DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA . Claims [a-z] have been reviewed and are under consideration by this office action.
Information Disclosure Statement
The information disclosure statements (IDS) submitted on 08/15/2025 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
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 a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
Step One - First, pursuant to step 1 in the January 2019 Guidance on 84 Fed. Reg. 53, the claim(s) is/are directed to statutory categories.
Step 2A, Prong One – The claims are found to recite limitations that set forth the abstract idea(s), namely in independent claims recite a series of steps for the abstract idea recited below.
Regarding independent claims, (additional elements bolded)
Regarding Claims 1, A system for managing natural resource production comprising: a processing system in communication with a computing device and one or more databases over a network, the computing device having one or more input systems and one or more output systems, the processing system configured to receive communication data associated with at least one of a product or a service;
a natural language processing system configured to process the communication data and generate embedding data; and
a categorization system having a machine learning model, the categorization system configured to generate a categorization for the communication data using the machine learning model, the embedding data configured to be input into the machine learning model, the machine learning model built from historical communication data.
Regarding Claims 11, A method for managing natural resource production comprising: receiving communication data associated with at least one of a product or a service;
receiving historical communication data from one or more databases;
processing the communication data using a natural language processing system;
generating embedding data for the communication data using the natural language processing system; and
generating a categorization for the communication data based on the embedding data using a machine learning model, the machine learning model built using the historical communication data.
Regarding Claims 19, A method comprising: receiving historical communication data associated with at least one of a product or a service;
processing the historical communication data using a natural language processing system;
determining if the historical communication data requires re-embedding;
generate embedding data for the historical communication data using the natural language processing system;
identifying the historical communication data that requires a manual categorization; and
training a machine learning model using the embedding data and the manual categorization.
As drafted, this is, under its broadest reasonable interpretation, within the Abstract idea groupings of “Mental processes—concepts performed in the human mind” (observation, evaluation, judgment, opinion) as the claims are directed towards receiving communication data, processing communication data, generating categorization data, and determining if data needs manual classification all of which are concepts capable of being performed in the human mind (i.e. via pen and paper).
Further the claims are directed towards the abstract idea grouping of “Certain methods of organizing human activity” — commercial or legal interactions (including agreements in the form of contracts; legal obligations; advertising, marketing or sales activities or behaviors; business relations) and/or managing personal behavior or relationships or interactions between people (including social activities, teaching, and following rules or instructions) as the claims are directed towards receiving communication data, processing communication data, generating categorization data, and determining if data needs manual classification.
Step 2A, Prong Two - This judicial exception is not integrated into a practical application. The independent claims utilize at least an additional elements. The additional elements are performing the steps would be no more than mere instructions to apply the exception using a generic computer component. See MPEP 2106.05(f) and/or amounts to no more than generally linking the use of the judicial exception to a particular technological environment or field of use – see MPEP 2106.05(h).
Step 2B - The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements are just “apply it” on a computer. (See MPEP 2106.05(f) – Mere Instructions to Apply an Exception – “Thus, for example, claims that amount to nothing more than an instruction to apply the abstract idea using a generic computer do not render an abstract idea eligible.” Alice Corp., 134 S. Ct. at 235) and/or amounts to no more than generally linking the use of the judicial exception to a particular technological environment or field of use – see MPEP 2106.05(h).
Regarding Claims 3, 5-10, 12, and 14-18, the claim further narrows the abstract idea or recite additional elements previously addressed in the independent claims (i.e. computing device, natural language processing system, etc.).
Regarding Claims 2, the claim further recite the additional element(s) of notification generation system and computing device to cause the notification to be presented using the one or more output systems. This elements is performing the steps would be no more than mere instructions to apply the exception using a generic computer component. See MPEP 2106.05(f) and/or amounts to no more than generally linking the use of the judicial exception to a particular technological environment or field of use – see MPEP 2106.05(h) in Steps 2A-Prong 2 and 2B.
Regarding Claims 4, the claim further recite the additional element(s) of computing device includes at least one of a smartphone, a tablet, a desktop computer, a laptop computer, or a personal computing device. This elements is performing the steps would be no more than mere instructions to apply the exception using a generic computer component. See MPEP 2106.05(f) and/or amounts to no more than generally linking the use of the judicial exception to a particular technological environment or field of use – see MPEP 2106.05(h) in Steps 2A-Prong 2 and 2B.
Regarding Claims 13, the claim further recite the additional element(s) of causing the notification to be presented using one or more output systems. This elements is performing the steps would be no more than mere instructions to apply the exception using a generic computer component. See MPEP 2106.05(f) and/or amounts to no more than generally linking the use of the judicial exception to a particular technological environment or field of use – see MPEP 2106.05(h) in Steps 2A-Prong 2 and 2B
Regarding Claims 20, the claim further recite the additional element(s) of implementing the machine learning model. This elements is performing the steps would be no more than mere instructions to apply the exception using a generic computer component. See MPEP 2106.05(f) and/or amounts to no more than generally linking the use of the judicial exception to a particular technological environment or field of use – see MPEP 2106.05(h) in Steps 2A-Prong 2 and 2B
Accordingly, the claim fails to recite any improvements to another technology or technical field, improvements to the functioning of the computer itself, use of a particular machine, effecting a transformation or reduction of a particular article to a different state or thing, adding unconventional steps that confine the claim to a particular useful application, and/or meaningful limitations beyond generally linking the use of an abstract idea to a particular environment. See 84 Fed. Reg. 55. Viewed individually or as a whole, these additional claim element(s) do not provide meaningful limitation(s) to transform the abstract idea into a patent eligible application of the abstract idea such that the claim(s) amounts to significantly more than the abstract idea itself.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 1-4, 9-13, 15, and 17-18 is/are rejected under 35 U.S.C. 103 as being unpatentable over Williams et al. (US 20200388144 A1) in view of Chen et al. (US 20230142526 A1).
Regarding Claims 1, Williams teaches: A system for managing… comprising: a processing system in communication with a computing device and one or more databases over a network, the computing device having one or more input systems and one or more output systems, the processing system configured to receive communication data associated with at least one of a product or a service; (Williams, [71, 79]; The CAD server subsystem 110 includes a CAD dispatch server 111 that interfaces with one or more dispatcher subsystems 120 over a first communication network, a mobile responder server 112 that interfaces with one or more personnel subsystems 130 over a second communication network, a database system 113 in which various types of data are maintained by the CAD dispatch server 111 and/or the mobile responder service 112, and optionally a machine learning service 114… The CAD server subsystem 110 maintains various types of information in the database system 113. For example, among other things, the CAD server subsystem 110 maintains information on the various incident types that may occur, information on each incident that does occur, and information on each emergency responder and Williams, [74]; The CAD server subsystem 110 includes a CAD dispatch server 111 that interfaces with one or more dispatcher subsystems 120 over a first communication network, a mobile responder server 112 that interfaces with one or more personnel subsystems 130 over a second communication network, a database system 113 in which various types of data are maintained by the CAD dispatch server 111 and/or the mobile responder service 112, and optionally a machine learning service).
a natural language processing system configured to process the communication data and generate embedding data; and (Williams, [69]; The inventors of the present invention contemplate the use of AI and ML in Computer-Aided Dispatch (CAD) systems for providing real-time, embedded analytics that can be used by a dispatcher or by the CAD system itself (e.g., autonomously) to make dispatch decisions and Williams, [205]; Natural Language Processing refers to the use of communicating to computers using a natural language, in opposite to a structured command language. This is a field of Artificial Intelligence and involves reading as well as writing. For this technology note, we will limit the application of reading a text and making decisions based on it and Williams, [336]; a unit recommendation agent that analyzes data to recommend the most appropriate unit for a given event, natural language and social stream analysis agents to generate context-based notifications or provide inputs to other agents, predictive policing agents that analyze data to identify potential criminal activity and make recommendations for proactive and reactive responses).
a categorization system having a machine learning model, the categorization system configured to generate a categorization for the communication data using the machine learning model, the embedding data configured to be input into the machine learning model, the machine learning model built from historical communication data. (Williams, [84]; each Intelligent Agent configured or trained to produce notifications based on a specific type of analysis of the historic operational data (e.g., one Intelligent Agent might detect patterns across events, another Intelligent Agent might detect similarities between events, etc.). The virtual dispatch assist system can support virtually any type of Intelligent Agent and provides for Intelligent Agents to be added to and removed from the system in virtually any desired combination of Intelligent Agents. Intelligent Agents can be implemented on-site with the CAD dispatch server subsystems 110 and/or can be implemented remotely, e.g., by a cloud-based machine learning service and Williams, [257, 264]; Categorical variables are operational variables that are expressed in terms of a category. For example, the event type is a categorical variable. The monitoring of a categorical variable is done by counting the variable during a period and observing the frequency of this category among all categories…. variables are classified into two types: categorical variables (that has the value defined in categories, like the event type), and continuous variables (that the values range from a minimum to a maximum continuously, like the time to arrive at the event). Each variable will be managed differently. The categorical statistic will be measured by the number of events per unit of time, e.g., measured in hours. It should be noted that continuous variables can be monitored as categorical, e.g., by organizing them into categories and Williams, [316]; An EventClassificationAgent can be included to define event agency, type, priority or urgency based on the event description and other available data. Here, an automatic classifier is trained with historical data (e.g., a pre-classified set of events). Feedback can be used by the EventClassificationAgent to learn from mistakes and improve classification with time).
While Williams teaches a system for managing systems and categorization, Williams does not appear to explicitly teach: natural resource production. However, Williams in view of the analogous art of Chen (i.e. system management) does teach the entirety of the limitation: (Chen, [05]; Implementations described and claimed herein address the foregoing problems by providing systems and methods for modeling production decline for a well by generating a well production profile. For instance, a method of predictive decline modeling for an oil well comprises).
It would have been obvious to try by one of ordinary skill in the art at the time the invention was made, to use natural resource production management of Chen and incorporate it into the management and categorization system of Williams since the system performs categorizing a plurality of data and the system would have performed the same regardless of the type of the industry the system is applied to and one of ordinary skill in the art could have pursued the known potential solutions with reasonable expectation of success (categorizing data). (See MPEP2143(E) – Obvious to try rationale).
Regarding Claims 2 and 13, Williams/Chen teaches: The system of claim 1 further comprising: a notification generation system configured to generate a notification associated with the categorization, the processing system configured to transmit the notification to the computing device to cause the notification to be presented using the one or more output systems. (Williams, [89]; The SmartAdvisor will communicate with its users by issuing Notifications (represented by the Notifier block in FIG. 2). In this context, a Notification is the product or output of an intelligent analysis that communicates relevant information to the user and Williams, [257, 269, 272]; Categorical variables are operational variables that are expressed in terms of a category. For example, the event type is a categorical variable. The monitoring of a categorical variable is done by counting the variable during a period and observing the frequency of this category among all categories… StatisticAgent—main class that stores a list of CategoricalVariables for monitoring and receives events continuously, parses the monitored variables, and passes them to Monitored variables. When the variables identify outliers, they issue notifications that are returned to the Agent… the agent is set up by adding a list of monitored categorial variables, suggested as the table of monitored categorical variables of CAD operation. After this setup, the agent follows continuously the main workflow for every event if receives. FIG. 17 depicts the main workflow for the StatisticAgent in accordance with an exemplary embodiment. First, the external Caller inputs sequentially a series of events and gets as a return a list of Notifications (Method—Trigger). Then, the Agent extracts from the Event the features for each categorical Variable and sends the feature data to the corresponding CategorialVariable that monitors it
Regarding Claims 3, Williams/Chen teaches: The system of claim 2, wherein the notification includes an indication that the communication data has been categorized. (Williams, [18]; the notifications presented to the user may provide a mechanism for the user to provide feedback regarding the notification. The system may allow notifications to be shared between users and/or with non-users. The system may attach a notification to a related event. Intelligent agents may be implemented by the at least one server (e.g., on-site) and/or may be implemented remotely from the at least one server (e.g., by a cloud-based machine learning service). and Williams, [89]; cited above in claim 2 rejection and Williams, [257,269, 272]; cited above in claim 2 rejection).
Regarding Claims 4, Williams/Chen teaches: The system of claim 1, wherein the computing device includes at least one of a smartphone, a tablet, a desktop computer, a laptop computer, or a personal computing device. (Williams, [73]; the optional personnel subsystem 130 includes a monitor device 131 that is wearable by the emergency responder (e.g., a bracelet-type device) and a mobile responder client device 132 (e.g., a smartphone-type device running a specially-configured mobile responder client application). The wearable monitor device 131 is in communication with (e.g., “paired” with) the mobile responder client device 132 over a wireless communication system (e.g., Bluetooth or WiFi). The mobile responder client device 132 is in communication with the CAD server subsystem 110 over the second communication network, which may include a wireless communication system such as cellular telephone/data network).
Regarding Claims 9 and 17, Williams/Chen teaches: The system of claim 1, wherein the categorization includes at least one of an activity category, an element category, or a contract category. (Williams, [264]; In an exemplary embodiment, variables are classified into two types: categorical variables (that has the value defined in categories, like the event type), and continuous variables (that the values range from a minimum to a maximum continuously, like the time to arrive at the event). Each variable will be managed differently. The categorical statistic will be measured by the number of events per unit of time, e.g., measured in hours. It should be noted that continuous variables can be monitored as categorical, e.g., by organizing them into categories). Examiner interprets the event type as an activity type.
Regarding Claims 10 and 18, Williams/Chen teaches: The system of claim 9, wherein the activity category indicates an objective of the communication data, the element category indicates one or more of the product or the service being purchased, and the contract category indicates to one or more of a contract or a structure of the communication data. (Williams, [264]; In an exemplary embodiment, variables are classified into two types: categorical variables (that has the value defined in categories, like the event type), and continuous variables (that the values range from a minimum to a maximum continuously, like the time to arrive at the event). Examiner interprets the time to arrive as an objective and further the Examiner notes that claim 9 only requires one of the categories in which the Examiner has chosen the activity category as the representative category for examination.
Regarding Claims 11, Williams teaches: A method for managing… comprising: receiving communication data associated with at least one of a product or a service; (Williams, [71, 79]; The CAD server subsystem 110 includes a CAD dispatch server 111 that interfaces with one or more dispatcher subsystems 120 over a first communication network, a mobile responder server 112 that interfaces with one or more personnel subsystems 130 over a second communication network, a database system 113 in which various types of data are maintained by the CAD dispatch server 111 and/or the mobile responder service 112, and optionally a machine learning service 114… The CAD server subsystem 110 maintains various types of information in the database system 113. For example, among other things, the CAD server subsystem 110 maintains information on the various incident types that may occur, information on each incident that does occur, and information on each emergency responder and Williams, [74]; The CAD server subsystem 110 includes a CAD dispatch server 111 that interfaces with one or more dispatcher subsystems 120 over a first communication network, a mobile responder server 112 that interfaces with one or more personnel subsystems 130 over a second communication network, a database system 113 in which various types of data are maintained by the CAD dispatch server 111 and/or the mobile responder service 112, and optionally a machine learning service).
receiving historical communication data from one or more databases; (Williams, [80]; Exemplary embodiments of the present invention provide a virtual dispatch assist system (sometimes referred to herein as the “SmartAdvisor”) in which various types of Intelligent Agents (sometimes referred to herein as “SmartAdvisor Agents”) are deployed, e.g., as part of a new CAD system architecture or as add-ons to existing CAD systems, to analyze vast amounts of historic operational data in the database system 113 and provide various types of dispatch assist notifications and recommendations that can be used by a dispatcher or by the CAD system itself (e.g., autonomously) to make dispatch decisions).
processing the communication data using a natural language processing system; generating embedding data for the communication data using the natural language processing system; and (Williams, [69]; The inventors of the present invention contemplate the use of AI and ML in Computer-Aided Dispatch (CAD) systems for providing real-time, embedded analytics that can be used by a dispatcher or by the CAD system itself (e.g., autonomously) to make dispatch decisions and Williams, [205]; Natural Language Processing refers to the use of communicating to computers using a natural language, in opposite to a structured command language. This is a field of Artificial Intelligence and involves reading as well as writing. For this technology note, we will limit the application of reading a text and making decisions based on it and Williams, [336]; a unit recommendation agent that analyzes data to recommend the most appropriate unit for a given event, natural language and social stream analysis agents to generate context-based notifications or provide inputs to other agents, predictive policing agents that analyze data to identify potential criminal activity and make recommendations for proactive and reactive responses).
generating a categorization for the communication data based on the embedding data using a machine learning model, the machine learning model built using the historical communication data. (Williams, [84]; each Intelligent Agent configured or trained to produce notifications based on a specific type of analysis of the historic operational data (e.g., one Intelligent Agent might detect patterns across events, another Intelligent Agent might detect similarities between events, etc.). The virtual dispatch assist system can support virtually any type of Intelligent Agent and provides for Intelligent Agents to be added to and removed from the system in virtually any desired combination of Intelligent Agents. Intelligent Agents can be implemented on-site with the CAD dispatch server subsystems 110 and/or can be implemented remotely, e.g., by a cloud-based machine learning service and Williams, [257, 264]; Categorical variables are operational variables that are expressed in terms of a category. For example, the event type is a categorical variable. The monitoring of a categorical variable is done by counting the variable during a period and observing the frequency of this category among all categories…. variables are classified into two types: categorical variables (that has the value defined in categories, like the event type), and continuous variables (that the values range from a minimum to a maximum continuously, like the time to arrive at the event). Each variable will be managed differently. The categorical statistic will be measured by the number of events per unit of time, e.g., measured in hours. It should be noted that continuous variables can be monitored as categorical, e.g., by organizing them into categories and Williams, [316]; An Event Classification Agent can be included to define event agency, type, priority or urgency based on the event description and other available data. Here, an automatic classifier is trained with historical data (e.g., a pre-classified set of events). Feedback can be used by the EventClassificationAgent to learn from mistakes and improve classification with time).
While Williams teaches a system for managing systems and categorization, Williams does not appear to explicitly teach: natural resource production. However, Williams in view of the analogous art of Chen (i.e. system management) does teach the entirety of the limitation: (Chen, [05]; Implementations described and claimed herein address the foregoing problems by providing systems and methods for modeling production decline for a well by generating a well production profile. For instance, a method of predictive decline modeling for an oil well comprises).
It would have been obvious to try by one of ordinary skill in the art at the time the invention was made, to use natural resource production management of Chen and incorporate it into the management and categorization system of Williams since the system performs categorizing a plurality of data and the system would have performed the same regardless of the type of the industry the system is applied to and one of ordinary skill in the art could have pursued the known potential solutions with reasonable expectation of success (categorizing data). (See MPEP2143(E) – Obvious to try rationale).
Regarding Claims 12, Williams/Chen teaches: The method of claim 11, wherein the communication data is inputted via one or more input systems of a computing device. (Williams, [73]; the optional personnel subsystem 130 includes a monitor device 131 that is wearable by the emergency responder (e.g., a bracelet-type device) and a mobile responder client device 132 (e.g., a smartphone-type device running a specially-configured mobile responder client application). The wearable monitor device 131 is in communication with (e.g., “paired” with) the mobile responder client device 132 over a wireless communication system (e.g., Bluetooth or WiFi). The mobile responder client device 132 is in communication with the CAD server subsystem 110 over the second communication network, which may include a wireless communication system such as cellular telephone/data network).
Regarding Claims 15, Williams/Chen teaches: The method of claim 11, further comprising: generating instructions to cause the categorization to be stored in the one or more databases. (Williams, [79]; The CAD server subsystem 110 maintains various types of information in the database system 113. For example, among other things, the CAD server subsystem 110 maintains information on the various incident types that may occur, information on each incident that does occur, and information on each emergency responder and Williams, [272]; the agent is set up by adding a list of monitored categorial variables, suggested as the table of monitored categorical variables of CAD operation. After this setup, the agent follows continuously the main workflow for every event if receives. FIG. 17 depicts the main workflow for the StatisticAgent in accordance with an exemplary embodiment. First, the external Caller inputs sequentially a series of events and gets as a return a list of Notifications (Method—Trigger). Then, the Agent extracts from the Event the features for each categorical Variable and sends the feature data to the corresponding CategorialVariable that monitors it, requesting from them a list of possible outliers (Method—getOutliersNote). During the training phase, the Monitor stores the feature and calculates the frequency for each variable (Method—Monitor.Add). At the end of the training phase, the Monitor is cloned to a Baseline (Method—Monitor.Close). The Baseline is sorted and the frequency is calculated (Baseline.SorteByCategory, Baseline.setFrequency). During each Checking cycle, the feature is stored in the Monitor
Claims 5-6 and 14 is/are rejected under 35 U.S.C. 103 as being unpatentable over Williams et al. (US 20200388144 A1) in view of Chen et al. (US 20230142526 A1), and Lanfranchi (US 20250284670 A1) (herein after referred to as “Lan”).
Regarding Claims 5 and 14, While Williams/Chen teaches an NLP system to process communication data, neither appears to explicitly teach input columns. However, Williams/Chen in view of the analogous art of Lan does teach the entirety of the limitation: The system of claim 1, wherein the natural language processing system is configured to process the communication data by generating one or more input columns. (Lan, [19]; FIG. 1 is a block diagram that depicts an example computer 100 that uses natural language processing (NLP) and structuring of generated lexical prompt 170 that causes large language model (LLM) 161 to generatively infer hybrid table schema 151 that contains natural language 141-142 that describe input data table 111 and its columns 121-122 and Lan, [31]; For example, a name may be an acronym or a contraction of one or more words. For example, acronym 131 is the name of column 121, and abbreviation 132 is the name of column 122. An acronym such as N. that might mean north or might mean new might be inherently ambiguous if surrounding context is disregarded. An abbreviation such as XmplAbrrvtn might be unconventional).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the disclosed invention to have combined the teachings of Williams/Chen including an NLP system to process communication data with the teachings of Lan including input columns and acronyms in order to select and synthesize phrases a prompt may not otherwise contain and to further incorporate context of surrounding areas (Lan, [31, 33]; For example, a name may be an acronym or a contraction of one or more words. For example, acronym 131 is the name of column 121, and abbreviation 132 is the name of column 122. An acronym such as N. that might mean north or might mean new might be inherently ambiguous if surrounding context is disregarded. An abbreviation such as XmplAbrrvtn might be unconventional… For example, natural language 142 may describe column components 122 and 132, and natural language 141 may describe input data table 111 or column components 121 and 131. LLM 161 accepts lexical prompt 170 as a sole input that causes LLM 161 to generate hybrid table schema 151. For example, natural language expansion 142 may contain the word north, even though lexical prompt 170 might not contain the word north. For example, abbreviation 132 may be a single letter N. In other words, LLM 161 can select words and synthesize phrases and sentences that lexical prompt 170 might not contain.
Regarding Claims 6 and 14, Williams/Chen/Lan teaches: The system of claim 5, wherein the one or more input columns include one or more translated acronyms and one or more combined column values. (Lan, [19]; FIG. 1 is a block diagram that depicts an example computer 100 that uses natural language processing (NLP) and structuring of generated lexical prompt 170 that causes large language model (LLM) 161 to generatively infer hybrid table schema 151 that contains natural language 141-142 that describe input data table 111 and its columns 121-122 and Lan, [31]; For example, a name may be an acronym or a contraction of one or more words. For example, acronym 131 is the name of column 121, and abbreviation 132 is the name of column 122. An acronym such as N. that might mean north or might mean new might be inherently ambiguous if surrounding context is disregarded. An abbreviation such as XmplAbrrvtn might be unconventional). Examiner interprets the contractions as combined column values.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the disclosed invention to have combined the teachings of Williams/Chen including an NLP system to process communication data with the teachings of Lan including input columns and acronyms in order to select and synthesize phrases a prompt may not otherwise contain and to further incorporate context of surrounding areas (Lan, [31, 33]; For example, a name may be an acronym or a contraction of one or more words. For example, acronym 131 is the name of column 121, and abbreviation 132 is the name of column 122. An acronym such as N. that might mean north or might mean new might be inherently ambiguous if surrounding context is disregarded. An abbreviation such as XmplAbrrvtn might be unconventional… For example, natural language 142 may describe column components 122 and 132, and natural language 141 may describe input data table 111 or column components 121 and 131. LLM 161 accepts lexical prompt 170 as a sole input that causes LLM 161 to generate hybrid table schema 151. For example, natural language expansion 142 may contain the word north, even though lexical prompt 170 might not contain the word north. For example, abbreviation 132 may be a single letter N. In other words, LLM 161 can select words and synthesize phrases and sentences that lexical prompt 170 might not contain.
Claims 7-8 and 16 is/are rejected under 35 U.S.C. 103 as being unpatentable over Williams et al. (US 20200388144 A1) in view of Chen et al. (US 20230142526 A1), and Wang et al. (US 20250182115 A1).
Regarding Claims 7 and 16, While Williams/Chen teaches communication data, neither appear to teach payment request. However, Williams/Chen in view of the analogous art of Wang (i.e. communication platforms) does teach: The system of claim 1, wherein the communication data includes a payment request. (Wang, [76]; when the SDK server initiates a payment request to the payer's account management system through the host program server, the SDK server may provide first payment policy information of the SDK owner. The first payment policy information may include the SDK owner's promotion information, payment activity information, etc., which may be used to calculate the amount of the payment indicated by the payment request message. The host program server may provide second payment policy information of the host program owner. The second payment policy information may include promotion information, payment activity information, etc., of the host program owner, which may be used to calculate the amount of the payment indicated by the payment request message).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the disclosed invention to have combined the teachings of Williams/Chen including communication data with the teachings of Wang including payment requests in order to provide a secure method for customers to pay for services (Wang, [06]; in response to a received payment request message, sending a security verification request message to a security control system, where the security verification request message includes security verification information for instructing a security control system to perform a security verification for a payment corresponding to the payment request message according to the security verification information; receiving security verification result information sent by the security control system; when the security verification result information indicates that the security verification is passed, sending a first notification message to an SDK in a terminal device, where the terminal device has the SDK and a host program).
Regarding Claims 8, Williams/Chen/Wang teaches: The system of claim 7, wherein the payment request is a commercial document issued by a seller to a buyer. (Wang, [76]; when the SDK server initiates a payment request to the payer's account management system through the host program server, the SDK server may provide first payment policy information of the SDK owner. The first payment policy information may include the SDK owner's promotion information, payment activity information, etc., which may be used to calculate the amount of the payment indicated by the payment request message. The host program server may provide second payment policy information of the host program owner. The second payment policy information may include promotion information, payment activity information, etc., of the host program owner, which may be used to calculate the amount of the payment indicated by the payment request message).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the disclosed invention to have combined the teachings of Williams/Chen including communication data with the teachings of Wang including payment requests in order to provide a secure method for customers to pay for services (Wang, [06]; in response to a received payment request message, sending a security verification request message to a security control system, where the security verification request message includes security verification information for instructing a security control system to perform a security verification for a payment corresponding to the payment request message according to the security verification information; receiving security verification result information sent by the security control system; when the security verification result information indicates that the security verification is passed, sending a first notification message to an SDK in a terminal device, where the terminal device has the SDK and a host program).
Claims 19-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Williams et al. (US 20200388144 A1) in view of Phung et al. (US 20250191581 A1), and Kvernik et al. (US 20190334784 A1).
Regarding Claims 19, Williams teaches: A method comprising: receiving historical communication data associated with at least one of a product or a service; (Williams, [79]; The CAD server subsystem 110 maintains various types of information in the database system 113. For example, among other things, the CAD server subsystem 110 maintains information on the various incident types that may occur, information on each incident that does occur, and information on each emergency responder and Williams, [74]; The CAD server subsystem 110 includes a CAD dispatch server 111 that interfaces with one or more dispatcher subsystems 120 over a first communication network, a mobile responder server 112 that interfaces with one or more personnel subsystems 130 over a second communication network, a database system 113 in which various types of data are maintained by the CAD dispatch server 111 and/or the mobile responder service 112, and optionally a machine learning service and Williams, [80]; Exemplary embodiments of the present invention provide a virtual dispatch assist system (sometimes referred to herein as the “SmartAdvisor”) in which various types of Intelligent Agents (sometimes referred to herein as “SmartAdvisor Agents”) are deployed, e.g., as part of a new CAD system architecture or as add-ons to existing CAD systems, to analyze vast amounts of historic operational data in the database system 113 and provide various types of dispatch assist notifications and recommendations that can be used by a dispatcher or by the CAD system itself (e.g., autonomously) to make dispatch decisions).).
processing the historical communication data using a natural language processing system; (Williams, [69]; The inventors of the present invention contemplate the use of AI and ML in Computer-Aided Dispatch (CAD) systems for providing real-time, embedded analytics that can be used by a dispatcher or by the CAD system itself (e.g., autonomously) to make dispatch decisions and Williams, [205]; Natural Language Processing refers to the use of communicating to computers using a natural language, in opposite to a structured command language. This is a field of Artificial Intelligence and involves reading as well as writing. For this technology note, we will limit the application of reading a text and making decisions based on it and Williams, [336]; a unit recommendation agent that analyzes data to recommend the most appropriate unit for a given event, natural language and social stream analysis agents to generate context-based notifications or provide inputs to other agents, predictive policing agents that analyze data to identify potential criminal activity and make recommendations for proactive and reactive responses).
generate embedding data for the historical communication data using the natural language processing system; (Williams, [84]; each Intelligent Agent configured or trained to produce notifications based on a specific type of analysis of the historic operational data (e.g., one Intelligent Agent might detect patterns across events, another Intelligent Agent might detect similarities between events, etc.). The virtual dispatch assist system can support virtually any type of Intelligent Agent and provides for Intelligent Agents to be added to and removed from the system in virtually any desired combination of Intelligent Agents. Intelligent Agents can be implemented on-site with the CAD dispatch server subsystems 110 and/or can be implemented remotely, e.g., by a cloud-based machine learning service and Williams, [257, 264]; Categorical variables are operational variables that are expressed in terms of a category. For example, the event type is a categorical variable. The monitoring of a categorical variable is done by counting the variable during a period and observing the frequency of this category among all categories…. variables are classified into two types: categorical variables (that has the value defined in categories, like the event type), and continuous variables (that the values range from a minimum to a maximum continuously, like the time to arrive at the event). Each variable will be managed differently. The categorical statistic will be measured by the number of events per unit of time, e.g., measured in hours. It should be noted that continuous variables can be monitored as categorical, e.g., by organizing them into categories and Williams, [316]; An EventClassificationAgent can be included to define event agency, type, priority or urgency based on the event description and other available data. Here, an automatic classifier is trained with historical data (e.g., a pre-classified set of events). Feedback can be used by the EventClassificationAgent to learn from mistakes and improve classification with time).
While Williams teaches embedding communications data, Williams does not appear to teach re-embedding. However, Williams in view of the analogous art of Phung (i.e. classification and embeddings) does teach: determining if the historical communication data requires re-embedding; (Phung, [13]; The training includes: using triplet loss, correcting first embeddings of the manual transcription and second embeddings of the ASR transcription to reduce a difference; based on the corrected first embeddings and second embeddings, determining a first predicted intent probability distribution of the manual transcription and a second predicted intent probability distribution of the ASR transcription).
While Williams teaches training based on categorization (Williams, [260, 272, 316]), Williams does not appear to teach training based on embedding data nor the use of manual categorization. However, Williams in view of the analogous art of (i.e. ) does teach: training a machine learning model using the embedding data… (Phung, [13]; The training includes: using triplet loss, correcting first embeddings of the manual transcription and second embeddings of the ASR transcription to reduce a difference; based on the corrected first embeddings and second embeddings, determining a first predicted intent probability distribution of the manual transcription and a second predicted intent probability distribution of the ASR transcription).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the disclosed invention to have combined the teachings of Williams including training based on categorization with the teachings of Phung including re-embedding and training an ML model using embeddings in order to ensure embedding are correct and minimize distance between embeddings (Phung, [12]; The operations include receiving manual transcription at a ground truth text encoder; receiving automatic speech recognition (ASR) transcription at an ASR encoder; generating first embeddings of the manual transcription at the ground truth text encoder and second embeddings of the ASR transcription at the ASR encoder; training the ASR encoder with the ground truth text encoder by applying a triplet loss function, where the applying the triplet loss function further comprises correcting the first embeddings and the second embeddings to modify a distance between first embeddings of the manual transcription and second embeddings of the ASR transcription based on a calculated value of the triplet loss function).
While Williams teaches training machine learning models and categorization. Williams does not appear to teach manual classification. However, Williams in view of the analogous art of Kvernik (i.e. categorization) does teach: and the manual categorization. (Kvernik, [99]; Anomaly classification is illustrated in FIG. 7, and may involve either or both of manual and/or automatic classification of anomalies. For example, manual intervention may be used to provide labelled training data to train a machine learning model to operate as an automatic anomaly classifier 700. Once the automatic anomaly classifier has been trained, then further manual intervention may only be required when a new anomaly type is identified which does not conform to the labelled training data).
identifying the historical communication data that requires a manual categorization; and (Kvernik, [99]; Anomaly classification is illustrated in FIG. 7, and may involve either or both of manual and/or automatic classification of anomalies. For example, manual intervention may be used to provide labelled training data to train a machine learning model to operate as an automatic anomaly classifier 700. Once the automatic anomaly classifier has been trained, then further manual intervention may only be required when a new anomaly type is identified which does not conform to the labelled training data).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the disclosed invention to have combined the teachings of Williams including training machine learning models and categorization with the teachings of Kvernik including manual classification in order to train a model to detect anomalies and reduce future anomalous data. (Kvernik, [99]; may involve either or both of manual and/or automatic classification of anomalies. For example, manual intervention may be used to provide labelled training data to train a machine learning model to operate as an automatic anomaly classifier 700. Once the automatic anomaly classifier has been trained, then further manual intervention may only be required when a new anomaly type is identified which does not conform to the labelled training data. In further examples of the present disclosure, a trained automatic anomaly classifier may be used to identify and remove anomalous data before that data is input to the anomaly detection method 400 during a learning phase. In this manner, the risk of training the method 400 on anomalous data may be reduced).
Regarding Claims 20, Williams teaches: The method of claim 19, further comprising:
implementing the machine learning model into production. (Williams, [71]; The machine learning service 114 can be implemented on-site with the CAD dispatch server subsystems 110 or can be implemented remotely, e.g., cloud-based).
Conclusion
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/JEREMY L GUNN/ Examiner, Art Unit 3624
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