DETAILED ACTION
This action is responsive to the application filed on 03/16/2023. Claims 1-20 are pending and have been examined.
This action is Non-final.
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 .
Priority
Applicant’s claim for the benefit of a prior-filed application under 35 U.S.C. 119(e) or under 35 U.S.C.
120, 121, 365(c), or 386(c) is acknowledged.
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.
Regarding claim 1,
Step 1: This claim is directed to a computer-implemented method (a process), which is one of the four statutory categories. Therefore, claim 1 satisfies Step 1.
Step 2A Prong 1:
“A computer-implemented method for utilizing predictions of future real-world events to make actionable decisions that prepare users for the real-world events, comprising: matching… one or more prediction results of the plurality of prediction results to the semantic information;” -- The limitation is directed to a method for utilizing predictions for real-world events and making decisions for users, and matching prediction results to semantic information. The limitation is directed to a process that can be performed in the human mind using evaluation, observation, and judgement (with aid of pen and paper) and thus the limitation is directed to a mental process.
Step 2A Prong 2 and Step 2B:
“obtaining…a plurality of prediction results in association with an event; receiving…semantic information; generating…one or more actionable outputs by processing the one or more prediction results and the semantic information…forwarding…the one or more actionable outputs, wherein the one or more actionable outputs comprise information that allows a user of the user device to act on in advance of occurrence of the event.” -- The limitation recites obtaining some prediction results that are associated with an event, generating outputs by processing gathered data/semantic information, and forwarding one or more actions outputs that comprises information for users to have before an event. The limitation in its recited entirety is an insignificant, extra-solution activity that cannot be integrated to a practical application (see MPEP 2106.05(g)). Furthermore, under Step 2B, the act of forwarding/transmitting data over a network/system. Thus, it does not provide significantly more than the judicial exception (see MPEP 2106.05(d)(II)).
“by a computer and from a machine learning model,…by the computer…by the computer for the user device…by the computer to the user device” -- The limitation recite applying proceeding and previous tasks on a computer, machine learning model, and merely states its application to the device. The limitation amounts to no more than mere instructions to apply onto a computer, and it does not integrate to a practical application, nor provide significantly more than the judicial exception (see MPEP 2106.05(f)).
Thus, claim 1 is non-patent eligible. Claims 14 and 20 are analogous to claim 1 aside from claim type and minimal changes, and therefore the same rejection can be applied.
Regarding claim 2,
Step 1: This claim is directed to a computer-implemented method (a process), which is one of the four statutory categories. Therefore, claim 2 satisfies Step 1.
There are no elements to be evaluated under Step 2A Prong 1.
Step 2A Prong 2 and Step 2B:
“The computer-implemented method of claim 1, wherein each of the plurality of prediction results comprises one or more parameters of: (1) the event; (2) a time window that the event occurs; (3) a geospatial area that the event occurs; (4) an intensity of the event; and (5) a probability of occurrence of the event corresponding to one or more of (2), (3), and (4).” -- The limitation recites prediction results will further comprise a list of parameters recited in the claim. The claim does not amount to no more than mere further limiting to a field of use/environment, without any integration to a practical application, nor significantly more than the judicial exception (see MPEP 2106.05(h)).
Thus, claim 2 is non-patent eligible. Claim 15 is analogous to claim 2, aside from claim type, and thus the same rejection will apply.
Regarding claim 3,
Step 1: This claim is directed to a computer-implemented method (a process), which is one of the four statutory categories. Therefore, claim 3 satisfies Step 1.
Step 2A Prong 1:
“The computer-implemented method of claim 1, wherein matching, by the computer, the one or more prediction results of the plurality of prediction results to the semantic information is based on a predetermined mapping relationship between the plurality of prediction results and the semantic information.” -- The limitation is directed to matching prediction results to semantic information based on predetermined mapping relationships between prediction results and semantic information. The limitation is directed to a process that can be performed in the human mind using evaluation, observation, and judgement (with aid of pen and paper) and thus the limitation is directed to a mental process.
There are no elements to be evaluated under Step 2A Prong 2 and Step 2B.
Thus, claim 3 is non-patent eligible. Claim 16 is analogous to claim 3, aside from claim type, and thus the same rejection will apply.
Regarding claim 4,
Step 1: This claim is directed to a computer-implemented method (a process), which is one of the four statutory categories. Therefore, claim 4 satisfies Step 1.
There are no elements to be evaluated under Step 2A Prong 1.
Step 2A Prong 2 and Step 2B:
“The computer-implemented method of claim 1, wherein generating, by the computer for the user device, one or more actionable outputs by processing the one or more prediction results and the semantic information, comprises: providing the one or more prediction results and the semantic information to a trained machine learning model; and receiving an output from the trained machine learning model, including the one or more actionable outputs.” -- The limitation recites generating, for the computer device, actionable outputs by processing prediction results and semantic information, for which comprises providing prediction results and semantic information to a trained machine learning model, and receiving output from trained mode including said actionable outputs. The limitation is directed to an insignificant, extra-solution activity that cannot be integrated to a practical application (see MPEP 2106.05(g)). Furthermore, under Step 2B, generating data by processing gathered data and transmitting/receiving data over a network is a well-understood, routine and conventional activity (WURC) which cannot provide significantly more than the judicial exception (see MPEP 2106.05(d)(II)).
Thus, claim 4 is non-patent eligible. Claim 17 is analogous to claim 4, aside from claim type, and thus the same rejection will apply.
Regarding claim 5,
Step 1: This claim is directed to a computer-implemented method (a process), which is one of the four statutory categories. Therefore, claim 5 satisfies Step 1.
There are no elements to be evaluated under Step 2A Prong 1.
Step 2A Prong 2 and Step 2B:
“The computer-implemented method of claim 1, wherein the one or more actionable outputs is automatically executed by the user device.” -- The limitation recites that the actionable outputs is automatically executed by the user device (computer), and does not integrate to a practical application and does not provide significantly more than the judicial exception (see MPEP 2106.05(f)).
Thus, claim 5 is non-patent eligible. Claim 18 is analogous to claim 5, aside from claim type, and thus the same rejection will apply.
Regarding claim 6,
Step 1: This claim is directed to a computer-implemented method (a process), which is one of the four statutory categories. Therefore, claim 6 satisfies Step 1.
There are no elements to be evaluated under Step 2A Prong 1.
Step 2A Prong 2 and Step 2B:
“The computer-implemented method of claim 1, wherein the one or more actionable outputs is displayed by the user device to a user using a graphic user interface (GUI).” -- The limitation recites that the output will be displaying by the user device to a user and also using a GUI. The limitation amounts to no more than mere instructions to apply onto a computer and it does not integrate to a practical application nor provides significantly more than the judicial exception (see MPEP 2106.05(f)).
Thus, claim 6 is non-patent eligible. Claim 19 is analogous to claim 6, aside from claim type, and thus the same rejection will apply.
Regarding claim 7,
Step 1: This claim is directed to a computer-implemented method (a process), which is one of the four statutory categories. Therefore, claim 7 satisfies Step 1.
There are no elements to be evaluated under Step 2A Prong 1.
Step 2A Prong 2 and Step 2B:
“The computer-implemented method of claim 1, wherein the event comprises a natural event or condition.” -- The limitation recites that the event will further comprise natural event or condition. The limitation amounts to no more than further limiting to a field of use/environment and it does not integrate to a practical application, nor provide significantly more than the judicial exception (see MPEP 2106.05(h)).
Thus, claim 7 is non-patent eligible.
Regarding claim 8,
Step 1: This claim is directed to a computer-implemented method (a process), which is one of the four statutory categories. Therefore, claim 8 satisfies Step 1.
There are no elements to be evaluated under Step 2A Prong 1.
Step 2A Prong 2 and Step 2B:
“The computer-implemented method of claim 7, wherein the natural event or condition comprises a natural hazardous event.” -- The limitation recites that the natural event/condition will further comprise natural hazardous event. The limitation amounts to no more than further limiting to a field of use/environment and it does not integrate to a practical application, nor provide significantly more than the judicial exception (see MPEP 2106.05(h)).
Thus, claim 8 is non-patent eligible.
Regarding claim 9,
Step 1: This claim is directed to a computer-implemented method (a process), which is one of the four statutory categories. Therefore, claim 9 satisfies Step 1.
There are no elements to be evaluated under Step 2A Prong 1.
Step 2A Prong 2 and Step 2B:
“The computer-implemented method of claim 1, wherein the semantic information comprises information corresponding to a product, a service, and/or a provider thereof” -- The limitation recites that the semantic information that comprises product, service, and a provider-corresponded information. The limitation amounts to no more than mere further limiting to a field of use/environment, and it does not integrate to a practical application, nor provide significantly more than the judicial exception (see MPEP 2106.05(h)).
Thus, claim 9 is non-patent eligible.
Regarding claim 10,
Step 1: This claim is directed to a computer-implemented method (a process), which is one of the four statutory categories. Therefore, claim 10 satisfies Step 1.
There are no elements to be evaluated under Step 2A Prong 1.
Step 2A Prong 2 and Step 2B:
“The computer-implemented method of claim 1, wherein obtaining, by the computer and from the machine learning model, the plurality of prediction results in association with the event is based on the semantic information.” -- The limitation recites using the computer to obtain prediction results associated with an event based on semantic information. The limitation is directed to an insignificant, extra-solution activity that cannot be integrated to a practical application (see MPEP 2106.05(g)). Furthermore, under Step 2B, the act of obtaining data based on gathered information to be implemented onto the computer is a well-understood, routine, and conventional activity (WURC) that cannot provide significantly more than the judicial exception (see MPEP 2106.05(d)(II)).
Thus, claim 10 is non-patent eligible.
Regarding claim 11,
Step 1: This claim is directed to a computer-implemented method (a process), which is one of the four statutory categories. Therefore, claim 11 satisfies Step 1.
Step 2A Prong 1:
“The computer-implemented method of claim 1, wherein generating the one or more actionable outputs by processing the one or more prediction results and the semantic information is in response to determining that at least one of the one or more parameters of the event satisfies a predetermined threshold.” -- The limitation is directed to generating outputs by processing results and semantic information after determining that one of the parameters of the event will satisfy a predetermined threshold. The limitation is directed to a process that can be performed in the human mind using evaluation, observation, and judgement, and thus the limitation is directed to a mental process.
There are no elements to be evaluated under Step 2A Prong 2 and Step 2B.
Thus, claim 11 is non-patent eligible.
Regarding claim 12,
Step 1: This claim is directed to a computer-implemented method (a process), which is one of the four statutory categories. Therefore, claim 11 satisfies Step 1.
Step 2A Prong 1:
“The computer-implemented method of claim 1, wherein matching, by the computer, the one or more prediction results of the plurality of prediction results to the semantic information is by calculating a correlation between historical events and historical data in correspondence to the semantic information.” -- The limitation is directed to matching one or more prediction results of the group of prediction results to the semantic information by calculating a correlation between historical events and data corresponding to semantic information. The limitation is directed primarily to a process that can be performed in the human mind using evaluation, observation, and judgement (with aid of pen and paper) and thus the limitation is directed to a mental process.
There are no elements to be evaluated under Step 2A Prong 2 and Step 2B.
Thus, claim 12 is non-patent eligible.
Regarding claim 13,
Step 1: This claim is directed to a computer-implemented method (a process), which is one of the four statutory categories. Therefore, claim 13 satisfies Step 1.
There are no elements to be evaluated under Step 2A Prong 1.
Step 2A Prong 2 and Step 2B:
“The computer-implemented method of claim 1, wherein matching, by the computer, the one or more prediction results of the plurality of prediction results to the semantic information is by using a machine learning model trained on historical events and historical data in correspondence to the semantic information.” -- The limitation is directed to matching prediction results to semantic information by using a trained machine learning model that’s in correspondence to semantic information. The limitation amounts to no more than mere instructions to apply, and does not integrate to a practical application, nor provides significantly more than the judicial exception (see MPEP 2106.05(f)).
Thus, claim 13 is non-patent eligible.
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.
The factual inquiries 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, 3-11, 13-14, 16-20 is rejected under 35 U.S.C. 103 as being unpatentable over US 20210215848 A1, by Mukherjee et. al. (referred herein as Mukherjee) in view of US 20200134545 A1, by Appel et. al. (referred herein as Appel).
Regarding claim 1, Mukherjee teaches:
A computer-implemented method for utilizing predictions of future real-world events to make actionable decisions that prepare users for the real-world events, comprising: ([Mukherjee, page 1] “A weather intelligence system retrieves weather forecast data for a number of geographic regions. The weather intelligence system determines, using the weather forecast data for each of the geographic regions, a set of geographic regions predicted to experience a weather anomaly, or unusual weather condition, during a particular time interval…The weather intelligence system can then programmatically enable a trigger to transmit a service - related offer associated with the weather anomaly to user devices located within one of the geographic regions predicted to experience a weather anomaly, or unusual weather condition, during a particular time interval.”, wherein the examiner interprets a weather intelligence system that retrieves weather forecast data and determines geographic regions predicted to experience a weather anomaly and programmatically enables a trigger to transmit a service-related offer to user devices to be the same as a “computer-implemented method for utilizing predictions of future real-world events to make actionable decisions that prepare users for the real-world events” because both are directed to systems that use predictive data about future conditions to generate and deliver actionable information to users before those conditions occur)
obtaining, by a computer and from a machine learning model, a plurality of prediction results in association with an event; ([Mukherjee, [0026] “Using the weather forecast data, the weather intelligence system 120 runs an AI model to predict whether a geographic region will experience an unusual weather condition, such as being hotter or colder than its regular weather conditions. The AI model learns the short-term weather data patterns over the last few days at a given time and makes predictions based on the learning” and [Mukherjee, [0014]] “The weather intelligence system determines , through a machine - learning process , whether the weather forecast data indicates conditions that people would generally consider to be unusually hot or cold. The weather intelligence system can then program matically enable a trigger to transmit a service - related offer associated with the unusual weather condition to user devices located within one of the geographic regions where that weather condition is determined”, wherein the examiner interprets “the weather intelligence system 120 runs an AI model to predict whether a geographic region will experience an unusual weather condition” and “determines, through a machine-learning process, whether the weather forecast data indicates conditions” to be the same as “obtaining, by a computer and from a machine learning model, a plurality of prediction results in association with an event” because both are directed to a computer system using artificial intelligence or machine learning to generate multiple predictions about future conditions or events.)
receiving, by the computer and from a user device, semantic information; ([Mukherjee, [0046]] “In some variations, the consumer interface 270 on the consumer process 250 receives a notification that a user has breached a geofence... The notification may include an identifier for the user device and other data, including the location of the user device, or the consumer process 250 can retrieve the location of the user device through the database of user profiles 284 and the identifier.”, [Mukherjee, [0029]] “the activity information can include descriptive information relating to an item that is the subject of the end user activity . Still further, the activity information can include contextual information (e.g., time of day, day of week , calendar day , etc. ) related to an activity that the end user is detected as having performed “, and [Mukherjee, [0054]] “Upon receiving the list of zip codes ( 435), the consumer process queries a user database for user profiles that match users currently located in ( or near ) any of the zip codes on the list (440).”wherein the examiner interprets “the consumer interface 270” “receives a notification” that “may include an identifier for the user device and other data, including the location of the user device” and “the activity information can include descriptive information” “and contextual information” to be the same as “receiving, by the computer and from a user device, semantic information” because both are directed to a computer system obtaining meaningful contextual data from or about user devices that describes the user's situation, activities, or characteristics.)
matching, by the computer, one or more prediction results of the plurality of prediction results to the semantic information; ([Mukherjee, [0054]] “Upon receiving the list of zip codes (435), the consumer process queries a user database for user profiles that match users currently located in ( or near ) any of the zip codes on the list (440).”, [Mukherjee, [0036]] “This not only helps the service provider to match the right set of zip codes to the right consumer process.”, and [Mukherjee, [0047]] “The consumer interface 270 can call the weather intelligence service 200 provider and request the current weather condition for the location of the user device which breached the geofence. The weather intelligence service 200 looks up the weather condition in the database of region contexts 212 and returns the current weather condition for the location to the consumer interface 270.”, wherein the examiner interprets “the consumer process queries a user database for user profiles that match users currently located in (or near) any of the zip codes on the list” and “match the right set of zip codes to the right consumer process” and “looks up the weather condition” “for the location of the user device” to be the same as “matching, by the computer, one or more prediction results of the plurality of prediction results to the semantic information” because both are directed to a computer system correlating prediction data for specific locations or conditions with user information or characteristics to identify relevant matches.)
forwarding, by the computer to the user device, the one or more actionable outputs, wherein the one or more actionable outputs comprise information that allows a user of the user device to act on in advance of occurrence of the event. ([Mukherjee, page 1] “The weather intelligence system can then programmatically enable a trigger to transmit a service-related offer associated with the weather anomaly to user devices located within one of the geographic regions where that weather anomaly is determined.”, [Mukherjee, [0045]] “For example, a retail location may configure an offer to send a push notification offering a discount on cold drinks to user devices of registered users within an unusually hot geographic region, but only for users who are not already regular customers of the business”, wherein the examiner interprets “programmatically enable a trigger to transmit a service-related offer associated with the weather anomaly to user devices” “where that weather anomaly is determined” and “send a push notification offering a discount on cold drinks to user devices” “within an unusually hot geographic region” to be the same as “forwarding, by the computer to the user device, the one or more actionable outputs, wherein the one or more actionable outputs comprise information that allows a user of the user device to act on in advance of occurrence of the event” because both are directed to a computer system delivering actionable information to user devices that enables users to take preparatory action before a predicted condition or event actually occurs.)
Mukherjee does not teach generating, by the computer for the user device, one or more actionable outputs by processing the one or more prediction results and the semantic information; and
Appel teaches generating, by the computer for the user device, one or more actionable outputs by processing the one or more prediction results and the semantic information; and ([Appel, [0035] - [0036]] “The cognitive model 315 may compute the vulnerability score of each entity … for a given period in the future … may make one or more recommendations for a mitigation strategy … recommend ways to mitigate the impact of an external event.”, [Appel, [0031]] “Examples of data that may be collected include : weather / climate data” and “price and other market data & historical risk data” and “supply chain network data about the entity information”, and [Appel, [0046]] “Recommendation component 325 may generate a recommendation for at least one entity”, wherein the examiner interprets “recommendations for a mitigation strategy” and “recommend ways to mitigate the impact of an external event” to be the same as “one or more actionable outputs” because they are both directed to concrete, action-oriented recommendations produced in view of an anticipated real-world/external event, and the examiner interprets “compute the vulnerability score … for a given period in the future” to be the same as “processing the one or more prediction results” because they are both directed to operating on prediction outputs that characterize a future condition/event. The examiner further interprets “weather / climate data”, “market data & historical risk data”, and “entity information” to be the same as the semantic information because they are both directed to contextual and business information used by the system to determine what output/recommendation should be produced for a particular recipient/context. Finally, the examiner interprets “generate a recommendation for at least one entity” to be the same as “by the computer for the user device” because they are both directed to generating the actionable output for a particular recipient endpoint.)
Mukherjee, Appel, and the instant application are analogous art because they are all directed to generating actionable outputs for users based on predicted future real-world/external events.
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the invention to modify the computer-implemented metho of prediction utilization disclosed by Mukherjee to include the mitigation strategy generation disclosed by Appel. One would be motivated to do so to effectively provide concrete, action-oriented outputs to users that specify what actions to take in response to the predicted event, as suggested by Appel ([Appel, [0036]] “recommend ways to mitigate the impact of an external event.”). Claims 14 and 20 are analogous to claim 1, aside from minute differences, thus I still performed an analysis for all, but the same rejection in content is analogous and can apply to all three.
Regarding claim 3, Mukherjee and Appel teach The computer-implemented method of claim 1, (see mapping for claim 1)
Mukherjee further teaches:
wherein matching, by the computer, the one or more prediction results of the plurality of prediction results to the semantic information ([Mukherjee, [0035]] “In order to register, a handshake happens between a given consumer process 250 and the weather intelligence service 200 (i.e., the service provider) so that the service provider uses the right namespaces for the consumers and the right services. In addition, this also helps the service provider to match the right set of zip codes to the right consumer process.” wherein the examiner interprets “match the right set of zip codes to the right consumer process” to be the same as matching … the one or more prediction results … to the semantic information because they are both directed to associating predicted-event outputs for a geographic region (zip codes/regions tied to a predicted condition) with contextual information defining what those outputs should be paired with (the configured/registered consumer process that is to receive them).)
is based on a predetermined mapping relationship between the plurality of prediction results and the semantic information. ([Mukherjee, [0036]] “Prior to the handshake process, a registration scheduler 260 receives configuration data, including the type of weather trigger (e.g., unusually hot, unusually cold, or inclement) that consumer process 250 should be configured to listen to and sign up credentials for the trigger. In one implementation, the registration scheduler 260 executes every 3 hours and posts the sign-up credentials as handshake data to the service provider. A parser 220 on the service provider side parses the handshake data, extracts artifact details identifying the type of weather trigger and the consumer process 250, and saves the details in an artifact table 222 for use by a publisher process 230 after processing weather forecast data.” and ([Mukherjee, [0053]-[0054]), “Then, based on the handshake registration data, the service provider determines which zip codes to send to which consumer processes (420). For any given consumer process, the consumer receives a list of zip codes from the service provider that match the unusual weather condition alert, or anomaly, that the consumer process is registered to receive (430)” wherein the examiner interprets “receives configuration data, including the type of weather trigger … configured to listen to” and “saves the details in an artifact table” to be the same as “based on a predetermined mapping relationship between the plurality of prediction results and the semantic information” because they are both directed to establishing and storing, in advance of later processing, an explicit association between (i) predicted-event outputs (trigger type / resulting published region outputs) and (ii) semantic/contextual descriptors (the registered consumer process configuration that defines what those outputs should be matched to). The examiner further interprets “based on the handshake registration data” as “based on a predetermined mapping relationship” because they are both directed to using pre-registered configuration/registration data to control how predicted-event outputs are routed/paired to the corresponding contextual entity (the registered consumer process.)
Regarding claim 4, Mukherjee and Appel teach The computer-implemented method of claim 1, (see mapping for claim 1)
Mukherjee further teaches:
wherein generating, by the computer for the user device, one or more actionable outputs by processing the one or more prediction results and the semantic information, comprises: ([Mukhjerjee, [0014]] “The weather intelligence system determines, through a machine-learning process, whether the weather forecast data indicates conditions that people would generally consider to be unusually hot or cold. The weather intelligence system can then programmatically enable a trigger to transmit a service-related offer associated with the unusual weather condition to user devices located within one of the geographic regions where that weather condition is determined.”, Wherein the examiner interprets “programmatically enable a trigger to transmit a service-related offer associated with the unusual weather condition to user devices” to be the same as “generating, by the computer for the user device, one or more actionable outputs by processing the one or more prediction results and the semantic information” because they are both directed to a computer system using prediction-related conditions and contextual information to produce an output that is intended to prompt a user action on a user device.)
providing the one or more prediction results and the semantic information to a trained machine learning model; and ([Mukherjee, [0026]] “Using the weather forecast data, the weather intelligence system 120 runs an AI model to predict whether a geographic region will experience an unusual weather condition, such as being hotter or colder than its regular weather conditions. The AI model learns the short-term weather data patterns over the last few days at a given time and makes predictions based on the learning.”, wherein the examiner interprets “runs an AI model to predict whether a geographic region will experience an unusual weather condition” to be the same as “providing the one or more prediction results to a trained machine learning model” because they are both directed to supplying learned model-based predictive processing using forecast data as input to generate event-related prediction results. The examiner further interprets “the AI model learns the short-term weather data patterns over the last few days” to be the same as the “trained machine learning model” because they are both directed to a machine learning model that has been trained on historical data to perform predictive inference.)
receiving an output from the trained machine learning model, including the one or more actionable outputs. ([Mukherjee, [0045]] “A rules engine 280 can then index user profiles from a database of user profiles 284 to determine which users registered with the environment are currently located within (or near) any of the geographic regions on the list” and [Mukherjee, [0056]] “Once the process has filtered users that match the business rules, offers are sent to the appropriate users (460).”, wherein the examiner interprets “index user profiles from a database of user profiles to determine which users … are currently located within (or near) any of the geographic regions on the list” to be the same as receiving an output from the trained machine learning model because they are both directed to obtaining a result produced by computational processing that identifies a subset of users relevant to predicted event conditions. The examiner further interprets “offers are sent to the appropriate users” to be the same as including the one or more actionable outputs because they are both directed to producing and delivering an output that enables a user to take an action in response to a predicted real-world event.)
Regarding claim 5, Mukherjee and Appel teach The computer-implemented method of claim 1, (see mapping for claim 1)
Mukherjee further teaches:
wherein the one or more actionable outputs is automatically executed by the user device. ([Mukherjee, page 1] “can then programmatically enable a trigger to transmit a service-related offer associated with the weather anomaly to user devices located within one of the geographic regions where that weather anomaly is determined.” and [Mukherjee, [0056]] “offers can cause a user device to display the offer on a user interface of an application (e.g., through the user of a push notification)”, wherein the examiner interprets “enable a trigger to transmit a service-related offer” to be the same as the one or more actionable outputs because they are both directed to an output generated for a user in response to a predicted real-world event and provided to the user device for action. The examiner further interprets “offer” to be the same as the one or more actionable outputs because they are both directed to an output delivered to a user device for the user to act upon, and wherein the examiner interprets “cause a user device to display” to be the same as is automatically executed by the user device because they are both directed to the user device automatically performing an action using the provided output without requiring the user to manually execute the output.)
Mukherjee, Appel, and the instant application are analogous art because they are all directed to providing actionable outputs to a user device such that the user device automatically performs an action with the actionable output.
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the invention to modify the computer-implemented method claim 1 disclosed by Mukherjee and Appel to include the process of displaying the offer disclosed by Mukherjee. One would be motivated to do so to effectively ensure the actionable outputs generated by Appel are automatically executed/presented on the user device so the user timely receives and can act upon the output without manual initiation, as suggested by Mukherjee ([Mukherjee, [0056]] “through the user of a push notification”).
Regarding claim 6, Mukherjee and Appel teach The computer-implemented method of claim 1, (see mapping for claim 1)
Appel further teaches wherein the one or more actionable outputs ([Appel, [0036]] “In some cases, the supply chain vulnerability system 300 may make one or more recommendations for a mitigation strategy. For example, the supply chain vulnerability system 300 may determine whether the vulnerability score for a particular entity is above (or below) a threshold, and if so, recommend ways to mitigate the impact of an external event.” wherein the examiner interprets “recommendation for a mitigation strategy” to be the same as “one or more actionable outputs” because they are both describing actionable outputs generated by the system (recommendations/mitigation strategies).
Mukherjee teaches is displayed by the user device to a user using a graphic user interface (GUI). ([Mukherjee, [0056]] “In some implementations, offers can cause a user device to display the offer on a user interface of an application (e.g., through the user of a push notification) registered with the platform environment.”, wherein the examiner interprets “user interface” of an application to be the same as “the one or more actionable outputs is displayed by the user device to a user using a graphic user interface (GUI)” because they are both describing actionable outputs (offers/recommendations) being displayed to users on a user device through a user interface.)
Mukherjee, Appel, and the instant application are analogous art because they are all directed to generating actionable outputs in response to predicted or forecast real-world/external events, and presenting those actionable outputs to a user via a user device interface.
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the invention to modify the computer-implemented method claim 1 disclosed by Mukherjee and Appel to include the notification system disclosed by Mukherjee. One would be motivated to do so to effectively present the mitigation recommendations/actionable outputs to users through the user device interface so that users can timely receive and act upon the recommendations, as suggested by Mukherjee ([Mukherjee, [0056]] “through the user of a push notification”).
Regarding claim 7, Mukherjee and Appel teach The computer-implemented method of claim 1, (see mapping for claim 1)
Mukherjee further teaches wherein the event comprises a natural event or condition. ([Mukherjee, [0014]] “The weather intelligence system determines, using the weather forecast data for each of the geographic regions, a set of geographic regions predicted to experience an unusual weather condition, or weather anomaly, during a particular time interval. The weather alert can be the result of abnormal weather conditions such as unusually hot or cold temperatures and inclement weather.”, wherein the examiner interprets “weather anomaly” and “unusual weather condition” to be the same as “a natural event or condition” because they are both identifying the type of event being detected/processed by the system as a naturally occurring phenomenon (weather conditions, temperature anomalies, precipitation) rather than a human-initiated or artificial event.)
Regarding claim 8, Mukherjee and Appel teach The computer-implemented method of claim 7, (see mapping for claim 7)
Mukherjee further teaches wherein the natural event or condition comprises a natural hazardous event. ([Mukherjee, [0014]] “The weather intelligence system determines, using the weather forecast data for each of the geographic regions, a set of geographic regions predicted to experience an unusual weather condition, or weather anomaly, during a particular time interval. The weather alert can be the result of abnormal weather conditions such as unusually hot or cold temperatures and inclement weather.”, wherein the examiner interprets “unusual weather condition , or weather anomaly” to be the same as the natural event or condition because they are both directed to a naturally occurring real-world condition/event, and wherein the examiner interprets “abnormal weather conditions such as unusually hot or cold temperatures and inclement weather” to be the same as a natural hazardous event because they are both directed to naturally occurring adverse conditions that can create hazardous impacts.)
Regarding claim 9, Mukherjee and Appel teach The computer-implemented method of claim 1, (see mapping for claim 1)
Mukherjee further teaches wherein the semantic information comprises information corresponding to a product, a service, and/or a provider thereof. ([Mukherjee, [0045]] “A consumer interface 270 receives the appropriate list of geographic regions from the service provider and creates corresponding weather-related triggers for each of the geographic regions on the list. A rules engine 280 can then index user profiles from a database of user profiles 284 to determine which users registered with the environment are currently located within (or near) any of the geographic regions on the list. The rules engine 280 can also filter the user profiles by one or more programmed business rules 282, which enterprise customers can configure to target desired characteristics for offer recipients. For example, a retail location may configure an offer to send a push notification offering a discount on cold drinks to user devices of registered users within an unusually hot geographic region, but only for users who are not already regular customers of the business.”, wherein the examiner interprets “one or more programmed business rules 282, which enterprise customers can configure” to be the same as the semantic information comprises information corresponding to a product, a service, and/or a provider thereof because they are both directed to configurable, meaning-bearing context information supplied by an enterprise/provider that specifies what offering is being provided and to whom. The examiner further interprets “cold drinks” to be the same as a product because they are both directed to a specific item being offered, and wherein the examiner interprets “retail location” to be the same as a provider thereof because they are both directed to an entity that provides the product and/or service referenced by the offer, and wherein the examiner interprets “offer” and “push notification offering a discount” to be the same as a service because they are both directed to an offering made available to users by the provider in connection with the system’s trigger-based operation.)
Regarding claim 10, Mukherjee and Appel teach The computer-implemented method of claim 1, (see mapping for claim 1).
Mukherjee further teaches:
wherein obtaining, by the computer and from the machine learning model, the plurality of prediction results in association with the event ([Mukherjee, [0014]] “The weather intelligence system determines, using the weather forecast data for each of the geographic regions, a set of geographic regions predicted to experience an unusual weather condition, or weather anomaly, during a particular time interval. The weather alert can be the result of abnormal weather conditions such as unusually hot or cold temperatures and inclement weather. The weather intelligence system determines, through a machine-learning process, whether the weather forecast data indicates conditions that people would generally consider to be unusually hot or cold.”, wherein the examiner interprets “through a machine-learning process” to be the same as obtaining, by the computer and from the machine learning model because they are both directed to a computer obtaining predictive output generated by a machine learning model. The examiner further interprets “a set of geographic regions predicted to experience” to be the same as the plurality of prediction results because they are both directed to multiple prediction outputs, and wherein the examiner interprets “weather anomaly , or unusual weather condition” to be the same as the event because they are both directed to a real-world event/condition being predicted.)
is based on the semantic information. ([Mukherjee, [0025]] “Once a geographic region identifier (e.g., a zip code) is passed in the header of the API, the weather data provider 130 returns the weather forecast data for that zip code.”, wherein the examiner interprets “a geographic region identifier ( e.g. , a zip code )” to be the same as the semantic information because they are both directed to a meaningful descriptor used to condition/select the information used for prediction. The examiner further interprets “returns the weather forecast data for that zip code” to be the same as obtaining … the plurality of prediction results … is based on the semantic information because they are both directed to the predictive results being obtained based on an input descriptor (here, the region identifier) that determines what data is used to generate the prediction results.)
Regarding claim 11, Mukherjee and Appel teach The computer-implemented method of claim 1, (see mapping for claim 1)
Mukherjee further teaches:
wherein generating the one or more actionable outputs by processing the one or more prediction results and the semantic information ([Mukherjee, [0032]] “Business rules can include any configurable options and conditions that an enterprise may place on an offer, such as requiring that a user be within a certain distance of a retail location or requiring that the user not be recognized as a regular customer of the business. Upon activation of a trigger and satisfaction of any business rules associated with the offer, the network computer system 100 may send the offer to a user device 160”, wherein the examiner interprets “activation of a trigger” to be the same as processing the one or more prediction results because they are both directed to using a prediction-driven condition as an input that drives output generation. The examiner further interprets “satisfaction of any business rules associated with the offer” to be the same as processing the semantic information because they are both directed to using meaning-bearing contextual constraints as an input to determine the output, and wherein the examiner interprets “send the offer” to be the same as generating the one or more actionable outputs because they are both directed to producing an actionable output for delivery to a user device.)
is in response to determining that at least one of the one or more parameters of the event satisfies a predetermined threshold. ([Mukherjee, [0051]] “For each time interval, the weather intelligence system determines whether the temperature of the time interval exceeds its moving average, combined with programmed floor and ceiling thresholds, in order to classify the time interval as unusually hot or cold (330).”, wherein the examiner interprets “temperature of the time interval” to be the same as at least one of the one or more parameters of the event because they are both directed to a quantitative parameter used to characterize the event. The examiner further interprets “programmed floor and ceiling thresholds” to be the same as a predetermined threshold because they are both directed to predefined threshold values used to evaluate whether the event parameter meets/exceeds a threshold condition that triggers the classification and responsive output generation.)
Regarding claim 13, Mukherjee and Appel teaches The computer-implemented method of claim 1, (see rejection of claim 1).
Mukherjee further teaches wherein matching, by the computer, the one or more prediction results of the plurality of prediction results to the semantic information ([Mukherjee, [0034]] “In order to register, a handshake happens between a given consumer process 250 and the weather intelligence service 200 (i.e., the service provider) so that the service provider uses the right namespaces for the consumers and the right services. In addition, this also helps the service provider to match the right set of zip codes to the right consumer process.”, wherein the examiner interprets “match the right set of zip codes to the right consumer process” to be the same as “matching, by the computer, the one or more prediction results of the plurality of prediction results to the semantic information” because they are both directed to associating predicted-event outputs for a geographic region (zip codes tied to a predicted weather condition) with contextual/semantic information identifying the intended recipient/context (the consumer process configuration/namespace).)
Mukherjee does not teach is by using a machine learning model trained on historical events and historical data in correspondence to the semantic information.
Appel teaches is by using a machine learning model trained on historical events and historical data in correspondence to the semantic information. ([Appel, [0034]] “One example of a cognitive model 315 would be a neural network . The past snapshots of the supply chain , ( i.e. , the historical data ) , are used to train the cognitive model by applying past known causes and tuning the ( also known ) effects . Once the model is trained , the network 315 may facilitate predictions for the supply chain based on the aggregated historical data.”, wherein the examiner interprets “neural network” to be the same as a machine learning model and “past snapshots of the supply chain, (i.e., the historical data)” to be the same as historical data because they are both directed to prior collected data used for training. The examiner further interprets “applying past known causes” and “tuning the (also known) effects” to be the same as historical events and historical data in correspondence to the semantic information because they are both directed to training the model using historical event/cause information paired with associated data/outcomes as supervised training examples.)
Mukherjee, Appel, and the instant application are analogous art because they are all directed to matching prediction results to semantic information using a machine learning models with historical data.
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the invention to modify the computer-implemented method claim 1 disclosed by Mukherjee and Appel to include the matching of semantic information disclosed by Mukherjee and the historical data training model disclosed by Appel. One would be motivated to do so to effectively improve the reliability and accuracy of the matching operation by learning the matching behavior from historical paired examples, as suggested by Appel ([Appel, [0034]] “Once the model is trained, the network 315 may facilitate predictions for the supply chain based on the aggregated historical data.”)
Regarding claim 14, the majority of the claim is analogous to claim 1 so Mukherjee and Appel is able to teach the limitations analogous to claim 1. Below is the unique limitation that is addressed:
Mukherjee teaches A system comprising: one or more computers and one or more storage devices storing instructions that are operable, when executed by the one or more computers, to cause the one or more computers to perform operations for utilizing predictions of future real-world events to make actionable decisions that prepare users for the real-world events, comprising: ([Mukherjee, [0062]-[0063]] “the computer system 600 includes a processor 604, memory 606 (including non-transitory memory), storage device 610 … computer system 600 for implementing the techniques described herein. According to one aspect, those techniques are performed by computer system 600 in response to processor 604 executing one or more sequences of one or more instructions contained in main memory 606.” and [Mukherjee, page 1] “A weather intelligence system retrieves weather forecast data for a number of geographic regions. The weather intelligence system determines, using the weather forecast data for each of the geographic regions, a set of geographic regions predicted to experience a weather anomaly, or unusual weather condition, during a particular time interval. The weather intelligence system can then programmatically enable a trigger to transmit a service-related offer associated with the weather anomaly to user devices”, wherein the examiner interprets “computer system 600” that “includes a processor 604, memory 606” and “storage device 610” that performs techniques “in response to processor 604 executing one or more sequences of one or more instructions” and “A weather intelligence system” that “retrieves weather forecast data” and “determines” “a set of geographic regions predicted to experience a weather anomaly” and “programmatically enable a trigger to transmit a service-related offer” “to user devices” to be the same as “one or more computers and one or more storage devices storing instructions that are operable, when executed by the one or more computers, to cause the one or more computers to perform operations for utilizing predictions of future real-world events to make actionable decisions that prepare users for the real-world events” because both are directed to a computer system architecture with processors, memory, and storage devices that execute stored instructions to use predictive data about future conditions to generate and deliver actionable information to users before those conditions occur.)
Regarding claim 16, Mukherjee and Appel teach The system of claim 14, (see mapping for claim 14).
Mukherjee further teaches:
wherein matching, by the computer, the one or more prediction results of the plurality of prediction results to the semantic information ([Mukherjee, [0035]] “In order to register, a handshake happens between a given consumer process 250 and the weather intelligence service 200 (i.e., the service provider) so that the service provider uses the right namespaces for the consumers and the right services. In addition, this also helps the service provider to match the right set of zip codes to the right consumer process.”, wherein the examiner interprets “match the right set of zip codes to the right consumer process” to be the same as “matching, by the computer, the one or more prediction results of the plurality of prediction results to the semantic information” because they are both directed to associating predicted-event outputs for a geographic region (zip codes tied to a predicted weather condition) with contextual/semantic information identifying the intended recipient/context (the configured consumer process).)
is based on a predetermined mapping relationship between the plurality of prediction results and the semantic information. ([Mukherjee, [0036]] “Prior to the handshake process, a registration scheduler 260 receives configuration data, including the type of weather trigger (e.g., unusually hot, unusually cold, or inclement) that consumer process 250 should be configured to listen to and sign up credentials for the trigger. In one implementation, the registration scheduler 260 executes every 3 hours and posts the sign-up credentials as handshake data to the service provider. A parser 220 on the service provider side parses the handshake data, extracts artifact details identifying the type of weather trigger and the consumer process 250, and saves the details in an artifact table 222 for use by a publisher process 230 after processing weather forecast data.”, AND ([Mukherjee, [0053]-[0054]] “Then, based on the handshake registration data, the service provider determines which zip codes to send to which consumer processes (420). For any given consumer process, the consumer receives a list of zip codes from the service provider that match the unusual weather condition alert, or anomaly, that the consumer process is registered to receive (430)”, wherein the examiner interprets “receives configuration data, including the type of weather trigger … configured to listen to” and “saves the details in an artifact table” to be the same as “predetermined mapping relationship between the plurality of prediction results and the semantic information” because they are both directed to establishing and storing, before later runtime processing, an explicit association between (i) predicted-event outputs (weather-trigger-driven zip code outputs / anomaly alerts) and (ii) semantic/contextual descriptors (the registered consumer process configuration that defines what those outputs should be matched to). The examiner further interprets “based on the handshake registration data” to be the same as based on a predetermined mapping relationship because they are both directed to using pre-registered configuration/registration data to control how predicted-event outputs are routed and paired to the corresponding contextual entity (the registered consumer process).)
Regarding claim 17, Mukherjee and Appel teach, The system of claim 14, (see mapping for claim 14).
Mukherjee further teaches:
wherein generating, by the computer for the user device, one or more actionable outputs by processing the one or more prediction results and the semantic information, comprises: ([Mukherjee, [0014]] “The weather intelligence system determines, through a machine-learning process, whether the weather forecast data indicates conditions that people would generally consider to be unusually hot or cold. The weather intelligence system can then programmatically enable a trigger to transmit a service-related offer associated with the unusual weather condition to user devices located within one of the geographic regions where that weather condition is determined.”, wherein the examiner interprets “programmatically enable a trigger to transmit a service-related offer associated with the unusual weather condition to user devices” to be the same as generating, by the computer for the user device, one or more actionable outputs by processing the one or more prediction results and the semantic information because they are both directed to a computer system using prediction-related conditions together with contextual information to produce an output intended to prompt a user action on a user device.)
providing the one or more prediction results and the semantic information to a trained machine learning model; and ([Mukherjee, [0026]] “Using the weather forecast data, the weather intelligence system 120 runs an AI model to predict whether a geographic region will experience an unusual weather condition, such as being hotter or colder than its regular weather conditions. The AI model learns the short-term weather data patterns over the last few days at a given time and makes predictions based on the learning.”, wherein the examiner interprets “runs an AI model to predict whether a geographic region will experience an unusual weather condition” to be the same as providing the one or more prediction results … to a trained machine learning model because they are both directed to supplying data to a machine learning model that has been trained to perform predictive inference, and wherein the examiner interprets “the AI model learns the short-term weather data patterns over the last few days” to be the same as the trained machine learning model because they are both directed to a machine learning model trained on historical data.)
receiving an output from the trained machine learning model, including the one or more actionable outputs. ([Mukherjee, [0045]] “A rules engine 280 can then index user profiles from a database of user profiles 284 to determine which users registered with the environment are currently located within (or near) any of the geographic regions on the list” and [Mukherjee, [0056]] “Once the process has filtered users that match the business rules, offers are sent to the appropriate users (460).”, wherein the examiner interprets “index user profiles … to determine which users … are currently located within (or near) any of the geographic regions on the list” to be the same as receiving an output from the trained machine learning model because they are both directed to obtaining a result produced by computational processing that identifies entities relevant to predicted event conditions, and wherein the examiner interprets “offers are sent to the appropriate users” to be the same as including the one or more actionable outputs because they are both directed to producing and delivering outputs that enable users to take action in response to a predicted real-world event.)
Regarding claim 18, Mukherjee and Appel teach, The system of claim 14, (see mapping for claim 14).
Mukherjee further teaches wherein the one or more actionable outputs is automatically executed by the user device. ([Mukherjee, page 1] “can then programmatically enable a trigger to transmit a service-related offer associated with the weather anomaly to user devices located within one of the geographic regions where that weather anomaly is determined.” and [Mukherjee, [0056]] “offers can cause a user device to display the offer on a user interface of an application (e.g., through the user of a push notification)”, wherein the examiner interprets “enable a trigger to transmit a service-related offer” to be the same as the one or more actionable outputs because they are both directed to an output generated for a user in response to a predicted real-world event and provided to the user device for action. The examiner further interprets “offer” to be the same as the one or more actionable outputs because they are both directed to an output delivered to a user device for the user to act upon, and wherein the examiner interprets “cause a user device to display” to be the same as is automatically executed by the user device because they are both directed to the user device automatically performing an action using the provided output without requiring the user to manually execute the output.)
Regarding claim 19, Mukherjee and Appel teach, The system of claim 14, (see mapping for claim 14).
Appel further teaches wherein the one or more actionable outputs ([Appel, [0036]] “In some cases, the supply chain vulnerability system 300 may make one or more recommendations for a mitigation strategy. For example, the supply chain vulnerability system 300 may determine whether the vulnerability score for a particular entity is above (or below) a threshold, and if so, recommend ways to mitigate the impact of an external event.” wherein the examiner interprets “recommendation for a mitigation strategy” to be the same as “one or more actionable outputs” because they are both describing actionable outputs generated by the system (recommendations/mitigation strategies).
Mukherjee further teaches is displayed by the user device to a user using a graphic user interface (GUI). Mukherjee teaches is displayed by the user device to a user using a graphic user interface (GUI). ([Mukherjee, [0056]] “In some implementations, offers can cause a user device to display the offer on a user interface of an application (e.g., through the user of a push notification) registered with the platform environment.”, wherein the examiner interprets “user interface” of an application to be the same as “the one or more actionable outputs is displayed by the user device to a user using a graphic user interface (GUI)” because they are both describing actionable outputs (offers/recommendations) being displayed to users on a user device through a user interface.)
Mukherjee, Appel, and the instant application are analogous art because they are all directed to generating actionable outputs in response to predicted or forecast real-world/external events, and presenting those actionable outputs to a user via a user device interface.
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the invention to modify the computer-implemented method claim 1 disclosed by Mukherjee and Appel to include actinal outputs disclosed by Appel and the notification system disclosed by Mukherjee. One would be motivated to do so to effectively present the mitigation recommendations/actionable outputs to users through the user device interface so that users can timely receive and act upon the recommendations, as suggested by Mukherjee ([Mukherjee, [0056]] “through the user of a push notification”).
Regarding claim 20, the majority of the claim is analogous to claim 1 so Mukherjee and Appel is able to teach the limitations analogous to claim 1. Below is the unique limitation that is addressed:
Mukherjee teaches A non-transitory computer-readable medium storing software comprising instructions executable by one or more computers which, upon such execution, cause the one or more computers to perform operations for utilizing predictions of future real-world events to make actionable decisions that prepare users for the real-world events, comprising: [Mukherjee, [0063]] “Examples described herein are related to the use of computer system 600 for implementing the techniques described herein. According to one aspect, those techniques are performed by computer system 600 in response to processor 604 executing one or more sequences of one or more instructions contained in main memory 606. Such instructions may be read into main memory 606 from another machine-readable medium, such as storage device 610. Execution of the sequences of instructions contained in main memory 606 causes processor 604 to perform the process steps described herein.” and [Mukherjee, page 1] “A weather intelligence system retrieves weather forecast data for a number of geographic regions. The weather intelligence system determines, using the weather forecast data for each of the geographic regions, a set of geographic regions predicted to experience a weather anomaly, or unusual weather condition, during a particular time interval. The weather intelligence system can then programmatically enable a trigger to transmit a service-related offer associated with the weather anomaly to user devices”, wherein the examiner interprets “instructions that are executable by one or more processors” that “may be carried on a computer-readable medium” and “computer system 600” executing “one or more sequences of one or more instructions” from “machine-readable medium” to perform techniques and “a weather intelligence system” that “retrieves weather forecast data” and “programmatically enable a trigger to transmit a service-related offer” “to user devices” to be the same as “A non-transitory computer-readable medium storing software comprising instructions executable by one or more computers which, … to make actionable decisions that prepare users for the real-world events” because both are directed to a non-transitory storage medium containing executable instructions that, when executed by computing hardware, cause the system to use predictive data about future conditions to generate and deliver actionable information to users before those predicted events occur.)
Claims 2 and 15 is rejected under 35 U.S.C. 103 as being unpatentable over Mukherjee in view of US 20170169446 A1, by Li et. al. (referred herein as Li).
Regarding claim 2, Mukherjee teaches:
The computer-implemented method of claim 1, wherein each of the plurality of prediction results comprises one or more parameters of: ([Mukherjee, page 1] “A weather intelligence system retrieves weather forecast data for a number of geographic regions. The weather intelligence system determines, using the weather forecast data for each of the geographic regions, a set of geographic regions predicted to experience a weather anomaly, or unusual weather condition, during a particular time interval.”, wherein the examiner interprets “weather forecast data for a number of geographic regions” and “during a particular time interval” to be the same as each of the plurality of prediction results comprises one or more parameters of: because they are both directed to prediction results that include parameter values describing a predicted event and at least where and when it is expected to occur.)
(1) the event; (2) a time window that the event occurs; (3) a geospatial area that the event occurs; ([Mukherjee,[0014]] “The weather intelligence system determines, using the weather forecast data for each of the geographic regions, a set of geographic regions predicted to experience an unusual weather condition, or weather anomaly, during a particular time interval.” wherein the examiner interprets “weather anomaly , or unusual weather condition”, “during a particular time interval”, and “geographic regions” to be the same as the event, “time window”, and geospatial area because they are both directed to the real-world event/condition being predicted for a particular time and place.)
(4) an intensity of the event; and ([Mukherjee, [0051]] “For each time interval, the weather intelligence system determines whether the temperature of the time interval exceeds its moving average, combined with programmed floor and ceiling thresholds, in order to classify the time interval as unusually hot or cold (330).” wherein the examiner interprets forecast “temperature” and “programmed floor and ceiling thresholds” to be the same as an intensity of the event because they are both directed to a quantitative magnitude/level used to characterize how severe or strong the predicted event/condition is.)
Mukherjee does not teach (5) a probability of occurrence of the event corresponding to one or more of (2), (3), and (4).
Li teaches, (5) a probability of occurrence of the event corresponding to one or more of (2), (3), and (4). ([Li, [0027]] “the confidence factor threshold can be 80%, such that a model being considered would be selected to forecast a future demand of the product when the model achieves a confidence factor based on historic forecasting that is 80% or greater.”, [Li, [0013]] “The selected model can be applied in generating a forecasted future demand of the first product at the shopping facility over a fixed future period of time.”, and [Li, [0051]] “The forecasted results may define a forecasted demand over one or more weeks at a shopping facility for the product of interest. For example, in some implementations, the results provide a forecast for 5, 17, 25 or more weeks.”, wherein the examiner interprets “confidence factor threshold can be 80%” to be the same as a probability of occurrence of the event because they are both directed to a numeric likelihood measure associated with a predicted outcome. The examiner further interprets “over a fixed future period of time” to be the same as corresponding to one or more of (2) because they are both directed to tying the prediction (and its likelihood/confidence) to a defined time window, and wherein the examiner interprets “at a shopping facility” to be the same as corresponding to one or more of (3) because they are both directed to tying the prediction (and its likelihood/confidence) to a defined location/geospatial area.)
Mukherjee, Li, and the instant application are analogous art because they are all directed to prediction results for a real-world event.
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the invention to modify the computer-implemented method claim 1 disclosed by Mukherjee to include the confidence factor disclosed by Li. One would be motivated to do so to reliably include a numeric likelihood/confidence parameter with the prediction results so that downstream actions are based on sufficiently reliable prediction results, as suggested by Li ([Li, [0027]] “The confidence threshold can be set to avoid making changes to inventory that are not expected to have significant benefit.”). Claim 15 is analogous to claim 2, aside from claim type, and thus the same rejection can apply to both as above.
Claim 12 is rejected under 35 U.S.C. 103 as being unpatentable over Mukherjee in view of US 8615473 B2, by Spiegel et. al. (referred herein as Spiegel).
Regarding claim 12, Mukherjee teaches, The computer-implemented method of claim 1, (see mapping for claim 1).
Mukherjee does not teach wherein matching, by the computer, the one or more prediction results of the plurality of prediction results to the semantic information is by calculating a correlation between historical events and historical data in correspondence to the semantic information.
Spiegel teaches wherein matching, by the computer, the one or more prediction results of the plurality of prediction results to the semantic information is [Spiegel, col 17, lines 40-45] “In one embodiment, data concerning historical customer shopping behavior, which may be collected by order center 270 or elsewhere within the enterprise, may be stored by data warehouse 220 for analysis by forecasting model 420… In some embodiments, forecasting model 420 may be configured to analyze such data in the aggregate to determine potential demand for items.”, wherein the examiner interprets “determine potential demand for items” to be the same as matching, by the computer, the one or more prediction results of the plurality of prediction results to the semantic information because they are both directed to associating a computed prediction (predicted demand/prediction result) with item/product information (semantic information).)
by calculating a correlation between historical events and historical data in correspondence to the semantic information. ([Spiegel, col 18, lines 15-23] “Demand may be predicted in various ways. For example, if a given customer has purchased a given item, other customers with similar historical shopping patterns… may be more likely to purchase the given item, and in some embodiments forecasting model 420 may be configured to detect such possible correlations.” and [Spiegel, col 18, lines 1] “individual customer purchasing history… [and] data concerning historical customer shopping behavior… Demand may be predicted… if a given customer has purchased a given item…”, wherein the examiner interprets “detect such possible correlations” to be the same as calculating a correlation because they are both directed to computing/identifying statistical association relationships used by a model to link past observations to predicted outcomes. The examiner further interprets “purchasing history” and “historical customer shopping behavior” to be the same as historical data and interprets “has purchased a given item” to be the same as historical events because they are both directed to past occurrences/actions and recorded historical information used as the basis for computing correlations tied to the item (product) being analyzed (semantic information).)
Mukherjee, Spiegel, and the instant application are analogous art because they are all directed to matching prediction results to semantic information.
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the invention to modify the computer-implemented method claim 1 disclosed by Mukherjee to include the correlation technique disclosed by Spiegel. One would be motivated to do so to effectively improve the accuracy and reliability of matching prediction results to the semantic information by using correlation relationships learned from historical events and historical data to associate predicted outcomes with the corresponding product/item context, as suggested by Spiegel ([Spiegel, col 18, lines 15-23] “other customers with similar historical shopping patterns… may be more likely to purchase the given item”).
Conclusion
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/DEVAN KAPOOR/Examiner, Art Unit 2126
/LUIS A SITIRICHE/Primary Examiner, Art Unit 2126