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 .
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.
Step 1: claims 1-20 are directed to either a process, machine, manufacture or composition of matter.
With respect to claims 1, 2, 15:
2A Prong 1:
determining a first attribute for the first action; (Abstract idea of analyzing data. Mental process. A human- mind with pen and paper can generate/determine data);
generating a feature input for an [artificial intelligence model], wherein the feature input is based on the first attribute, and wherein the [artificial intelligence model] is [trained] to prioritize notifications based on detected actions based on a comparison of inputted attributes and user response times to the notifications corresponding to the inputted attributes(Abstract idea of analyzing data. Mental process. A human- mind with pen and paper can generate/determine data);
determining whether to generate a first notification for the first action based on comparing the first output to a first threshold (Abstract idea of analyzing data. Mental process. A human- mind with pen and paper can generate/determine data);
and generating for [display], on a [user interface], the first notification in response to determining that the first output equals or exceeds the first threshold (Abstract idea of analyzing data. Mental process. A human- mind with pen and paper can generate/determine data).
2A Prong 2: This judicial exception is not integrated into a practical application.
Additional elements:
processors (computer component is recited at a high level of generality and amounts to no more than mere instructions to apply the exception using a generic computer component; the mere recitation of a generic computer cannot transform a patent-ineligible abstract idea into a patent-eligible invention." Alice, 134 S. Ct. at 2358);
a non-transitory, computer-readable medium comprising instructions that, when executed by the one or more processors, cause operations (Adding insignificant extra-solution activity to the judicial exception - see MPEP 2106.05(g));
receiving first real-time data indicating a first action; (mere data gathering and output recited at a high level of generality - insignificant extra-solution activity to the judicial exception - see MPEP 2106.05(g));
artificial intelligence model (Adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea - see MPEP 2106.05(f));
trained to prioritize notifications based on detected actions based on a comparison of inputted attributes and user response times to the notifications corresponding to the inputted attributes (Adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea - see MPEP 2106.05(f) – Examiner’s note: high level recitation of training a machine learning model with previously determined data);
inputting the feature input into the artificial intelligence model to generate a first output (mere data gathering and output recited at a high level of generality - insignificant extra-solution activity to the judicial exception - see MPEP 2106.05(g));
one or more interactive displays (computer component is recited at a high level of generality and amounts to no more than mere instructions to apply the exception using a generic computer component);
generating for display (insignificant extra solution and under 2B Berkheimer from MPEP 2106.05(d)(II)) on a user interface (computer component is recited at a high level of generality and amounts to no more than mere instructions to apply the exception using a generic computer component);
2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Additional elements:
processors (computer component is recited at a high level of generality and amounts to no more than mere instructions to apply the exception using a generic computer component; the mere recitation of a generic computer cannot transform a patent-ineligible abstract idea into a patent-eligible invention." Alice, 134 S. Ct. at 2358);
a non-transitory, computer-readable medium comprising instructions that, when executed by the one or more processors, cause operations (Adding insignificant extra-solution activity to the judicial exception - see MPEP 2106.05(g));
receiving first real-time data indicating a first action; (mere data gathering and output recited at a high level of generality - insignificant extra-solution activity to the judicial exception - see MPEP 2106.05(g));
artificial intelligence model, (Adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea - see MPEP 2106.05(f));
trained to prioritize notifications based on detected actions based on a comparison of inputted attributes and user response times to the notifications corresponding to the inputted attributes (Adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea - see MPEP 2106.05(f) – Examiner’s note: high level recitation of training a machine learning model with previously determined data);
inputting the feature input into the artificial intelligence model to generate a first output (mere data gathering and output recited at a high level of generality - insignificant extra-solution activity to the judicial exception - see MPEP 2106.05(g));
one or more interactive displays (computer component is recited at a high level of generality and amounts to no more than mere instructions to apply the exception using a generic computer component);
generating for display (insignificant extra solution and under 2B Berkheimer from MPEP 2106.05(d)(II)) on a user interface (computer component is recited at a high level of generality and amounts to no more than mere instructions to apply the exception using a generic computer component);
Displaying on an interface: Presenting offers and gathering statistics, OIP Techs., 788 F.3d at 1362-63, 115 USPQ2d at 1092-93;
Further, the receiving/inputing steps were considered to be extra-solution activity in Step 2A Prong 2, and thus it is re-evaluated in Step 2B to determine if it is more than what is well-understood, routine, conventional activity in the field. The receiving and/or transmitting limitations constitute extra-solution activity. See buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355 (Fed. Cir. 2014) ("That a computer receives and sends the information over a network-with no further specification-is not even arguably inventive."). The court decisions cited in MPEP 2106.05(d)(II) indicate that merely Receiving and/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). Thereby, a conclusion that the claimed receiving/transmitting steps are well-understood, routine, conventional activity is supported under Berkheimer. The claim is not patent eligible.
3. The method of claim 2, wherein training the artificial intelligence model to prioritize the notifications further comprises: determining an average response time corresponding to the first attribute; and determining a priority based on the average response time and the first action (further expand mental process, abstract idea of analyzing data. Mental process. A human- mind with pen and paper can generate/determine data).
4. The method of claim 2, wherein training the artificial intelligence model to prioritize the notifications further comprises: determining a rate of change in response times corresponding to the first attribute; and determining a priority based on the rate of change in response times and the first action(further expand mental process, abstract idea of analyzing data. Mental process. A human- mind with pen and paper can generate/determine data).
5. The method of claim 2, wherein training the artificial intelligence model to prioritize the notifications further comprises: determining a frequency of the first action corresponding to the first attribute; and determining a priority based on the frequency and the first action(further expand mental process, abstract idea of analyzing data. Mental process. A human- mind with pen and paper can generate/determine data).
6. The method of claim 2, wherein training the artificial intelligence model to prioritize the notifications further comprises: determining a magnitude of the first action corresponding to the first attribute; and determining a priority based on the magnitude and the first action(further expand mental process, abstract idea of analyzing data. Mental process. A human- mind with pen and paper can generate/determine data).
7, 16. The method of claim 2, wherein training the artificial intelligence model to prioritize the notifications further comprises: determining a number of user inputs received in response to the first notification; and determining a priority based on the number of user inputs and the first action(further expand mental process, abstract idea of analyzing data. Mental process. A human- mind with pen and paper can generate/determine data).
8, 17. The method of claim 2, wherein determining the first threshold further comprises: determining a current time; determining a user response time corresponding to the current time; and determining the first threshold based on the current time and the user response time(further expand mental process, abstract idea of analyzing data. Mental process. A human- mind with pen and paper can generate/determine data).
9, 18. The method of claim 2, wherein determining the first threshold further comprises: determining a rate of change of detected actions; and determining the first threshold based on the rate of change of detected actions over a period of time(further expand mental process, abstract idea of analyzing data. Mental process. A human- mind with pen and paper can generate/determine data).
10. The method of claim 2, wherein determining the first threshold further comprises: determining an upcoming user action based on local user-provided communication; and determining the first threshold based on the upcoming user action(further expand mental process, abstract idea of analyzing data. Mental process. A human- mind with pen and paper can generate/determine data).
11. The method of claim 2, wherein determining the first threshold further comprises: determining a tone corresponding to a notification associated with the first action; and determining the first threshold based on the tone corresponding to the notification(further expand mental process, abstract idea of analyzing data. Mental process. A human- mind with pen and paper can generate/determine data).
12. The method of claim 2, wherein determining the first threshold further comprises: determining a satisfaction metric corresponding to a user action, wherein the satisfaction metric is derived from user data; and determining the first threshold based on the satisfaction metric corresponding to the user action(further expand mental process, abstract idea of analyzing data. Mental process. A human- mind with pen and paper can generate/determine data).
13. The method of claim 2, wherein determining the first threshold further comprises: retrieving location data corresponding to a user action; and determining the first threshold based on the location data corresponding to the user action(further expand mental process, abstract idea of analyzing data. Mental process. A human- mind with pen and paper can generate/determine data).
14. The method of claim 2, wherein determining the first threshold further comprises: retrieving gyroscope data and accelerometer data corresponding to a user action; and determining the first threshold based on the gyroscope data and the accelerometer data corresponding to the user action(further expand mental process, abstract idea of analyzing data. Mental process. A human- mind with pen and paper can generate/determine data).
19. The non-transitory, computer-readable medium of claim 15, wherein determining the first threshold further comprises: determining an upcoming user action by: retrieving a local user-provided communication; identifying a plurality of keywords using natural language processing, wherein the plurality of keywords is a subset of the local user-provided communication; and searching for the plurality of keywords in a keyword database, wherein the keyword database comprises a plurality of known keywords and a plurality of corresponding actions; and determining the first threshold based on the upcoming user action(further expand mental process, abstract idea of analyzing data. Mental process. A human- mind with pen and paper can generate/determine data).
20. The non-transitory, computer-readable medium of claim 15, wherein determining the first threshold further comprises: determining a tone corresponding to a notification associated with the first action by: identifying a response to the notification, wherein the response to the notification comprises a user message; and using a sentiment analysis model to identify the tone corresponding to the response; and determining the first threshold based on the tone corresponding to the notification(further expand mental process, abstract idea of analyzing data. Mental process. A human- mind with pen and paper can generate/determine data).
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.
Claim(s) 1-13, 15-20 are rejected under 35 U.S.C. 103 as being unpatentable over Proulx (US 2016/0086241) in view of Lee (US 2018/0253659).
Proulx
1, 2, 15. A system for generating time-sensitive notifications based on real-time data that reduces inconvenience to users, the system comprising:
one or more processors (Fig. 1); and a non-transitory, computer-readable medium (Fig. 2) comprising instructions that, when executed by the one or more processors, cause operations comprising:
receiving first real-time (relative term, reads on current data, data transmitted and received as it happens, Figs. 3) data indicating a first action (not defined, reads on any action at all, e.g., one of a many different actions pertaining to a computer system, e.g., Figs. 3 or action can be responding to a message, offer, or making a purchase, “The interactive notifications include multiple single- or limited-action user-response options, such as buttons for purchasing the product or indicating that the user is not interested, enabling a user to immediately respond to the notification”, abstract;
messaging/notifying, “Email, text message, and other traditional forms of communication that solicit a user response (e.g., offers to purchase”, 0002, 0012;
user responding to words, “user responses and metadata, user engagement levels with notifications, or words or presentation formats that are more likely to induce particular user responses (such as purchases)”, 0051, 0058-9;
user responding to messages/notifications/recommendations, “responses may be characterized as one of three types: (1) positive or accepting (e.g., user accepts an offer or recommendation, buys a product, completes a customer survey); (2) rejection or active ignore, wherein the user indicates that he or she is not interested in the offer, recommendation, suggestion, activity (i.e., completing a survey), etc.; and (3) passive rejection or passive ignore, indicating that the user sees the notification but does not act.”, 0061)
determining a first attribute for the first action (not defined, attribute reads on many different things, metadata, quantities, prices, amounts, data pertaining to actions pertaining to messages or purchases, see e.g., 0012 or ““metadata” refers to analytical data or other data that may be collected from the interactive notification event and may be used by the merchant (or other solicitor) to understand the user, tailor future notifications, or otherwise determine how users/customers like to interact with communication from the merchant (or other solicitor). For instance, metadata might include information indicating when and how long a notification was presented on the mobile device, the applications currently running on the user device when the notification is presented, the user's location, user activity on the mobile device before and after the notification was presented, or similar data”, 0013);
generating a feature input for an artificial intelligence model, wherein the feature input is based on the first attribute, and wherein the artificial intelligence model is trained to prioritize notifications based on detected actions based on a comparison of inputted attributes and user response times to the notifications corresponding to the inputted attributes (Lee);
inputting the feature input into the artificial intelligence model to generate a first output (Lee);
determining whether to generate a first notification for the first action based on comparing the first output to a first threshold (Figs. 3); and generating for display (Figs. 3), on a user interface (e.g., 360, Figs. 3), the first notification in response to determining that the first output equals or exceeds the first threshold (see Lee for “threshold”, Proulx discloses priority levels, “The notifications manager 262, in general, manages the presentation of notifications and determines to present notifications on client device 260, such as what content to present, when to present a notification, how to present it, the priority of the notification”, 0039; “notifications policy determines a priority associated with the notification, such as whether the notification should interrupt other applications running on client device 260”, 0041, 0045, 0070).
Proulx fails to particularly call for the computer system (Figs. 1-3) to be an AI system or generating a feature input for an artificial intelligence model, wherein the feature input is based on the first attribute, and wherein the artificial intelligence model is trained to prioritize notifications based on detected actions based on a comparison of inputted attributes and user response times to the notifications corresponding to the inputted attributes; inputting the feature input into the artificial intelligence model to generate a first output; or that determining a priority level of messages inherently involves thresholds .
Lee teaches generating a feature input for an artificial intelligence model, wherein the feature input is based on the first attribute, and wherein the artificial intelligence model is trained to prioritize notifications based on detected actions based on a comparison of inputted attributes and user response times to the notifications corresponding to the inputted attributes; inputting the feature input into the artificial intelligence model to generate a first output (“machine learning engine 112e may train one or more machine learning models 112g to determine opportunities for performing automated message management actions based on outputs of the machine learning models”, 0042; “one or more models 112g used by machine learning engine 112e to generate a priority score may be trained (e.g., by message management computing platform 110 and/or machine learning engine 112e) based on monitored user interaction data (e.g., as received in step 202) indicating various opportunities to perform automated message management actions. For example, machine learning engine 112e may use an order of selection of messages (e.g., from among available unread messages) and/or response times to messages received at step 201 as a measurement of priority of a corresponding message, and thereby train a machine learning model 112g to estimate the priority of a new message”, 0047, 0049, 0065;
“Message management computing platform 110 may monitor and record such interactions so that machine learning engine 112e may later train, for example, a machine learning model 112g that estimates a priority of a message”, 0042;
“the message management computing platform may train machine learning models to estimate a priority for messages. Subsequently, after estimating a priority for a newly-received message, the message management computing platform may automatically manage the new message by, for example, ranking the new message based on its priority, notifying the user”, 0022;
“Based, for example, on a user commonly sending certain responses or certain types of responses to messages having certain characteristics, the message management computing platform may train one or more machine learning models to classify a message”, 0023);
and that priority level of messages inherently involves thresholds (“message management computing platform 110 and/or message server module 112a may select a certain number of the highest priority messages having certain characteristics (e.g., four unresponded messages) and/or all messages above a threshold priority score having the characteristics for presentation”, 0071-3) and real-time (“monitoring response times in real time, “Message management computing platform 110 may monitor user interactions with messages including user responses to messages, user selections of messages, user categorizations of messages, and the like”, 0042).
It would have been obvious to combine the references before the effective filing date because they are in the same field of endeavor and to use AI and/or or a model/classifier to determine which notifications should be sent or what their priority is in order to save the consumer time and filter, rank and/or prioritize messages. Indicating that priority levels amount to thresholds can with metrics indicating when to bother a user with a message, pop-up message.
3. The method of claim 2, wherein training the artificial intelligence model to prioritize the notifications further comprises: determining an average response time corresponding to the first attribute; and determining a priority based on the average response time and the first action.
Lee teaches averages (0058) and response times (“a relatively low response time (e.g., a relatively fast response time) to a particular message may indicate a high priority for that message”, 0042).
It would have been obvious to combine the references before the effective filing date because they are in the same field of endeavor and indicating that priorities are based on above and below averages allows for thresholds to be set accordingly when deciding whether or not to bother a user with a message. Labeling messages as high priority because a user responded very fast makes sense and allows for better classification of messages.
4. The method of claim 2, wherein training the artificial intelligence model to prioritize the notifications further comprises: determining a rate of change (reads on zero change) in response times corresponding to the first attribute; and determining a priority based on the rate of change in response times and the first action.
Lee teaches rate of change in response times (e.g., “a relatively low response time (e.g., a relatively fast response time) to a particular message may indicate a high priority for that message”, 0042, 0047, 0054, 0084).
It would have been obvious to combine the references before the effective filing date because they are in the same field of endeavor and indicating that priorities are based on above and below averages allows for thresholds to be set accordingly when deciding whether or not to bother a user with a message. Labeling messages as high priority because a user responded very fast makes sense and allows for better classification of messages.
5. The method of claim 2, wherein training the artificial intelligence model to prioritize the notifications further comprises: determining a frequency of the first action corresponding to the first attribute (reads on zero or more times of a user buying a product, zero times, for the first time, many times, or quickly buying it, “purchases are referred to as impulse purchases”, 0059; “where a user has purchased a particular sweater from a merchant, the merchant may follow up with the user, via notification 330, to recommend that the user purchase a matching scarf. In this example, the response options include a “YES—BUY NOW” option 334, which initiates a purchase transaction”, 0058; “in the case of a user who orders coffee each month from an online coffee vendor, a notification from the coffee vendor may be presented to the user querying whether it is time to order more coffee and with response options”, 0053; “user history, such as previous notification responses and related analytical data, past purchases, or browsing or search history.”, 0052); and determining a priority based on the frequency (reads on zero) and the first action (“where a user has purchased a particular sweater from a merchant, the merchant may follow up with the user, via notification 330, to recommend that the user purchase a matching scarf. In this example, the response options include a “YES—BUY NOW” option 334, which initiates a purchase transaction”, 0058; “in the case of a user who orders coffee each month from an online coffee vendor, a notification from the coffee vendor may be presented to the user querying whether it is time to order more coffee and with response options”, 0053; “user history, such as previous notification responses and related analytical data, past purchases, or browsing or search history.”, 0052).
6. The method of claim 2, wherein training the artificial intelligence model to prioritize the notifications further comprises: determining a magnitude of the first action (reads on zero, none or more) corresponding to the first attribute; and determining a priority based on the magnitude and the first action(“where a user has purchased a particular sweater from a merchant, the merchant may follow up with the user, via notification 330, to recommend that the user purchase a matching scarf. In this example, the response options include a “YES—BUY NOW” option 334, which initiates a purchase transaction”, 0058; “in the case of a user who orders coffee each month from an online coffee vendor, a notification from the coffee vendor may be presented to the user querying whether it is time to order more coffee and with response options”, 0053; “user history, such as previous notification responses and related analytical data, past purchases, or browsing or search history.”, 0052).
7, 16. The method of claim 2, wherein training the artificial intelligence model to prioritize the notifications further comprises: determining a number of user inputs (reads on zero or more) received in response to the first notification; and determining a priority based on the number of user inputs (reads on zero or more, Figs. 3) and the first action (“where a user has purchased a particular sweater from a merchant, the merchant may follow up with the user, via notification 330, to recommend that the user purchase a matching scarf. In this example, the response options include a “YES—BUY NOW” option 334, which initiates a purchase transaction”, 0058; “in the case of a user who orders coffee each month from an online coffee vendor, a notification from the coffee vendor may be presented to the user querying whether it is time to order more coffee and with response options”, 0053; “user history, such as previous notification responses and related analytical data, past purchases, or browsing or search history.”, 0052).
8, 17. The method of claim 2, wherein determining the first threshold further comprises: determining a current time (using mobile devices that inherently track current time, Figs. 3); determining a user response time corresponding to the current time; and determining the first threshold based on the current time and the user response time.
Lee teaches current time and determining a user response time corresponding to the current time; and determining the first threshold based on the current time and the user response time (monitoring response times in real time, “Message management computing platform 110 may monitor user interactions with messages including user responses to messages, user selections of messages, user categorizations of messages, and the like. Such interactions may indicate, for example, a priority that a user assigns to a message. For example, a relatively low response time (e.g., a relatively fast response time) to a particular message may indicate a high priority for that message. As another example, a user of an email service may, upon opening an inbox of unread messages, select higher priority messages before other messages”, 0042); and that priority level of messages inherently involves thresholds (“message management computing platform 110 and/or message server module 112a may select a certain number of the highest priority messages having certain characteristics (e.g., four unresponded messages) and/or all messages above a threshold priority score having the characteristics for presentation”, 0071-3).
It would have been obvious to combine the references before the effective filing date because they are in the same field of endeavor and to use thresholds in determining which notifications should be sent or what their priority is in order to save the consumer time and filter, rank and/or prioritize messages. Indicating that priority levels amount to thresholds can help with metrics indicating when to bother a user with a message, pop-up message.
9, 18. The method of claim 2, wherein determining the first threshold further comprises: determining a rate of change (reads on no rate of change) of detected actions (Figs. 3, detecting user interacting with mobile/computer, user making purchases, and/or see Lee for detecting responses to messages; user buying a product, zero times, for the first time, many times, or quickly buying it, “purchases are referred to as impulse purchases”, 0059; “where a user has purchased a particular sweater from a merchant, the merchant may follow up with the user, via notification 330, to recommend that the user purchase a matching scarf. In this example, the response options include a “YES—BUY NOW” option 334, which initiates a purchase transaction”, 0058; “in the case of a user who orders coffee each month from an online coffee vendor, a notification from the coffee vendor may be presented to the user querying whether it is time to order more coffee and with response options”, 0053; “user history, such as previous notification responses and related analytical data, past purchases, or browsing or search history.”, 0052); and determining the first threshold based on the rate of change (of zero, none or non-zero) of detected actions over a period of time (no time or any time).
Lee teaches determining the first threshold based on the rate of change (of zero, none or non-zero) of detected actions over a period of time (no time or any time, “message management computing platform 110 and/or message server module 112a may select a certain number of the highest priority messages having certain characteristics (e.g., four unresponded messages) and/or all messages above a threshold priority score having the characteristics for presentation”, 0071-3).
It would have been obvious to combine the references before the effective filing date because they are in the same field of endeavor and to detect that no change has happened, user responded quickly or use thresholds in determining which notifications should be sent or what their priority is in order to save the consumer time and filter, rank and/or prioritize messages. Indicating that priority levels amount to thresholds can help with metrics indicating when to bother a user with a message, pop-up message.
10. The method of claim 2, wherein determining the first threshold further comprises: determining an upcoming user action (undefined action reads on any action at all, e.g., doing nothing) based on local user-provided communication (e.g., action can be turning on their phone, reading a message, buying a product, “in the case of a user who orders coffee each month from an online coffee vendor, a notification from the coffee vendor may be presented to the user querying whether it is time to order more coffee and with response options”, 0053; “user history, such as previous notification responses and related analytical data, past purchases, or browsing or search history.”, 0052); and determining the first threshold based on the [undefined] upcoming user action.
Lee teaches determining the first threshold based on an undefined action (user responds or doesn’t respond to a message, “message management computing platform 110 and/or message server module 112a may select a certain number of the highest priority messages having certain characteristics (e.g., four unresponded messages) and/or all messages above a threshold priority score having the characteristics for presentation”, 0071-3).
It would have been obvious to combine the references before the effective filing date because they are in the same field of endeavor and to detect that no change has happened, user responded quickly or use thresholds in determining which notifications should be sent or what their priority is in order to save the consumer time and filter, rank and/or prioritize messages. Indicating that priority levels amount to thresholds can help with metrics indicating when to bother a user with a message, pop-up message.
11. The method of claim 2, wherein determining the first threshold further comprises: determining a tone corresponding to a notification associated with the first action (“Notification response options, such as options 334, 336, and 338, may vary based on the particular notification message 332. By way of example and not limitation, response options may include options like: “Yes;” “No;” “Buy Now” or “Buy It Now,” as described above; Add To My Cart,” which in one embodiment adds the item to an online shopping cart associated with the user profile, payment provider, online merchant portal, or solicitor of the notification, for example; (Thus, a user who is not yet ready to purchase the item may, nevertheless, indicate interest in the item.); “Show Me More Items Like This,” “Not My Size,” “I'd Like More Information,” etc., which may direct the user to an app or website for related products, other sizes, additional information, etc.; “Remind Me Later” or “I'm Busy Now,” which may cause a notification to be presented at a later time or provide metadata to the solicitor causing the solicitor to initiate a second notification at a more optimal time to provoke a user response; “Call Me” or “Contact Me About This Offer;” “I'm Not Interested;” and “Not Relevant To Me.” In some embodiments, the response options and notification message, as well as parameters controlling when, where, or how the notification is presented, may be designed to provoke a particular user response, such as purchasing a product. In some instances, these purchases are referred to as impulse purchases or passion purchases.”, 0059); and determining the first threshold based on the tone corresponding to the notification.
Proulx fails to particularly call for the responses to be a tone or to use thresholds.
Lee teaches responses to be a tone or to use thresholds (determining a tone, “Message content analysis system 120 may use one or more of natural language processing, textual analysis, and/or computational linguistics techniques to determine a sentiment of the messages. In some embodiments, message content analysis system 120 may indicate a polarity of the message (e.g., a positive, negative, or neutral tone). Additionally or alternatively, message content analysis system 120 may indicate one or more moods associated with the message (e.g., angry, happy, neutral, and the like). Message content analysis system 120 may assign the one or more indicated sentiments to the corresponding message and send the indicated sentiments back to message management computing platform 110, as illustrated”, 0060).
It would have been obvious to combine the references before the effective filing date because they are in the same field of endeavor and to detect a tone by detecting a user is eager, upset, doesn’t want to be bothered, user responded quickly or use thresholds in determining which notifications should be sent or what their priority is in order to save the consumer time and filter, rank and/or prioritize messages. Indicating that priority levels amount to thresholds can help with metrics indicating when to bother a user with a message, pop-up message.
12. The method of claim 2, wherein determining the first threshold further comprises: determining a satisfaction metric corresponding to a user action, wherein the satisfaction metric is derived from user data (satisfied or not satisfied, buying more, buying again, not buying, not responding to offer messages, “Notification response options, such as options 334, 336, and 338, may vary based on the particular notification message 332. By way of example and not limitation, response options may include options like: “Yes;” “No;” “Buy Now” or “Buy It Now,” as described above; Add To My Cart,” which in one embodiment adds the item to an online shopping cart associated with the user profile, payment provider, online merchant portal, or solicitor of the notification, for example; (Thus, a user who is not yet ready to purchase the item may, nevertheless, indicate interest in the item.); “Show Me More Items Like This,” “Not My Size,” “I'd Like More Information,” etc., which may direct the user to an app or website for related products, other sizes, additional information, etc.; “Remind Me Later” or “I'm Busy Now,” which may cause a notification to be presented at a later time or provide metadata to the solicitor causing the solicitor to initiate a second notification at a more optimal time to provoke a user response; “Call Me” or “Contact Me About This Offer;” “I'm Not Interested;” and “Not Relevant To Me.” In some embodiments, the response options and notification message, as well as parameters controlling when, where, or how the notification is presented, may be designed to provoke a particular user response, such as purchasing a product. In some instances, these purchases are referred to as impulse purchases or passion purchases.”, 0059); and determining the first threshold based on the satisfaction metric corresponding to the user action.
Lee teaches determining the first threshold based on the satisfaction metric corresponding to the user action (determining how satisfied a person is by their tone, by determining response time, eagerness, dismissive, “Message content analysis system 120 may use one or more of natural language processing, textual analysis, and/or computational linguistics techniques to determine a sentiment of the messages. In some embodiments, message content analysis system 120 may indicate a polarity of the message (e.g., a positive, negative, or neutral tone). Additionally or alternatively, message content analysis system 120 may indicate one or more moods associated with the message (e.g., angry, happy, neutral, and the like). Message content analysis system 120 may assign the one or more indicated sentiments to the corresponding message and send the indicated sentiments back to message management computing platform 110, as illustrated”, 0060; thresholds, “message management computing platform 110 and/or message server module 112a may select a certain number of the highest priority messages having certain characteristics (e.g., four unresponded messages) and/or all messages above a threshold priority score having the characteristics for presentation”, 0071-3).
It would have been obvious to combine the references before the effective filing date because they are in the same field of endeavor and to detect a tone by detecting a user is eager, upset, doesn’t want to be bothered, user responded quickly or use thresholds in determining which notifications should be sent or what their priority is in order to save the consumer time and filter, rank and/or prioritize messages. Indicating that priority levels amount to thresholds can help with metrics indicating when to bother a user with a message, pop-up message.
13. The method of claim 2, wherein determining the first threshold further comprises: retrieving location data corresponding to a user action (“For instance, metadata might include information indicating when and how long a notification was presented on the mobile device, the applications currently running on the user device when the notification is presented, the user's location, user activity on the mobile device before and after the notification was presented, or similar data.”, 0013, 0015, 0023, 0034;
“Embodiments of the notifications policy may include parameters relating to the presentation of notifications such as colors, formatting, size, duration, authorized times, or conditions (e.g., only during waking hours or only when connected over Wi-Fi). For example, in an embodiment, notifications manager 262 uses metadata as described in connection to user response component 264 to determine present conditions associated with client device 260, such as location, time, currently running applications, etc., and determines to present a notification based on one or more of these conditions. For example, a user may be presented with a discount to purchase a new pair of running shoes immediately following conditions indicating that the user was likely running, which may be determined based on location and motion information and a currently running streaming music app. In this manner, notifications may be precisely timed to provoke a user response desired by the solicitor.”, 0041-2); and determining the first threshold based on the location data corresponding to the user action.
Lee teaches thresholds and location (“message management computing platform 110 and/or message server module 112a may select a certain number of the highest priority messages having certain characteristics (e.g., four unresponded messages) and/or all messages above a threshold priority score having the characteristics for presentation”, 0071-3; location in the sense that user may be at work or not available, “certain combinations of input features, such as scheduling information indicating the user is busy and/or a low priority score, may tend to indicate a “please follow up later” response classification (e.g., a response requesting the sender to try messaging again later when the user is less busy)”, 0067).
It would have been obvious to combine the references before the effective filing date because they are in the same field of endeavor and to detect a user is busy, available, at work, in motion/running, responding quickly when determining which notifications should be sent and when or what their priority is in order to save the consumer time and filter, rank and/or prioritize messages.
19. The non-transitory, computer-readable medium of claim 15, wherein determining the first threshold further comprises: determining an upcoming user action (to respond or buy or do nothing or not respond quickly) by: retrieving a local user-provided communication (Figs, 3); identifying a plurality of keywords using natural language processing, wherein the plurality of keywords is a subset of the local user-provided communication (“Data store 240 generally facilitates storage of notification information, notification parameters, policies, responses, user profiles, user IDs, user account information, notification payloads, response payloads, solicitor information, user history, processed and unprocessed analytical data including metadata related to notifications, and other stored information used in various embodiments of the invention. In some embodiments, the analytical data includes de-identified data based on metadata associated with notifications collected from a population of users. Such data may also include, for example, user responses and metadata, user engagement levels with notifications, or words or presentation formats that are more likely to induce particular user responses (such as purchases)”, 0051; “content used for a second notification and for generating a second notification payload may be determined based on information from the first notification response. In one example, the notification message words and/or response options may be modified based on information derived from the first notification response payload.”, 0078); and searching for the plurality of keywords in a keyword database, wherein the keyword database comprises a plurality of known keywords (e.g., yes, no, not my size) and a plurality of corresponding actions (0051, 0078); and determining the first threshold based on the upcoming user action (undefined action, buying not buying, responding, not responding, “Notification response options, such as options 334, 336, and 338, may vary based on the particular notification message 332. By way of example and not limitation, response options may include options like: “Yes;” “No;” “Buy Now” or “Buy It Now,” as described above; Add To My Cart,” which in one embodiment adds the item to an online shopping cart associated with the user profile, payment provider, online merchant portal, or solicitor of the notification, for example; (Thus, a user who is not yet ready to purchase the item may, nevertheless, indicate interest in the item.); “Show Me More Items Like This,” “Not My Size,” “I'd Like More Information,” etc., which may direct the user to an app or website for related products, other sizes, additional information, etc.; “Remind Me Later” or “I'm Busy Now,” which may cause a notification to be presented at a later time or provide metadata to the solicitor causing the solicitor to initiate a second notification at a more optimal time to provoke a user response; “Call Me” or “Contact Me About This Offer;” “I'm Not Interested;” and “Not Relevant To Me.” In some embodiments, the response options and notification message, as well as parameters controlling when, where, or how the notification is presented, may be designed to provoke a particular user response, such as purchasing a product. In some instances, these purchases are referred to as impulse purchases or passion purchases.”, 0059).
Lee teaches that priority level of messages inherently involves thresholds (“message management computing platform 110 and/or message server module 112a may select a certain number of the highest priority messages having certain characteristics (e.g., four unresponded messages) and/or all messages above a threshold priority score having the characteristics for presentation”, 0071-3) and real-time (“monitoring response times in real time, “Message management computing platform 110 may monitor user interactions with messages including user responses to messages, user selections of messages, user categorizations of messages, and the like”, 0042).
It would have been obvious to combine the references before the effective filing date because they are in the same field of endeavor and to use AI and/or or a model/classifier to determine which notifications should be sent or what their priority is in order to save the consumer time and filter, rank and/or prioritize messages. Indicating that priority levels amount to thresholds can with metrics indicating when to bother a user with a message, pop-up message.
20. The non-transitory, computer-readable medium of claim 15, wherein determining the first threshold further comprises: determining a tone corresponding to a notification associated with the first action by: identifying a response to the notification, wherein the response to the notification comprises a user message; and using a sentiment analysis model to identify the tone corresponding to the response(“Notification response options, such as options 334, 336, and 338, may vary based on the particular notification message 332. By way of example and not limitation, response options may include options like: “Yes;” “No;” “Buy Now” or “Buy It Now,” as described above; Add To My Cart,” which in one embodiment adds the item to an online shopping cart associated with the user profile, payment provider, online merchant portal, or solicitor of the notification, for example; (Thus, a user who is not yet ready to purchase the item may, nevertheless, indicate interest in the item.); “Show Me More Items Like This,” “Not My Size,” “I'd Like More Information,” etc., which may direct the user to an app or website for related products, other sizes, additional information, etc.; “Remind Me Later” or “I'm Busy Now,” which may cause a notification to be presented at a later time or provide metadata to the solicitor causing the solicitor to initiate a second notification at a more optimal time to provoke a user response; “Call Me” or “Contact Me About This Offer;” “I'm Not Interested;” and “Not Relevant To Me.” In some embodiments, the response options and notification message, as well as parameters controlling when, where, or how the notification is presented, may be designed to provoke a particular user response, such as purchasing a product. In some instances, these purchases are referred to as impulse purchases or passion purchases.”, 0059); and determining the first threshold based on the tone corresponding to the notification.
Proulx fails to particularly call for the responses to be a tone or to use thresholds.
Lee teaches responses to be a tone or to use thresholds (determining a tone, “Message content analysis system 120 may use one or more of natural language processing, textual analysis, and/or computational linguistics techniques to determine a sentiment of the messages. In some embodiments, message content analysis system 120 may indicate a polarity of the message (e.g., a positive, negative, or neutral tone). Additionally or alternatively, message content analysis system 120 may indicate one or more moods associated with the message (e.g., angry, happy, neutral, and the like). Message content analysis system 120 may assign the one or more indicated sentiments to the corresponding message and send the indicated sentiments back to message management computing platform 110, as illustrated”, 0060).
It would have been obvious to combine the references before the effective filing date because they are in the same field of endeavor and to detect a tone by detecting a user is eager, upset, doesn’t want to be bothered, user responded quickly or use thresholds in determining which notifications should be sent or what their priority is in order to save the consumer time and filter, rank and/or prioritize messages. Indicating that priority levels amount to thresholds can help with metrics indicating when to bother a user with a message, pop-up message.
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.
Claim(s) 14 is rejected under 35 U.S.C. 103 as being unpatentable over the combination of Proulx (US 2016/0086241) and Lee (US 2018/0253659) in view of Kumar (US 2019/0075069).
14. The method of claim 2, wherein determining the first threshold further comprises: retrieving gyroscope data and accelerometer data corresponding to a user action (motion data, “computing device 100 may be equipped with accelerometers or gyroscopes that enable detection of motion.”, 0032; “Embodiments of the notifications policy may include parameters relating to the presentation of notifications such as colors, formatting, size, duration, authorized times, or conditions (e.g., only during waking hours or only when connected over Wi-Fi). For example, in an embodiment, notifications manager 262 uses metadata as described in connection to user response component 264 to determine present conditions associated with client device 260, such as location, time, currently running applications, etc., and determines to present a notification based on one or more of these conditions. For example, a user may be presented with a discount to purchase a new pair of running shoes immediately following conditions indicating that the user was likely running, which may be determined based on location and motion information and a currently running streaming music app. In this manner, notifications may be precisely timed to provoke a user response desired by the solicitor.”, 0041-2); and determining the first threshold based on the gyroscope data and the accelerometer data corresponding to the user action.
Lee teaches thresholds and location (“message management computing platform 110 and/or message server module 112a may select a certain number of the highest priority messages having certain characteristics (e.g., four unresponded messages) and/or all messages above a threshold priority score having the characteristics for presentation”, 0071-3; location in the sense that user may be at work or not available, “certain combinations of input features, such as scheduling information indicating the user is busy and/or a low priority score, may tend to indicate a “please follow up later” response classification (e.g., a response requesting the sender to try messaging again later when the user is less busy)”, 0067).
It would have been obvious to combine the references before the effective filing date because they are in the same field of endeavor and to detect a user is busy, available, at work, in motion/running, responding quickly when determining which notifications should be sent and when or what their priority is in order to save the consumer time and filter, rank and/or prioritize messages.
Kumar teaches using both gyroscopes and accelerometers can be used (“location sensor 214 may include accelerometers that measure linear acceleration and/or rotation angle of the apparatus 202, gyroscopes that measure the angular rate of rotation of the apparatus”, 0031).
It would have been obvious to combine the references before the effective filing date because they are in the same field of endeavor and to detect a user is busy, available, at work, in motion/running, using both gyroscopes and accelerometers when determining which notifications should be sent and when or what their priority is in order to save the consumer time and filter, rank and/or prioritize messages.
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Miles (US 2023/0235495) teaches impulse buying (0023), AI models (0033).
Any inquiry concerning this communication or earlier communications from the examiner should be directed to DAVID R VINCENT whose telephone number is (571)272-3080. The examiner can normally be reached ~Mon-Fri 12-8:30.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Alexey Shmatov can be reached at 5712703428. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/DAVID R VINCENT/Primary Examiner, Art Unit 2123
Prosecution Insights