Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Response to Arguments
Applicant’s arguments, see pages 8-12, filed on 15/19/2026, with respect to the rejection(s) of claim(s) 1-20 under 35 U.S.C. 103 as being unpatentable over Bathe et al (US 20220245530 A1 --- hereinafter—Bathe) in view of WANG et al. (US 20220245393 A ---hereinafter--"WANG”) have been fully considered and are persuasive. Therefore, the rejection has been withdrawn. However, upon further consideration, a new ground(s) of rejection is made in view of MILLERAND et al. (US 20220124002 A1—hereinafter—"MILLERAND”).
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 (i.e., changing from AIA to pre-AIA ) 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 Bathe et al (US 20220245530 A1 --- hereinafter—Bathe) in view of WANG et al. (US 20220245393 A ---hereinafter--"WANG”) in further view of MILLERAND et al. (US 20220124002 A1—hereinafter—"MILLERAND”).
As per claim 1: Bathe discloses a method for managing a prediction model ([0018] generating one or more feature vectors for a user, the one or more feature vectors at least comprising transaction-based features and slot-based features; generating, using a machine learning architecture, a repurchase prediction for the user based, at least in part, on the one or more feature vectors; generating, using the machine learning architecture, a time slot prediction for the user based, at least in part, on the one or more feature vectors, the time slot prediction predicting a time slot desired by the user for an upcoming transaction; and executing a reservation function that facilitates reserving of the time slot for the user), comprising:
obtaining gradient information associated with the prediction model based on sample data for a time slot in a predetermined time period ([0066] The machine learning architecture 350 can be configured to analyze the historical data 320 (including the slot selection data 420) to generate repurchase predictions 351 indicating whether or not the users 305 are likely to place or initiate new transactions 321 within an upcoming time period 455, and time slot predictions 352 indicating the time slots 370 the users 305 will prefer in connection with placing or initiating these transactions 321. The machine learning architecture 350 can utilize various machine learning models to generate these predictions. Exemplary machine learning models can include a random forest decision tree model 451, a logistic regression model 452, and gradient boosted tree model 453);
acquiring an offset of the time slot in the predetermined time period ([0085] The random forest decision tree model 451 can be configured for multi-class prediction to generate the time slot predictions 352. The machine learning architecture 350 may additionally, or alternatively, use a heuristics approach to generate the time slot predictions 352. For each user, the heuristics approach can identify a frequency parameter (e.g., indicating the most frequent slot chosen by the user based on the historical data 320 stored for the user) and a recency parameter (e.g., indicating the most recent time slot chosen by the user based on the historical data 320 stored for user). These parameters may then be used as baselines to generate the time slot predictions 352. Note that: The frequency parameter and recency parameter are used as a baselines (offset) to generate the time slot predictions); and
updating a parameter of the prediction model based on the gradient information, the weight, and historical gradient information, the historical gradient information is determined based on historical sample data for a group of historical time slots before the time slot (([0072] The random forest decision tree model 451, logistic regression model 452, and/or gradient boosted tree model 453 can be trained on feature vectors 460 (including the transaction-based features 461 and slot-based features 462) generated from several months of historical data 320 for a large number of users 305 (e.g., thousands or millions of users 305). The performance of these models can be evaluated based on the purchases of the same set of users 305 in a subsequent time period (e.g., k days or weeks) following the several months of data used for training. The performance of these models can be evaluated against different frequency and recency-based baseline heuristics, e.g., which may indicate the most frequent time slot 370 chosen by the users 305 in the historical data 320 and the most recent time slot 370 chosen by the users 305).
Bathe does not explicitly disclose determining a step size for the updated parameter. WANG, in analogous art however, discloses determining a step size for the updated parameter ([0041] The dynamic interval function increases (increasing sampling frequency) as the gradient increases. The sampling frequency can decrease when the gradient decreases. The degree (gain) to which the sample frequency changes as a function of the gradient can be arbitrarily defined depending on the needs of a particular application. Moreover, the change in sampling can be rough (predefined steps) or smooth (arbitrary steps) based on the slope of the gradient. [0045] A revision metric is calculated based on the gradient and the deviation at each time step: Revise(t)=revise(d(t), g.sub.t)). The revision metric can evaluate to “true” or “false”, indicating that the gradient is enough (e.g., sharply negative but just above the L threshold; sharply positive but just below the H threshold, etc.) to trigger a judgment adjustment. [0049] Having multiple data sets “k” against which deviations are calculated, the compare module 212 can include further functionality, such as a precheck module 600 that orders the data sets in terms of importance prior to passing those real data-prediction data pairs to a compare block 602. FIG. 6 Precheck module 600 that can be incorporated within or prior to the compare module 212. Deviations D.sub.1, D.sub.2, . . . , D.sub.k for k data sets can be reordered using an importance precheck 604 (also referred to as an importance-weighted order) into the list D.sub.i, D.sub.l, D.sub.b, . . . , D.sub.s. In some embodiments of the invention, ordering of the k data sets can be by magnitude of the individual deviations (d.sub.i) for each time step. For example, the deviations can be reordered such that the most deviant data set is first (most critical) and the least deviant data set is last (least critical). Only the real data-predicted data pairs for the top “x” deviations in terms of magnitude are passed to a compare block 602, reducing the computational load and time latency of the system. Each real-prediction pair (R.sub.i-P.sub.i) passed through to the compare block 602 is evaluated to determine whether the deviation d for the pair is >H, <L, or L<d<H. If a current real-prediction pair (RN-PN) for a dataset “N” is within the L and H thresholds, the next real-prediction pair (R.sub.N+1-P.sub.N+1) for the next dataset “N+1” is checked until a pair is found having a deviation >H or <L. Once found, the corresponding dataset (here, N+1) is deemed most critical for a next judgment, and the associated real-prediction pair is passed to the compare module 212 and/or the compare block 502 (FIG. 5). This process of selectively making judgments on only the most critical data sets (which dynamically change over time depending on each data set's deviation value at each time step) or only the “x” most critical data sets can continue indefinitely and the number of “x” data sets chosen for judgments can be adjusted based on the computational availably or time latency constraints of a given application. For example, in applications having strict time latency constraints (judgments must be made as quickly as possible) the number “x” can be lowered. The value of “x” can be dynamically adjusted, such as, for example, lowered when the system cannot achieve predetermined time latency constraints at the current value for “x”. In this manner a tradeoff can be made between computational rigor and platform-specific timing requirements). Therefore, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the invention to modify the claimed limitations of the updated parameter disclosed by Bathe to include determining a step size for the updated parameter. This modification would have been obvious because a person having ordinary skill in the art would have been motivated by the desire to provide products to dynamically evaluate the acceptability of a model by generating a plurality of predictions that each define a plurality of future inputs for the model. A deviation curve can be generated by determining a distance between each prediction of the plurality of predictions and a respective known data point of a plurality of known data points. One or more points in the deviation curve are sampled and the sampled points are compared to a low threshold and a high threshold. A judgement is determined for each prediction to determine whether the respective prediction will be accepted or denied as an input to the model. The future inputs for the model are modified based on the judgments as suggested by WANG ([0001- [0004]).
Bathe and WANG do not explicitly disclose wherein the offset represents a difference between the time slot and a first time slot in the predetermined time period and determining a weight for historical gradient information based on the offset. MILLERAND, in analogous art however, discloses wherein the offset represents a difference between the time slot and a first time slot in the predetermined time period ([0159] The system is adapted to a dynamic process, so that one or more batches 21 of data samples ranging over a time interval 210 are completing previously obtained and kept data samples 20. For example, in regularly and possibly periodically gathering data in a given environment (e.g. every 10 minutes, half-hour or hour), each batch 21 of data may correspond to newly collected input and output data at a given time slot or at successive time slots (e.g. at 5 or 10 successive time slots). For sake of convenience, the time interval 210 is considered to start at the end of the previous data recording and to finish with the lastly collected data sample. [0163] The system 1 is thus relying on the data samples of the new batches 21 and on previously kept data samples 20, distributed over the part of the time window 230 preceding the time interval 210. The previously kept samples 20 extend however below the time window 230, and further covers past samples 25 older than the start of the time window 230 and distributed over a time interval 250. As a whole, a processing time range 220 comprising the time window 230 and the contiguously preceding time interval 250 is taken into account by the system 1); determining a weight for historical gradient information based on the offset ([0043] Gradient boosting is an ML method that combines iteratively weak ML prediction models (such as e.g. Decision Trees) into a single stronger ML prediction model, by optimizing a cost function over a function space by an iterative gradient descent (i.e. by iteratively choosing a function pointing in the negative gradient direction); wherein the weight increases with the offset ([0182] In variant implementations, each of the samples belonging to the complementary share 27 in the leaf 530 is pre-weighted further to the decision tree construction, as an increasing function of the number of the past samples 25 in the leaf 530. The related weights are then increased whenever one of the current samples 321 arrives. Once a preset deletion threshold is reached for any of the weighted samples 281, the latter is removed). Therefore, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the invention to modify the claimed limitations of the offset and the predetermined time period disclosed by Bathe to include the offset represents a difference between the time slot and a first time slot in the predetermined time period and determining a weight for historical gradient information based on the offset. This modification would have been obvious because a person having ordinary skill in the art would have been motivated by the desire to creates a controlled mix of time-based retention and similarity-based retention to preserve both recent behavior and unusual historical cases. Selected samples are then fed to downstream machine learning for power management, including failure detection and dynamic adaptation. A remember/forget balance can be set manually or tuned automatically by validation on successive time positions as suggested by MILLERAND ([0029- 0031]).
As per claim 2: Bathe in view of WANG in further view of MILLERAND disclose the method according to claim 1, wherein determining the step size comprises:
generating the step size based on the gradient information, the historical gradient information, and the weight for the historical gradient information (WANG [0020] the sampling interval itself can be dynamically adjusted according to calculated changes in a gradient (slope) at a sampling point in the deviation curve. For example, a large gradient can indicate an acceleration or deceleration of the gap at a future sampling point. This gradient data can be leveraged to make improved judgments (e.g., denying a prediction that would otherwise be acceptable because the gradient at that sampling point indicates that the gap is accelerating).
As per claim 3: Bathe in view of WANG in further view of MILLERAND disclose the method according to claim 2, wherein the weight is within a predefined area (WANG [0051] an example workflow 700 for evaluating model acceptability in accordance with one or more embodiments. As shown in FIG. 7, a system for evaluating model acceptability can include the prediction module 208, the sampling module 210, and the compare module 212 discussed previously. FIG. 7 further illustrates the data flows over time when evaluating model acceptability. In particular, judgments at times t−n, t−1, t, t+n−1, and t+n are provided for scenarios Deny, Recheck, and Accept. In FIG. 7, “m” is the sampling interval, “n” is the prediction interval, “f(x)” is the sampling function, “D” is the deviation, “L” is the Low deviation threshold, “H” is the High deviation threshold, “R.sub.p” is the prediction data result, and “R.sub.r” is the real data result).
As per claim 4: Bathe in view of WANG in further view of MILLERAND disclose the method according to claim 2, wherein generating the step size comprises:
determining an intermediate parameter associated with the time slot based on the gradient information and a weighted historical gradient information that is determined based on the historical gradient information and the weight (WANG [0040] The sampling module 210 is configured to sample the deviation values (Di) at one or more times “t.sub.0 . . . t.sub.N”. In some embodiments of the invention, the sampling frequency (time between samples) can be predetermined (e.g., every 5 seconds, 10 minutes, hour, etc.). In some embodiments of the invention, the sampling frequency can be dynamically calculated. For example, timestamp t.sub.i+1 for a next sample can occur at timestamp t.sub.i+a dynamic interval m(g.sub.i), resulting in the sampling interval formula: f.sub.i+1=sample(t.sub.i+m(g.sub.i)) where t.sub.i is the last sampling timestamp, g.sub.i is the last sampling point gradient, and m(g.sub.i) is the sampling interval function defined as m(g.sub.i)=dyn_interval(g.sub.i−1, g.sub.i) where “g” is the gradient of the deviation curve calculated at the time t.sub.i); and
creating the step size based on the intermediate parameter and the gradient information (WANG [0041] the dynamic interval function increases (increasing sampling frequency) as the gradient increases. Likewise, the sampling frequency can decrease when the gradient decreases. The degree (gain) to which the sample frequency changes as a function of the gradient can be arbitrarily defined depending on the needs of a particular application. Moreover, the change in sampling can be rough (predefined steps) or smooth (arbitrary steps) based on the slope of the gradient; WANG [0042] The Deviation Curve 402 illustrated in FIG. 4 provides example dynamic sampling points taken along the sampling lines “f” along the curve “d” and their associated calculated gradients at times t.sub.0, t.sub.1, t.sub.2, t.sub.i+1, etc. As shown in FIG. 4, the sampling rate increased (the dynamic interval decreased) during the interval t.sub.i, t.sub.i+1 due to the relatively high gradient g.sub.i−1 at time t.sub.i−1. It should be understood that the exact gradient slope required for a given change in sampling frequency can be arbitrarily defined. In some embodiments of the invention, the dynamic interval function itself is predefined. For example, each 5% increase in the gradient can result in a 5% (or 10%, etc.) increase in sample frequency, although a linear function is merely used for illustration and non-linear functions are also within the scope of the invention).
As per claim 5: Bathe in view of WANG in further view of MILLERAND disclose the method according to claim 4, wherein creating the step size comprises:
obtaining an attenuation factor for the intermediate parameter based on the offset (WANG [0037] the sampling module 210 is configured to continuously or periodically calculate a deviation value (D.sub.i) for each prediction (P.sub.i) based on the known real data (R.sub.i) at the time corresponding to each prediction. In some embodiments of the invention, the deviation values (D.sub.i) are taken as the error between the prediction (P.sub.i) and the known real data (R.sub.i) according to the error formula: (R.sub.i−P.sub.i)/R.sub.i, although it is understood that any other suitable deviation measure can be used (absolute difference, percent difference, etc.). In some embodiments of the invention, the deviation values (D.sub.i) are plot over time to define a deviation curve, such as the Deviation Curve 402 illustrated in FIG. 4 and corresponding to the Sampling Curve 400 discussed previously); and
determining the step size based on the gradient information and an attenuated intermediate parameter that is determined based on the intermediate parameter and the attenuation factor (WANG [0040] In some embodiments of the invention, the sampling module 210 is configured to sample the deviation values (Di) at one or more times “t.sub.0 . . . t.sub.N”. In some embodiments of the invention, the sampling frequency (time between samples) can be predetermined (e.g., every 5 seconds, 10 minutes, hour, etc.). In some embodiments of the invention, the sampling frequency can be dynamically calculated. For example, timestamp t.sub.i+1 for a next sample can occur at timestamp t.sub.i+a dynamic interval m(g.sub.i), resulting in the sampling interval formula: f.sub.i+1=sample(t.sub.i+m(g.sub.i)) where t.sub.i is the last sampling timestamp, g.sub.i is the last sampling point gradient, and m(g.sub.i) is the sampling interval function defined as m(g.sub.i)=dyn_interval(g.sub.i−1, g.sub.i) where “g” is the gradient of the deviation curve calculated at the time t.sub.i).
As per claim 6: Bathe in view of WANG in further view of MILLERAND disclose the method according to claim 1, wherein obtaining the gradient information comprises:
obtaining a prediction for a label portion in the sample data based on a data portion in the sample data and the prediction model (Bathe [[073] The prediction for a new sample X (e.g., which may include the feature vector associated with a user) is obtained by taking the majority vote of the n predictions from n trees. Each leaf corresponding to X contains the majority vote of the labels from the training samples which belong to that leaf);
determining a loss between the prediction for the label portion and the label portion (Bathe [0082] The machine learning architecture 350 can additionally, or alternatively, utilize a gradient boosted tree model 453 to generate the repurchase predictions 351 and/or time slot predictions 352. The gradient boosted tree model 453 can include an ensemble of weak classifiers and may be implemented with decision trees in some cases);and
acquiring the gradient information based on a gradient of the loss and the parameter of the prediction model (Bathe [0083] During training, the gradient boosted tree model 453 receives the feature vector 460 for the users and corresponding labels y, and learns a sequence of K decision trees via a boosting method. The parameters of the gradient boosted tree model 453, which include tree depth and K number of decision trees, can be declared at the beginning of the training process).
As per claim 7: Bathe in view of WANG in further view of MILLERAND disclose the method according to claim 6, wherein the data portion represents features associated with a user and an object, the label portion represents an event between the user and the object, and the predetermined time period has a length of one or more days (Bathe [0080] the random forest decision tree model 451 can be configured for multi-class prediction using random forests. Unlike a binary prediction problem such as the purchase prediction model above (e.g., which predicts whether or not the user will purchase in the next k days in a binary fashion), the random forest decision tree model 451 can be configured to address a multi-class problem that chooses between multiple outcomes or classes to generate time slot predictions. In this scenario, each time slot (e.g., 1-2 PM on Date 1, 2-3 PM on Date 1, etc.) may represent a class. The same or similar historical data and feature vectors described above may be used to train the model and to generate the time slot predictions. For example, exemplary feature vectors that are used to train the model can include slot-based features indicating any or of the following features: a number of times each time slot was chosen; number of times each time slot was chosen as a ratio of total number of orders; the last time or occurrence each time slot was chosen; whether a user prefers time slots on weekends or weekdays; and/or whether the user prefers a morning or evening time slot).
As per claim 8: Bathe in view of WANG in further view of MILLERAND disclose the method according to claim 1, further comprising determining the historical gradient information by:
obtaining respective gradient information based on respective historical sample data for the group of historical time slots before the time slot (Bathe [0084] The output of the gradient boosted tree model 453 can include a repurchase prediction 351 that indicates the probability and/or likelihood that each user will repurchase items and/or place a new transaction 321 in the next k days. The output can be a value between 0 and 1. The gradient boosted tree model 453 can then learn thresholds for the probabilities based on the distribution of the users and their transactions with the goal of maximizing the confidence in the predictions. The particular threshold chosen can vary, but can include any number between 0 and 1 that reflects whether users are likely to repurchase in the next k days. In some exemplary cases, the threshold may be set to 0.5, 0.7, 0.8, or 0.9); and
acquiring the historical gradient information based on the obtained respective gradient information (Bathe [0085] the random forest decision tree model 451 can be configured for multi-class prediction to generate the time slot predictions 352. The machine learning architecture 350 may additionally, or alternatively, use a heuristics approach to generate the time slot predictions 352. For each user, the heuristics approach can identify a frequency parameter (e.g., indicating the most frequent slot chosen by the user based on the historical data 320 stored for the user) and a recency parameter (e.g., indicating the most recent time slot chosen by the user based on the historical data 320 stored for user). These parameters may then be used as baselines to generate the time slot predictions 352).
As per claim 9: Bathe in view of WANG in further view of MILLERAND disclose the method according to claim 8, wherein acquiring the historical gradient information comprises:
determining respective squares of respective gradient information associated with the respective historical time slots in the group of historical time slots, the group of historical time slots being within the predefined time period (Bathe [0058] The machine learning architecture 350 (or other component of the system 300) may initially analyze the historical data 320 to identify a subset of users who routinely and/or regularly utilize the electronic platform 330 and/or who routinely and/or regularly conduct transactions 321 for particular types of items (e.g. groceries and/or household items). The machine learning architecture 350 and scheduling system 360 can receive this list of active users and execute the functions described herein (e.g., related to generating repurchase predictions 351, generating time slot predictions, and executing reservation functions 361) for these users 305);
determining the historical gradient information based on a sum of the respective squares (Bathe [0083] During training, the gradient boosted tree model 453 receives the feature vector 460 for the users and corresponding labels y, and learns a sequence of K decision trees via a boosting method. The parameters of the gradient boosted tree model 453, which include tree depth and K number of decision trees, can be declared at the beginning of the training process. During inference, prediction ŷ of each individual tree i in an ensemble of size K is computed based on the feature vector 460. In this algorithm, the predictions can be updated such that the sum of our residuals is close to 0 (or minimum) and predicted values are sufficiently close to the actual values. The gradient boosted tree model 453 can use multiple weak learners to reduce the prediction error before it outputs the class for the repurchase predictions 351 indicating whether the customer will purchase in the next k days).
As per claim 10: Bathe in view of WANG in further view of MILLERAND disclose the method according to claim 1, further comprising: updating the parameter of the prediction model with the step size (WANG [0085] the random forest decision tree model 451 can be configured for multi-class prediction to generate the time slot predictions 352. The machine learning architecture 350 may additionally, or alternatively, use a heuristics approach to generate the time slot predictions 352. For each user, the heuristics approach can identify a frequency parameter (e.g., indicating the most frequent slot chosen by the user based on the historical data 320 stored for the user) and a recency parameter (e.g., indicating the most recent time slot chosen by the user based on the historical data 320 stored for user). These parameters may then be used as baselines to generate the time slot predictions 352).
As pers claims 11-19: Claims 11-19 are directed to an electronic device, comprising a computer processor coupled to a computer-readable memory unit, the memory unit comprising instructions that when executed by the computer processor implements a method for managing a prediction model, the method having substantially similar corresponding limitations of claims 1-6, 7 and 10, and 8-9 respectively, and therefore 11-19 are rejected with the same rationale given above to reject corresponding limitations of claims 1-6, 7 and 10, and 8-9 respectively.
As per claim 20: Claim 20 is directed to a non-transitory computer program product, the non-transitory computer program product comprising a computer readable storage medium having program instructions embodied therewith, the program instructions executable by an electronic device to cause the electronic device to perform a method for managing a prediction model, the method having substantially similar corresponding limitations of claim 1 and therefore claims 20 is rejected with the same rationale given above to reject corresponding limitations of claim 1.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Ryan et al. (US 20190379589 A1) describes software for finding meaningful patterns in time-series data, such as network traffic, sensor readings, or other measurements recorded over time. It turns the data into moving windows, either as one-dimensional slices or as multi-dimensional matrices. Those windows are used to train machine learning models, including deep neural networks such as CNNs. Once trained, the model reviews new incoming data and looks for patterns tied to a target category, such as an anomaly, congestion, or a future alarm. The system can also tell when in time the pattern likely occurred. Ryan emphasizes that this approach can work with historical data, simulated data, or both. It also discusses using transformations such as heat maps, Fourier transforms, and feature masking to improve detection. In some versions, the system searches for the best transformation and hyper-parameters before selecting the best model.
Naik et al. US 20240154912 A1 describes wireless devices that use machine learning to identify what kind of traffic they are seeing, such as gaming, video conferencing, or XR traffic. A device first collects signaling and extracts features like packet counts, packet timing, packet size, and direction. It then checks whether the traffic looks like a known traffic class, rather than an unfamiliar one. In some examples, it first screens for periodic versus aperiodic traffic, since periodic traffic is more likely to match known real-time classes. If the traffic passes that screen, the device uses an autoencoder or another model to decide whether the traffic class is known. If the traffic is known, a second model predicts the associated application. The device may use the prediction to assign QoS treatment, scheduling, access category, link selection, or power-save behavior. Naik also describes exchanging machine-learning model information between devices so one device can use a model trained by another. It further describes using confidence levels to decide whether to trust the prediction and how aggressively to act on it. The overall goal is to improve traffic classification accuracy and network behavior in Wi-Fi and similar wireless systems.
Rubin et al. US 8520695 B1 describes a vehicle-to-vehicle communication system that repeatedly broadcasts a vehicle’s position, heading, and speed on a fixed schedule. The basic idea is to divide time into short repeating intervals and give each vehicle a time slot for sending its message. The messages are designed to be very short so they fit within a slot and can be received reliably. The system also reserves different parts of the repeating interval for different classes of traffic, such as emergency vehicles and lower-priority messages. It uses a collision-detection and recovery scheme so that if two vehicles try to use the same slot, the conflict can be identified and corrected quickly. Rubin emphasizes broadcast communication rather than point-to-point connections, so every nearby receiver can hear the same safety data. It also proposes a “proxying” feature, where an equipped vehicle can broadcast information about a nearby unequipped vehicle to improve usefulness at low adoption rates
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/TECHANE GERGISO/ Primary Examiner, Art Unit 2408
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