209-211; Par. 326- “LF=Life Facts-Life facts may include demographic details such as age, family status, employment status, etc. Life facts may also include sentiment indicators from derived from NLP processing of emails, sms or voice phone calls with the advisor.”);
monitor the channel of the communications network to assign respective values to a set of indicators corresponding to the quantized insight index and the user (Wachell Par. 23-“In some embodiments, the processor updates the event rules and next best action data based on the feedback data by adjusting a priority or rank of the next best action data in terms of advisor relevance, wherein the feedback data is indicative of the advisor relevance.”; Par. 30-32; Par. 68-69; Par. 507-“ In some embodiments of the process 3000, the feedback data rates the Next Best Action as highly relevant, relevant, somewhat relevant or not relevant. The direct advisor feedback (relevant, not relevant), or implicit feedback (examined, unexamined) or (account action, no account action) or (communication initiated, no communication initiated) or (money moved, non money movement) can be used to attenuate either 1) the hyperplane for the event as a categorizer 2) the rank relevance of the Next Best Action (NBA) for the advisors priority dashboard.”);
generate a current value of the quantized insight index for the user based on the respective values of the set of indicators corresponding to the quantized insight index; (Wachell Par. 371-“A portfolio user 202, such as an advisor, may be concerned with managing and advising client strategies and accounts, and receives guidance from wealth management system 200 in the form of Next Best Actions and in a dashboard of enterprise application 304 in order to guide decision making and operations Client telemetry may provide evidence which supports the next best action. For a simple event, this might be a timeseries chart of the threshold univariate value. For a predictive model, this might be the most important input features, OR a curated list of timeseries that the researcher felt were relevant to the advisor”; Par. 507)
determine that the current value is equal to, or greater than, a trigger threshold and in response executing, by a processor, state improvement code that causes the enterprise in real time or near real-time to : (Wachell Par. 207-208-“Using a representation like FIG. 16 to randomly, parametrically, or manually build a binary univariate thresholding function on a single feature, or multivariate separating hyperplane on salient features from client state, it may be possible to build a model that will detect labelled events that are relevant to a wealth advisor. Based on some initialization of the threshold/hyperplane, an event will be detected and dispatched to the advisor. The advisor can use feedback mechanisms diagrammed in FIG. 2 and FIG. 5D to rate the event highly relevant (resolved, contact client), somewhat relevant (snooze) or not relevant.”
input the current value of the quantized insight index into an issue forecaster model to identify an issue with the electronic communication between the user and the user interface of the enterprise system (Par. 210-211; Par. 228-“In some embodiments, the supervised learning pipeline will consist of a “data pipeline”, consisting of (1) data loading, (2) preprocessing and feature estimation, (3) supervised variable screening to reduce data dimensionality, (4) classification (or probabilistic classification) to predict which elements in the target variable will be zero, and which nonzero (or the likelihood of each), (5) magnitude prediction for nonzero target variable predictions, (6) k-step ahead prediction using the model fit using the previous steps (and sometimes re-fit for each new time point).”; Par 233; Par- 466),…);
monitor the channel of the communications network to update the respective values of the set of indicators (Wachell Par. 39-“updating the event rules and the next best action data based on the feedback data using the processor; and automatically updating the display of the interface dashboard based on the updated event rules.”);
and determining that the updated value … the trigger threshold. (Wachell Par. 200-201-“ Quasiperiodicity/Periodicity Analysis focuses on the questions of “is it a cycle?” or “is it a habit?”. Payroll is by design a cycle. Groceries or “retail therapy” are to lesser or greater extents habits. Analysis of season, trend, cycle, and quasiperiodic components (e.g., inter-event intervals) may be achieved through windowed statistics describing periodic and quasiperiodic statistics (quasiperiodic refers to a regular pattern of recurrence with a random component that does not lend itself to precise, deterministic characterization) as well as multivariate approaches appropriate for periodic and quasiperiodic data (windowed cross-correlation, cross-recurrence, vector autoregressive, deep neural networks, recurrent neural networks). This is mostly in service of feature engineering for further predictive analysis.”; Par. 207-208-“Using a representation like FIG. 16 to randomly, parametrically, or manually build a binary univariate thresholding function on a single feature, or multivariate separating hyperplane on salient features from client state, it may be possible to build a model that will detect labelled events that are relevant to a wealth advisor. Based on some initialization of the threshold/hyperplane, an event will be detected and dispatched to the advisor. The advisor can use feedback mechanisms diagrammed in FIG. 2 and FIG. 5D to rate the event highly relevant (resolved, contact client), somewhat relevant (snooze) or not relevant.”).
Wachell teaches tracking client behavior and the feature is expounded upon by Kim
… monitoring, in real-time using a biometric monitor comprising a microphone and at least one of an infrared sensor or a camera, a channel of a communications network communicatively coupled to an interface of multimedia I/O devices for an initial biometric indicator …(Kim Par. 9-10- Various embodiments provide a method and apparatus for analyzing, based on various types of context information (e.g., a time, a place, a device usage log, etc.), biometric data collected by multiple electronic devices, storing related insight information, and recognizing a user context to provide coaching to the user through the insight information appropriate for corresponding context. An electronic device according to various embodiments of the disclosure may include: a sensor module, a display device, and a processor, wherein the processor: acquires, using the sensor module, biometric information of a user and place information related to the user; matches the biometric information with the place information; displays an interface including biometric information for a predetermined period of time through the display device; determines a place of a region selected by the user and a duration corresponding to the place in the interface; and specifies the duration and displays biometric information by highlighting the biometric information within the duration in the interface.”; Par. 62; )
… generate a mediating output action comprising a change to the user interface of an enterprise system to select a communication channel for the user, (ii) indicating an offer for a discounted product, or (iii) connecting the user with a representative, and implement the change to the user interface (Kim Par. 255-“ According to an embodiment, the function objects 1751, 1753, 1755, and 1760 may include one or more objects 1751, 1753, and 1755 related to users recommended by the electronic device 101, and a contact object 1760 which allows the user to directly select a person to talk to (or a person to talk on the phone), based on the contact information. According to an embodiment, the function objects 1751, 1753, and 1755 related to the recommended users may be provided based on users each having a high priority among recommended users, wherein as many objects as the number of the recommended users may be provided. The electronic device 101 may provide only the contact object 1760 when there is no recommended user.; Par. 97; Par. 230)
Wachell and Kim are directed to context monitoring analysis. It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have improve upon analysis of Wachell, as taught by Kim, by utilizing additional context analysis with a reasonable expectation of success of arriving at the claimed invention. One of ordinary skill in the art would have been motivated to make the modification to the teachings of Wachell with the motivation of using biometric information measured at the time point (Kim Par. 5).
Wachell in view of Kim teaches tracking client behavior and the feature is expounded upon by Tortoriello:
… monitor, in real-time or near-real-time, a channel of a communications network for an initial indicator in order to determine a context of an electronic communication between a user and a user interface of to an enterprise system (Tortoriello- Par. 18-19“As described herein, a dialogue monitoring and communications system using artificial intelligence (AI) and analytics-based machine learning may provide a more robust approach and solution. The dialogue monitoring and communications system described herein may collect and monitor customer dialogues and better identify customer touchpoints before, during, and after a transaction. For example, the dialogue monitoring and communications system may better track customer interactions across all channels and dimensions. This may enable more focus on pattern identification for process optimization. Also, the dialogue monitoring and communications system, as described herein, may help identify customer groups based on omnichannel behaviors, which may further enable a merchant to shape and design a more dedicated and personalized dynamic experience for each identified customer group. The dialogue monitoring and communications system may also execute various customer experience strategies through real-time or near real-time personalization driven by customer-centric actions.”);
Wachell, Kim and Tortoriello are directed to insight analysis. It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have improve upon data analysis of Wachell in view of Kim, as taught by Tortoriello, by utilizing additional insight analysis with a reasonable expectation of success of arriving at the claimed invention. One of ordinary skill in the art would have been motivated to make the modification to the teachings of Wachell in view of Kim with the motivation of improve the overall customer journey and experience (Tortoriello Par. 5).
Wachell in view of Kim in further view of Tortoriello teach insight analysis and the feature is expounded upon by Siebel:
generate an updated value of the quantized insight index based on updated values of the set of indicators corresponding to the quantized insight index (Siebel Par. 115-“In addition, the model orchestrator function 242 may create actionable recommendations for representatives to improve their machine learning scores to thereby improve their chances of achieving specified objectives (such as closing an opportunity or mitigating churn risk). ... Par. 123; Par. 135-“ Also, in some embodiments, the continuous analytics processing may use map reduce, iterative processing, or any other processing paradigm to process the data when a change in data is detected. In addition, in some embodiments, continuous analytics processing may perform processing for only a sub-portion of an analytic. For instance, some calculations may be updated based only on a changed or new value and, thus, not all calculations that go into an analytic may need to be recalculated. Only those that are impacted by the change may be recalculated to save resources and time.”; Par. 226-227-“… For each contributing feature, the outputs from the opportunity-level machine learning model 402 may further include the following: a current value of the feature, a change in the value of the feature since the last output of the model 402 was generated, and a polarity of the change in the value of the feature (positive or negative) since the last output of model 402 was generated. Of course, these outputs of the model 402 are for illustration only and can vary as needed or desired.”; Par. 236);
… is less than the trigger threshold (Siebel Par. 173- “In some embodiments, a data flow event is a combination of an analytic defining what is being measured, a period defining the period of a time-series to be analyzed, and an interval that defines a granular for aggregation. In addition, analytics may specify a completeness threshold for a data flow event that defines how much of the potential data for a period has been collected so far. ; Par. 389);
Wachell, Kim, Tortoriello and Siebel are directed to market analysis. It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have improve upon data analysis of Wachell in view of Kim in further view of Tortoriello, as taught by Siebel, by utilizing additional insight analysis with a reasonable expectation of success of arriving at the claimed invention. One of ordinary skill in the art would have been motivated to make the modification to the teachings of Wachell in view of Kim in further view of Tortoriello with the motivation of improving performances and/or performing other remediating actions (Siebel Par. 105).
Regarding Claim 17,
Wachell teaches At least one non-transitory computer-readable storage medium storing computer-executable program code instructions that, when executed by a computing apparatus, cause the computing apparatus to: monitoring … (Wachell Par. 60; Par. 6; Par. 527)
select a quantized insight index relevant to a current situation of the user based on the context of the electronic communication (Wachell Par. 23; Par. 208-“The advisor can use feedback mechanisms diagrammed in FIG. 2 and FIG. 5D to rate the event highly relevant (resolved, contact client), somewhat relevant (snooze) or not relevant. These labels could, for example, correspond to multinomial categories or values on the unit interval that could then be used in standard regression, supervised learning and or generalized linear models. The bias for false positives or false negatives, recall, precision, etc. could also be adjusted by supervisors for events deemed more or less important at an enterprise level.”; Par. 209-211; Par. 326- “LF=Life Facts-Life facts may include demographic details such as age, family status, employment status, etc. Life facts may also include sentiment indicators from derived from NLP processing of emails, sms or voice phone calls with the advisor.”);
monitor the channel of the communications network to assign respective values to a set of indicators corresponding to the quantized insight index and the user (Wachell Par. 23-“In some embodiments, the processor updates the event rules and next best action data based on the feedback data by adjusting a priority or rank of the next best action data in terms of advisor relevance, wherein the feedback data is indicative of the advisor relevance.”; Par. 30-32; Par. 68-69; Par. 507-“ In some embodiments of the process 3000, the feedback data rates the Next Best Action as highly relevant, relevant, somewhat relevant or not relevant. The direct advisor feedback (relevant, not relevant), or implicit feedback (examined, unexamined) or (account action, no account action) or (communication initiated, no communication initiated) or (money moved, non money movement) can be used to attenuate either 1) the hyperplane for the event as a categorizer 2) the rank relevance of the Next Best Action (NBA) for the advisors priority dashboard.”);
generate a current value of the quantized insight index for the user based on the respective values of the set of indicators corresponding to the quantized insight index; (Wachell Par. 371-“A portfolio user 202, such as an advisor, may be concerned with managing and advising client strategies and accounts, and receives guidance from wealth management system 200 in the form of Next Best Actions and in a dashboard of enterprise application 304 in order to guide decision making and operations Client telemetry may provide evidence which supports the next best action. For a simple event, this might be a timeseries chart of the threshold univariate value. For a predictive model, this might be the most important input features, OR a curated list of timeseries that the researcher felt were relevant to the advisor”; Par. 507)
determine that the current value is equal to, or greater than, a trigger threshold and in response executing, by a processor, state improvement code that causes the enterprise in real time or near real-time to : (Wachell Par. 207-208-“Using a representation like FIG. 16 to randomly, parametrically, or manually build a binary univariate thresholding function on a single feature, or multivariate separating hyperplane on salient features from client state, it may be possible to build a model that will detect labelled events that are relevant to a wealth advisor. Based on some initialization of the threshold/hyperplane, an event will be detected and dispatched to the advisor. The advisor can use feedback mechanisms diagrammed in FIG. 2 and FIG. 5D to rate the event highly relevant (resolved, contact client), somewhat relevant (snooze) or not relevant.”
input the current value of the quantized insight index into an issue forecaster model to identify an issue with the electronic communication between the user and the user interface of the enterprise system (Par. 210-211; Par. 228-“In some embodiments, the supervised learning pipeline will consist of a “data pipeline”, consisting of (1) data loading, (2) preprocessing and feature estimation, (3) supervised variable screening to reduce data dimensionality, (4) classification (or probabilistic classification) to predict which elements in the target variable will be zero, and which nonzero (or the likelihood of each), (5) magnitude prediction for nonzero target variable predictions, (6) k-step ahead prediction using the model fit using the previous steps (and sometimes re-fit for each new time point).”; Par 233; Par.- 466));
monitor the channel of the communications network to update the respective values of the set of indicators (Wachell Par. 39-“updating the event rules and the next best action data based on the feedback data using the processor; and automatically updating the display of the interface dashboard based on the updated event rules.”);
and determining that the updated value … the trigger threshold. (Wachell Par. 200-201-“ Quasiperiodicity/Periodicity Analysis focuses on the questions of “is it a cycle?” or “is it a habit?”. Payroll is by design a cycle. Groceries or “retail therapy” are to lesser or greater extents habits. Analysis of season, trend, cycle, and quasiperiodic components (e.g., inter-event intervals) may be achieved through windowed statistics describing periodic and quasiperiodic statistics (quasiperiodic refers to a regular pattern of recurrence with a random component that does not lend itself to precise, deterministic characterization) as well as multivariate approaches appropriate for periodic and quasiperiodic data (windowed cross-correlation, cross-recurrence, vector autoregressive, deep neural networks, recurrent neural networks). This is mostly in service of feature engineering for further predictive analysis.”; Par. 207-208-“Using a representation like FIG. 16 to randomly, parametrically, or manually build a binary univariate thresholding function on a single feature, or multivariate separating hyperplane on salient features from client state, it may be possible to build a model that will detect labelled events that are relevant to a wealth advisor. Based on some initialization of the threshold/hyperplane, an event will be detected and dispatched to the advisor. The advisor can use feedback mechanisms diagrammed in FIG. 2 and FIG. 5D to rate the event highly relevant (resolved, contact client), somewhat relevant (snooze) or not relevant.”).
Wachell teaches tracking client behavior and the feature is expounded upon by Kim
… monitoring, in real-time using a biometric monitor comprising a microphone and at least one of an infrared sensor or a camera, a channel of a communications network communicatively coupled to an interface of multimedia I/O devices for an initial biometric indicator …(Kim Par. 9-10- Various embodiments provide a method and apparatus for analyzing, based on various types of context information (e.g., a time, a place, a device usage log, etc.), biometric data collected by multiple electronic devices, storing related insight information, and recognizing a user context to provide coaching to the user through the insight information appropriate for corresponding context. An electronic device according to various embodiments of the disclosure may include: a sensor module, a display device, and a processor, wherein the processor: acquires, using the sensor module, biometric information of a user and place information related to the user; matches the biometric information with the place information; displays an interface including biometric information for a predetermined period of time through the display device; determines a place of a region selected by the user and a duration corresponding to the place in the interface; and specifies the duration and displays biometric information by highlighting the biometric information within the duration in the interface.”; Par. 62; )
… generate a mediating output action comprising a change to the user interface of an enterprise system to select a communication channel for the user, (ii) indicating an offer for a discounted product, or (iii) connecting the user with a representative, and implement the change to the user interface (Kim Par. 255-“ According to an embodiment, the function objects 1751, 1753, 1755, and 1760 may include one or more objects 1751, 1753, and 1755 related to users recommended by the electronic device 101, and a contact object 1760 which allows the user to directly select a person to talk to (or a person to talk on the phone), based on the contact information. According to an embodiment, the function objects 1751, 1753, and 1755 related to the recommended users may be provided based on users each having a high priority among recommended users, wherein as many objects as the number of the recommended users may be provided. The electronic device 101 may provide only the contact object 1760 when there is no recommended user.; Par. 97; Par. 230)
Wachell and Kim are directed to context monitoring analysis. It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have improve upon analysis of Wachell, as taught by Kim, by utilizing additional context analysis with a reasonable expectation of success of arriving at the claimed invention. One of ordinary skill in the art would have been motivated to make the modification to the teachings of Wachell with the motivation of using biometric information measured at the time point (Kim Par. 5).
Wachell in view of Kim teaches tracking client behavior and the feature is expounded upon by Tortoriello:
… monitor, in real-time or near-real-time, a channel of a communications network for an initial indicator in order to determine a context of an electronic communication between a user and a user interface of to an enterprise system (Tortoriello- Par. 18-19“As described herein, a dialogue monitoring and communications system using artificial intelligence (AI) and analytics-based machine learning may provide a more robust approach and solution. The dialogue monitoring and communications system described herein may collect and monitor customer dialogues and better identify customer touchpoints before, during, and after a transaction. For example, the dialogue monitoring and communications system may better track customer interactions across all channels and dimensions. This may enable more focus on pattern identification for process optimization. Also, the dialogue monitoring and communications system, as described herein, may help identify customer groups based on omnichannel behaviors, which may further enable a merchant to shape and design a more dedicated and personalized dynamic experience for each identified customer group. The dialogue monitoring and communications system may also execute various customer experience strategies through real-time or near real-time personalization driven by customer-centric actions.”);
Wachell, Kim and Tortoriello are directed to insight analysis. It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have improve upon data analysis of Wachell in view of Kim, as taught by Tortoriello, by utilizing additional insight analysis with a reasonable expectation of success of arriving at the claimed invention. One of ordinary skill in the art would have been motivated to make the modification to the teachings of Wachell in view of Kim with the motivation of improve the overall customer journey and experience (Tortoriello Par. 5).
Wachell in view of Kim in further view of Tortoriello teach insight analysis and the feature is expounded upon by Siebel:
generate an updated value of the quantized insight index based on updated values of the set of indicators corresponding to the quantized insight index (Siebel Par. 115-“In addition, the model orchestrator function 242 may create actionable recommendations for representatives to improve their machine learning scores to thereby improve their chances of achieving specified objectives (such as closing an opportunity or mitigating churn risk). ... Par. 123; Par. 135-“ Also, in some embodiments, the continuous analytics processing may use map reduce, iterative processing, or any other processing paradigm to process the data when a change in data is detected. In addition, in some embodiments, continuous analytics processing may perform processing for only a sub-portion of an analytic. For instance, some calculations may be updated based only on a changed or new value and, thus, not all calculations that go into an analytic may need to be recalculated. Only those that are impacted by the change may be recalculated to save resources and time.”; Par. 226-227-“… For each contributing feature, the outputs from the opportunity-level machine learning model 402 may further include the following: a current value of the feature, a change in the value of the feature since the last output of the model 402 was generated, and a polarity of the change in the value of the feature (positive or negative) since the last output of model 402 was generated. Of course, these outputs of the model 402 are for illustration only and can vary as needed or desired.”; Par. 236);
… is less than the trigger threshold (Siebel Par. 173- “In some embodiments, a data flow event is a combination of an analytic defining what is being measured, a period defining the period of a time-series to be analyzed, and an interval that defines a granular for aggregation. In addition, analytics may specify a completeness threshold for a data flow event that defines how much of the potential data for a period has been collected so far. ; Par. 389);
Wachell, Kim, Tortoriello and Siebel are directed to market analysis. It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have improve upon data analysis of Wachell in view of Kim in further view of Tortoriello, as taught by Siebel, by utilizing additional insight analysis with a reasonable expectation of success of arriving at the claimed invention. One of ordinary skill in the art would have been motivated to make the modification to the teachings of Wachell in view of Kim in further view of Tortoriello with the motivation of improving performances and/or performing other remediating actions (Siebel Par. 105).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure: US Patent No. 10157358 B1 to Brisebois et al.- Abstract-“ In one embodiment, a method includes receiving a future virtual-meeting time interval and a set of virtual-meeting attributes in relation to a future virtual meeting. The method also includes correlating the future virtual-meeting time interval to at least one recurring time interval of a time map. In addition, the method includes retrieving a time-based performance pattern. The method further includes determining, from the time-based performance pattern, a stream quality for the virtual-meeting attributes at the at least one recurring time interval. In addition, the method includes publishing information related to the determined stream quality to a requestor.”
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Chesiree Walton, whose telephone number is (571) 272-5219. The examiner can normally be reached from Monday to Friday between 8 AM and 5 PM. If any attempt to reach the examiner by telephone is unsuccessful, the examiner’s supervisor, Patricia Munson, can be reached at (571) 270-5396. The fax telephone numbers for this group are either (571) 273-8300 or (703) 872-9326 (for official communications including After Final communications labeled “Box AF”).
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Sincerely,
/CHESIREE A WALTON/ Examiner, Art Unit 3624
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