DETAILED ACTION
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 .
Information Disclosure Statement
The information disclosure statements (IDS) submitted on 12/03/2024 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statements are being considered by the examiner.
Double Patenting
The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on nonstatutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP § 2146 et seq. for applications not subject to examination under the first inventor to file provisions of the AIA . A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
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Claims 1-8 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-8 of U.S. Patent No. US 12,004,256 B2. Although the claims at issue are not identical, they are not patentably distinct from each other because claims 1-8 are “anticipated by” claims 1-8 of U.S. Patent No. US 12,004,256 B2.
Application No. 18/733,467 (Instant)
US 12,004,256 B2
1. A server system configured to infer future-communication instructions in a reply message from a user, the server system comprising: one or more hardware processors; and memory storing computer instructions, the computer instructions when executed by the one or more hardware processors configured to perform: receiving a reply from the user of a client device, in response to a communication sent to the client device; evaluating sentiment and context of the reply to infer intent for receiving future communications and to generate a confidence score associated with the intent, the evaluating using a computer model; instructing to adjust a frequency of the future communications based on the inferred intent and the confidence score; receiving feedback, the feedback including an explicit instruction from the user regarding the future communications or including an adjustment of the confidence score; and training the computer model based on the feedback.
2. The server system of claim 1, wherein the sentiment is based on an existence or an absence of profanity within the reply.
3. The server system of claim 1, wherein the sentiment is based on historical behavior associated with the user of the client device.
4. The server system of claim 3, wherein the historical behavior is based on a frequency of opting back in following an instruction to terminate the future communications.
5. The server system of claim 1, wherein the context is based on the communication sent to the client device.
6. The server system of claim 1, wherein the computer model is generated by a trained machine learning component.
7. The server system of claim 1, wherein the evaluating the sentiment and the context of the reply includes evaluating media components within the reply.
8. The server system of claim 1, wherein the computer instructions, when executed by the one or more hardware processors, are configured to perform adjusting the frequency of the future communications based on an ambivalent confidence score.
1. A server system configured to infer opt-out instructions in a reply message from a user, the server system comprising: one or more hardware processors; and memory storing computer instructions, the computer instructions when executed by the one or more hardware processors configured to perform: receiving a reply from the user of a client device, in response to a communication sent to the client device; evaluating sentiment and context of the reply to infer intent whether to opt-out of receiving future communications and to generate a confidence score associated with the intent, the evaluating using a computer model; selectively instructing to terminate the future communications based on the inferred intent and the confidence score; receiving feedback, the feedback including an instruction to opt back in after instructing to terminate the future communications or including an adjustment of the confidence score; and training the computer model based on the feedback.
2. The server system of claim 1, wherein the sentiment is based on an existence or an absence of profanity within the reply.
3. The server system of claim 1, wherein the sentiment is based on historical behavior associated with the user of the client device.
4. The server system of claim 3, wherein the historical behavior is based on a frequency of opting back in following the instructing to terminate the future communications.
5. The server system of claim 1, wherein the context is based on the communication sent to the client device.
6. The server system of claim 1, wherein the computer model is generated by a trained machine learning component.
7. The server system of claim 1, wherein the evaluating the sentiment and the context of the reply includes evaluating media components within the reply.
8. The server system of claim 1, wherein the computer instructions, when executed by the one or more hardware processors, are configured to perform adjusting a frequency of the future communications based on an ambivalent confidence score.
Claims 9-16 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 9-16 of U.S. Patent No. US 12,004,256 B2. Although the claims at issue are not identical, they are not patentably distinct from each other because claims 9-16 are “anticipated by” claims 9-16 of U.S. Patent No. US 12,004,256 B2.
Application No. 18/733,467 (Instant)
US 12,004,256 B2
9. A processor-based method of inferring future-communication instructions in a reply message from a user, the method comprising: receiving a reply from the user of a client device, in response to a communication sent to the client device; evaluating sentiment and context of the reply to infer intent for receiving future communications and to generate a confidence score associated with the intent, the evaluating using a computer model; instructing to adjust a frequency of the future communications based on the inferred intent and the confidence score; receiving feedback, the feedback including an explicit instruction from the user regarding the future communications or including an adjustment of the confidence score; and training the computer model based on the feedback.
10. The processor-based method of claim 9, wherein the sentiment is based on an existence or an absence of profanity within the reply.
11. The processor-based method of claim 9, wherein the sentiment is based on historical behavior associated with the user of the client device.
12. The processor-based method of claim 11, wherein the historical behavior is based on a frequency of opting back in following an instruction to terminate the future communications.
13. The processor-based method of claim 9, wherein the context is based on the communication sent to the client device.
14. The processor-based method of claim 9, wherein the computer model is generated by a trained machine learning component.
15. The processor-based method of claim 9, wherein the evaluating the sentiment and the context of the reply includes evaluating media components within the reply.
16. The processor-based method of claim 9, further comprising adjusting the frequency of the future communications based on an ambivalent confidence score.
9. A processor-based method of inferring opt-out instructions in a reply message from a user, the method comprising: receiving a reply from the user of a client device, in response to a communication sent to the client device; evaluating sentiment and context of the reply to infer intent whether to opt-out of receiving future communications and to generate a confidence score associated with the intent, the evaluating using a computer model; selectively instructing to terminate the future communications based on the inferred intent and the confidence score; receiving feedback, the feedback including an instruction to opt back in after instructing to terminate the future communications or including an adjustment of the confidence score; and training the computer model based on the feedback.
10. The processor-based method of claim 9, wherein the sentiment is based on an existence or an absence of profanity within the reply.
11. The processor-based method of claim 9, wherein the sentiment is based on historical behavior associated with the user of the client device.
12. The processor-based method of claim 11, wherein the historical behavior is based on a frequency of opting back in following the instructing to terminate the future communications.
13. The processor-based method of claim 9, wherein the context is based on the communication sent to the client device.
14. The processor-based method of claim 9, wherein the computer model is generated by a trained machine learning component.
15. The processor-based method of claim 9, wherein the evaluating the sentiment and the context of the reply includes evaluating media .components within the reply.
16. The processor-based method of claim 9, further comprising adjusting a frequency of the future communications based on an ambivalent confidence score.
Similarly all other dependent claims of the instant application (Application No. 18/733,467) are rejected on the ground of nonstatutory double patenting as being unpatentable over combinations of dependent claims (similar to combinations of independent/dependent claims as shown above) of U.S. Patent No. US 12,004,256 B2. Although those claims at issue are not identical, they are not patentably distinct from each other because combination of those dependent claims are “anticipated by” the combination of dependent claims of U.S. Patent No. US 12,004,256 B2.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
In 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 factual inquiries set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 1, 3, 5-6, 8-9, 11, 13-14 and 16 are rejected 35 U.S.C. 103 as being unpatentable over Gao et al. (2020/0336450), Gao hereinafter in view of Kuo et al. (2018/0322114), Kuo hereinafter.
Re. claims 1 and 9, Gao a teaches a processor-based method of inferring future-communication instructions in a reply message from a user (Fig. 1-12 & ¶0006 - The messages sent by the message selection system to respective client devices of users can be opened via an application executing on the respective client devices. The application executing on the client device can display a message from the message selection system to a user of the client device, prompting the user to perform an action or to elicit a user response. Fig. 1-12 & ¶0007 - the message selection system can maintain user profiles for a plurality of users. For each user profile, the message selection system can execute an invocator which can invoke message objects at predetermined time intervals. Each message object can generate a candidate message using a message template that is a candidate for transmission to the client device associated with the user profile. A message object evaluator of the message selection system can incorporate contextual data into a model of the message object to output a confidence value associated with the candidate message generated by the message object. The confidence value can be indicative of the likelihood that a candidate message sent to a user will have an intended effect. Fig. 1-12 & ¶0008 - The message selection system can receive response data from a reporting agent of the application executing on the client device associated with the user profile. A response evaluator of the message selection system can incorporate the response data into the models of the message objects so as to improve the performance of the models based on response data corresponding to previous messages sent to the client device. The response data can be compared against target values (or desired endpoints) of the message objects to improve the models of the message objects. In this way, the message selection system can learn from a user's message preferences and responses and improve the selection process of future candidate messages as well as improve the quality of the confidence values computed for the future candidate messages), and a server system (Fig. 1C-1D/Fig.2/Fig.7/Fig.9A) configured to infer future-communication instructions in a reply message from a user (Fig. 1-12 & ¶0006 - The messages sent by the message selection system to respective client devices of users can be opened via an application executing on the respective client devices. The application executing on the client device can display a message from the message selection system to a user of the client device, prompting the user to perform an action or to elicit a user response. Fig. 1-12 & ¶0007 - the message selection system can maintain user profiles for a plurality of users. For each user profile, the message selection system can execute an invocator which can invoke message objects at predetermined time intervals. Each message object can generate a candidate message using a message template that is a candidate for transmission to the client device associated with the user profile. A message object evaluator of the message selection system can incorporate contextual data into a model of the message object to output a confidence value associated with the candidate message generated by the message object. The confidence value can be indicative of the likelihood that a candidate message sent to a user will have an intended effect. Fig. 1-12 & ¶0008 - The message selection system can receive response data from a reporting agent of the application executing on the client device associated with the user profile. A response evaluator of the message selection system can incorporate the response data into the models of the message objects so as to improve the performance of the models based on response data corresponding to previous messages sent to the client device. The response data can be compared against target values (or desired endpoints) of the message objects to improve the models of the message objects. In this way, the message selection system can learn from a user's message preferences and responses and improve the selection process of future candidate messages as well as improve the quality of the confidence values computed for the future candidate messages. ) the server system comprising: one or more hardware processors (Fig. 1D, 121); and memory (Fig. 1D, 122) storing computer instructions (Fig. 1C-1D/Fig.2/Fig.7/Fig.9A & ¶0070), the computer instructions when executed by the one or more hardware processors configured (Fig. 1C-1D/Fig.2/Fig.7/Fig.9A & ¶0070/¶0108) to perform: receiving a reply from the user of a client device, in response to a communication sent to the client device; (Fig. 1-12 & ¶0006 - The messages sent by the message selection system to respective client devices of users can be opened via an application executing on the respective client devices. The application executing on the client device can display a message from the message selection system to a user of the client device, prompting the user to perform an action or to elicit a user response. The application can be configured to provide information back to the message selection system including data corresponding to when messages were received at the client device, when the application displayed the message, activity performed on the application by the user, activity performed by the user on the client device, among others. The message selection system can use the information collected or received from the client device of the user to evaluate the effectiveness of the message in getting the user to perform an action or the content and timing of the user response to improve the personalization of the messages sent to individual users or in some embodiments, to monitor the user and their behavior to the messages. Fig. 1-12 & ¶0008 - The message selection system can receive response data from a reporting agent of the application executing on the client device associated with the user profile. A response evaluator of the message selection system can incorporate the response data into the models of the message objects so as to improve the performance of the models based on response data corresponding to previous messages sent to the client device. The response data can be compared against target values (or desired endpoints) of the message objects to improve the models of the message objects. In this way, the message selection system can learn from a user's message preferences and responses and improve the selection process of future candidate messages as well as improve the quality of the confidence values computed for the future candidate messages. Fig. 1-12 & ¶0030 - the at least one server may establish a performance model for determining performance scores using a training dataset. The training dataset may include historical response data from users for one or more of the plurality of candidate message objects. Fig. 1-12 & ¶0109 - The response evaluator 256 can update the model 210 based on historical data. Fig. 1-12 & ¶0120 - The response evaluator 256 can use historical user time-of-day preference as more messages 208 are sent to the user over time. As more messages are sent and results are received, a more accurate picture of when a user is likely to be receptive to a message can be constructed. The models 210 can weigh historical inputs based on the amount of historical inputs that exist in the database and the effect of success rate the predictive nature of the model 210.); evaluating sentiment and context of the reply to infer intent for receiving future communications and to generate a confidence score associated with the intent, the evaluating using a computer model (Fig. 1-12 & ¶0007 - the message selection system can maintain user profiles for a plurality of users. For each user profile, the message selection system can execute an invocator which can invoke message objects at predetermined time intervals. Each message object can generate a candidate message using a message template that is a candidate for transmission to the client device associated with the user profile. A message object evaluator of the message selection system can incorporate contextual data into a model of the message object to output a confidence value associated with the candidate message generated by the message object. The confidence value can be indicative of the likelihood that a candidate message sent to a user will have an intended effect. The message object evaluator can output a confidence value for message objects that satisfy certain constraints. The message object evaluator can evaluate the confidence values associated with each of the candidate messages based on certain conditions, update the confidence value based on a cool down factor and determine to send the candidate message to the client device if the updated confidence value crosses a predetermined threshold. Fig. 1-12 & ¶0008 - the message selection system can learn from a user's message preferences and responses and improve the selection process of future candidate messages as well as improve the quality of the confidence values computed for the future candidate messages. Fig. 1-12 & ¶0012 - The message selection system can select messages based on a likelihood that the message is going to achieve a desired endpoint or target value while adjusting the likelihood based on a time since the last message was delivered to the client device so as to reduce the likelihood that a user will experience message fatigue. The message selection system can reduce the likelihood that a user will experience message fatigue by selectively transmitting or sending a message to the client device of the user based upon a likelihood that the message will have an intended effect. Because the number and frequency of messages that cause individual users to experience message fatigue can vary, the message selection system can incorporate information about each user's message preferences, application engagement, and responses to similar messages to build a framework or model for delivering customized messages at appropriate times. Fig. 1-12 & ¶0112 - message selection system 202 can be configured to update the models 210 of the message objects 206 used to generate candidate messages 240 based on historical data relating to the candidate messages 240 generated by the message objects 206. The successes or failures of message objects 206 in the past can improve the predictive capability of future models 210.); instructing to adjust a frequency of the future communications based on the inferred intent and the confidence score (Fig. 1-12 & ¶0004 -The message selection system can generate a confidence value for each of the messages and use the confidence value associated with each message to select one or more messages to send to the application of the client device. The message selection system can adjust the confidence values associated with each of the selected messages based on a cool down factor. The message selection system can use the cool down factor to adjust the confidence values of each of the selected messages based on various factors …. to an amount of time since the last message was received by a client device, the total number of messages a user of the client device elects to receive within a predetermined time period, and a length of time the user of the client device is awake or active. The message selection system can then, based on the adjusted confidence values of each of the selected messages, make a determination to send one or more of the selected messages based on the adjusted confidence values. Fig. 1-12 & ¶0005 - The message selection system can select messages based on a likelihood that the message is going to achieve a desired endpoint or target value while adjusting the likelihood based on a time since the last message was delivered to the client device so as to reduce the likelihood that a user will experience message fatigue. The message selection system can reduce the likelihood that a user will experience message fatigue by selectively transmitting or sending a message to the client device of the user based upon a likelihood that the message will have an intended effect. Fig. 1-12 & ¶0185 - Based on a comparison of the output with the result indicated in the historical response data, the model trainer 912 may adjust or modify the weights of the evaluation model 920, and repeat the application until convergence. The modification of the weights of the evaluation model 920 may be in accordance with learning for the architecture used to implement the evaluation model 920); and training the computer model based on the feedback (Fig. 1-12 & ¶0111 - message selection system 202 can use feedback received from the remote computing devices 270 to which messages were sent to improve the message generation and selection capabilities of the message selection system 202. For instance, the message selection system 202 can be configured to update the models 210 of the message objects 206 used to generate candidate messages 240 based on historical data relating to the candidate messages 240 generated by the message objects 206. The successes or failures of message objects 206 in the past can improve the predictive capability of future models 210. A model 210 can use sent messages and their corresponding results and feature sets during the training phase of the model 210. A number of different supervised machine learning models can be trained online or offline. Training a model 210 can use a cross-validation approach to find optimal model parameters. Retraining message object models 210 can occur at each iteration (e.g., training cycle). A message object 206 can include a model 210 that is predictive of the confidence values 242 of candidate messages 240. Fig. 1-12 & ¶0185 - The model trainer 912 may initiate or establish the evaluation model 920 using a training dataset. The training of the evaluation model 920 may be in accordance for the architecture of the evaluation model 920. For example, when the evaluation model 920 is a classification model, the training may be iteratively performed until convergence. The training dataset for the evaluation model 920 may include historical response data from users (e.g., the user 936) to previously presented messages. The messages in the training dataset may correspond to one or more of the message objects 930. The training dataset may include the message templates 944, the constraints 946, and the selection criteria 948 of the message objects 930 corresponding to the messages previously served to the users. The historical response data may indicate success or failure at achieving the endpoint specified by the message objects 930. In training the model, the model trainer 912 may apply the training dataset to the evaluation model 920 to generate an output indicating a success or failure. Based on a comparison of the output with the result indicated in the historical response data, the model trainer 912 may adjust or modify the weights of the evaluation model 920, and repeat the application until convergence. The modification of the weights of the evaluation model 920 may be in accordance with learning for the architecture used to implement the evaluation model 920.).
Yet, Gao does not expressly teach receiving feedback, the feedback including an explicit instruction from the user regarding the future communications or including an adjustment of the confidence score;
However, in the analogous art, Kuo explicitly discloses receiving feedback, the feedback including an explicit instruction from the user regarding the future communications or including an adjustment of the confidence score (Fig. 1-6 & ¶0005 - the present invention provide a system that can be used to determine whether a sentiment analysis model can be portable between two data sets. During operation, the system analyzes the text of a respective review in a data set (e.g., a set of reviews) using the sentiment analysis model to determine a sentiment expressed in the review. The system then computes a confidence score, which indicates an accuracy of a respective sentiment. The system subsequently determines a confidence score distribution for various sentiments, as determined by the sentiment analysis model. The system further determines the significance of changes between the confidence score distribution and a benchmark confidence score distribution, which is associated with a benchmark data set for which the sentiment analysis model yields a high accuracy. The system can then determine whether the sentiment analysis model is portable to the data set based on the significance of changes. Fig. 1-6 & ¶0006 - If the significance of changes is greater than or equal than a portability threshold, the system can determine that the sentiment analysis model is portable to the data set, thereby indicating that the sentiment analysis model can yield a high accuracy for the data set. Fig. 1-6 & ¶0009 - On the other hand, if the significance of changes is less than the portability threshold, the system can determine that the sentiment analysis model needs retraining for the data set, and can indicate that the sentiment analysis model is not portable to the data set. In some embodiments, the system can determine whether the significance of changes is less than the portability threshold by determining a recall of a set of p-values obtained from applying a Kolmogorov-Smirnov (K-S) test to the confidence score distribution and the benchmark confidence score distribution, and comparing the recall with the portability threshold. Fig. 1-6 & ¶0033 - During operation, model 102 is trained using a training data set with identified sentiments. When model 102 is trained, model 102 is used to determine user sentiments in a new data set. Fig. 1-6 & ¶0039 - system 160 further includes an update module 166, which updates the benchmark data set. For example, if the median confidence score of data set 150 is more than the median confidence score of the benchmark data set, update module 166 sets data set 150 as the benchmark data set. Adjusting the benchmark data set based on the median confidence score ensures that system 160 maintains the distribution with the highest accuracy as the benchmark data set. Fig. 1-6 & ¶0040 - sentiment analysis model 102 is incorporated with determination module 162. Model 102 can include a sentiment prediction mechanism 172, which analyzes the text of review 152 in data set 150 to determine (or predict) the sentiment expressed in review 152. Model 102 further includes a confidence score generation mechanism 174, which calculates a confidence score <See ¶0044-¶0045 along with Fig.3> for the determined sentiment for review 152. Fig. 1-6 & ¶0051 - Portability analysis system 618 includes instructions for determining the sentiment expressed in the text of a respective user review in a data set and a corresponding confidence score for the sentiment (determination module 620). Portability analysis system 618 can also include instructions for determining a confidence score distribution for the data set (determination module 620)).
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Therefore, it would have been obvious to one of the ordinary skill in the art before the effective filling date of the claimed invention to combine Gao’s invention of a system a method for message selection and transmission based on confidence values and cool down factors in a networking system to include Kuo’s invention of system and a method for determining portability of a sentiment analysis system in a networking system, because it provides a system that can be used in determining whether a sentiment analysis model is portable to another data set in the same domain or to a new domain in the networking system . (¶0002-¶0005, Kuo)
Re. claims 3 and 11, Gao and Kuo teach claims 1 and 9.
Yet, Gao does not expressly teach wherein the sentiment is based on historical behavior associated with the user of the client device.
However, in the analogous art, Kuo explicitly discloses wherein the sentiment is based on historical behavior associated with the user of the client device. (Fig. 1-6 & ¶0003 - An application server for the business entity may store the reviews in a local storage device. Machine learning techniques can be used on the reviews to obtain the sentiment from the reviews. Sentiment analysis involves determining whether the text of a review expresses positive, negative, neutral, or mixed sentiments. Such sentiment analysis typically uses a historic data set for training a sentiment analysis model. For example, a sentiment analysis model can be trained using a training data set that has been labeled by a user (e.g., the sentiments have been identified by the user). The trained model learns the associations between various language patterns and the corresponding sentiments in the training data set.)
Therefore, it would have been obvious to one of the ordinary skill in the art before the effective filling date of the claimed invention to combine Gao’s invention of a system a method for message selection and transmission based on confidence values and cool down factors in a networking system to include Kuo’s invention of system and a method for determining portability of a sentiment analysis system in a networking system, because it provides a system that can be used in determining whether a sentiment analysis model is portable to another data set in the same domain or to a new domain in the networking system . (¶0002-¶0005, Kuo)
Re. claims 5 and 13, Gao and Kuo teach claims 1 and 9.
Gao further teaches wherein the context is based on the communication sent to the client device. (Fig. 1-12 & ¶0007 -A message object evaluator of the message selection system can incorporate contextual data into a model of the message object to output a confidence value associated with the candidate message generated by the message object. The confidence value can be indicative of the likelihood that a candidate message sent to a user will have an intended effect. The message object evaluator can output a confidence value for message objects that satisfy certain constraints. The message object evaluator can evaluate the confidence values associated with each of the candidate messages based on certain conditions, update the confidence value based on a cool down factor and determine to send the candidate message to the client device if the updated confidence value crosses a predetermined threshold. Fig. 1-12 & ¶0008 - the message selection system can learn from a user's message preferences and responses and improve the selection process of future candidate messages as well as improve the quality of the confidence values computed for the future candidate messages. Fig. 1-12 & ¶0012 - The message selection system can select messages based on a likelihood that the message is going to achieve a desired endpoint or target value while adjusting the likelihood based on a time since the last message was delivered to the client device so as to reduce the likelihood that a user will experience message fatigue. The message selection system can reduce the likelihood that a user will experience message fatigue by selectively transmitting or sending a message to the client device of the user based upon a likelihood that the message will have an intended effect. Because the number and frequency of messages that cause individual users to experience message fatigue can vary, the message selection system can incorporate information about each user's message preferences, application engagement, and responses to similar messages to build a framework or model for delivering customized messages at appropriate times. Fig. 1-12 & ¶0112 - message selection system 202 can be configured to update the models 210 of the message objects 206 used to generate candidate messages 240 based on historical data relating to the candidate messages 240 generated by the message objects 206. The successes or failures of message objects 206 in the past can improve the predictive capability of future models 210).
Re. claims 6 and 14, Gao and Kuo teach claims 1 and 9.
Gao further teaches wherein the computer model is generated by a trained machine learning component. (Fig. 1-12 & ¶0030 - the at least one server may establish a performance model for determining performance scores using a training dataset. The training dataset may include historical response data from users for one or more of the plurality of candidate message objects. Fig. 1-12 & ¶0111 - message selection system 202 can use feedback received from the remote computing devices 270 to which messages were sent to improve the message generation and selection capabilities of the message selection system 202…. A model 210 can use sent messages and their corresponding results and feature sets during the training phase of the model 210. A number of different supervised machine learning models can be trained online or offline. Training a model 210 can use a cross-validation approach to find optimal model parameters. Retraining message object models 210 can occur at each iteration (e.g., training cycle). A message object 206 can include a model 210 that is predictive of the confidence values 242 of candidate messages 240. Fig. 1-12 & ¶0116 - Estimating the effectiveness of a particular message of a particular user at a particular point in time can include a supervised machine learning approach. The current time context (e.g., time of day, day of the week, or week of the year), user application data, and user attention can be used as features in a supervised machine learning model. The type of decision model (e.g., polynomial regression, decision tree, or paired comparison analysis) can vary from message object 206 to message object 206. The model 210 of each message object 206 can output a confidence value 242 indicating the confidence level of the target value 214. The message object evaluator 252 can update confidence values 242 when an invocator 250 initiates an invoke process. Fig. 1-12 & ¶0185 - In training the model, the model trainer 912 may apply the training dataset to the evaluation model 920 to generate an output indicating a success or failure. Based on a comparison of the output with the result indicated in the historical response data, the model trainer 912 may adjust or modify the weights of the evaluation model 920, and repeat the application until convergence.).
Re. claims 8 and 16, Gao and Kuo teach claims 1 and 9.
Gao further teaches wherein the computer instructions, when executed by the one or more hardware processors, are configured to perform adjusting the frequency of the future communications based on an ambivalent confidence score. (Fig. 1-12 & ¶0004 -The message selection system can generate a confidence value for each of the messages and use the confidence value associated with each message to select one or more messages to send to the application of the client device. The message selection system can adjust the confidence values associated with each of the selected messages based on a cool down factor. The message selection system can use the cool down factor to adjust the confidence values of each of the selected messages based on various factors …. to an amount of time since the last message was received by a client device, the total number of messages a user of the client device elects to receive within a predetermined time period, and a length of time the user of the client device is awake or active. The message selection system can then, based on the adjusted confidence values of each of the selected messages, make a determination to send one or more of the selected messages based on the adjusted confidence values. Fig. 1-12 & ¶0005 - The message selection system can select messages based on a likelihood that the message is going to achieve a desired endpoint or target value while adjusting the likelihood based on a time since the last message was delivered to the client device so as to reduce the likelihood that a user will experience message fatigue. The message selection system can reduce the likelihood that a user will experience message fatigue by selectively transmitting or sending a message to the client device of the user based upon a likelihood that the message will have an intended effect. Fig. 1-12 & ¶0185 - Based on a comparison of the output with the result indicated in the historical response data, the model trainer 912 may adjust or modify the weights of the evaluation model 920, and repeat the application until convergence. The modification of the weights of the evaluation model 920 may be in accordance with learning for the architecture used to implement the evaluation model 920).
Claims 2 and 10 are rejected under 35 U.S.C. 103 as being unpatentable over Gao, in view of Kuo, further in view of Jaggi et al. (2017/0004517 as submitted in IDS), Jaggi hereinafter.
Re. claims 2 and 10, Gao and Kuo teach claims 1 and 9.
Yet, Gao and Kuo do not expressly teach wherein the sentiment is based on an existence or an absence of profanity within the reply.
However, in the analogous art, Jaggi explicitly discloses wherein the sentiment is based on an existence or an absence of profanity within the reply. (Fig. 1-7 & ¶0054- ¶0055 - In step 401, any slang used in the set of text responses is determined. In this step, a set of slang words and phrases, including profanity, are retrieved from a database.. The set of text responses is scanned and compared to the set of slang words and phrases for any matches. In step 402, a text sentiment is determined from the set of text responses,…. In step 403, the demographics, non-speech sentiment, slang, and text sentiment are saved for later reporting.)
Therefore, it would have been obvious to one of the ordinary skill in the art before the effective filling date of the claimed invention to combine Gao’s invention of a system a method for message selection and transmission based on confidence values and cool down factors in a networking system to include Kuo’s invention of system and a method for determining portability of a sentiment analysis system in a networking system to include Jaggi’s invention of a survey system and a method, because it provides an efficient mechanism for capturing and analyzing speech to determine emotion and sentiment from a survey. (¶0009, Jaggi)
Claims 4, 7, 12 and 15 are rejected under 35 U.S.C. 103 as being unpatentable over Gao, in view of Kuo, further in view of Daniel et al. (2017/0026318 as submitted in IDS), Daniel hereinafter.
Re. claims 4 and 12, Gao and Kuo teach claims 3 and 11.
Yet, Gao and Kuo do not expressly teach wherein the historical behavior is based on a frequency of opting back in following an instruction to terminate the future communications.
However, in the analogous art, Daniel explicitly discloses wherein the historical behavior is based on a frequency of opting back in following an instruction to terminate the future communications. (Fig. 1-8 & ¶0162 - In this way, a user's future actions may be predicted based on the user's prior actions, where the coefficient may be calculated at least in part... the history of the user's actions….Fig. 1-8 & ¶0170 - ….privacy settings may allow users to opt in or opt out of having their actions logged... In particular embodiments, the privacy settings associated with an object may specify any suitable granularity of permitted access or denial of access. Also, behavioral information associated with the user profile may include their privacy settings, which may include the number of times they have allowed or denied access based on the aforesaid paragraphs along with ¶0050 and ¶0148).
Therefore, it would have been obvious to one of the ordinary skill in the art before the effective filling date of the claimed invention to combine Gao’s invention of a system a method for message selection and transmission based on confidence values and cool down factors in a networking system to include Kuo’s invention of system and a method for determining portability of a sentiment analysis system in a networking system to include Daniel’s invention of system and a method for providing personal assistant service via messaging, because it improves the efficiency with which an agent is able to provide services to a user based on a determined intent of a request received from the user. (¶0008, Daniel)
Re. claims 7 and 15, Gao and Kuo teach claims 1 and 9.
Yet, Gao and Kuo do not expressly teach wherein the evaluating the sentiment and the context of the reply includes evaluating media components within the reply.
However, in the analogous art, Daniel explicitly discloses wherein the evaluating the sentiment and the context of the reply includes evaluating media components within the reply. (Fig. 1-8 & ¶0026 - Messages can include text messages, photos, stickers or other icons, videos, voice recordings, music, voice mails, etc. Also, see ¶0025, ¶0054, ¶0147, ¶0157).
Therefore, it would have been obvious to one of the ordinary skill in the art before the effective filling date of the claimed invention to combine Gao’s invention of a system a method for message selection and transmission based on confidence values and cool down factors in a networking system to include Kuo’s invention of system and a method for determining portability of a sentiment analysis system in a networking system to include Daniel’s invention of system and a method for providing personal assistant service via messaging, because it improves the efficiency with which an agent is able to provide services to a user based on a determined intent of a request received from the user. (¶0008, Daniel)
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Liu et al. (2021/0380126); See Abstract, ¶0052, ¶0167 along with Fig.1-10.
Foerster et al. (2019/0268632); See ¶0068-¶0071 along with Fig.1-15.
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/MOHAMMED S CHOWDHURY/Primary Examiner, Art Unit 2467
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