DETAILED ACTION
Response to Amendment
The amendment filed on 10 April 2026 has been entered.
Claims 1-20 are pending.
Claims 1, 6, 8, 12-13, 15, 17 are amended.
Claims 7, 14, 20 are cancelled.
Claims 1-6, 8-13, 15-19 will be pending.
Applicant’s amendments to the Claims have overcome each and every rejection under 35 USC 101 previously set forth in the Non-Final Office Action mailed 14 January 2026.
Response to Arguments
Applicant’s remarks, regarding the rejections of claims under 35 USC 103, have been fully considered.
Applicant notes Claim 1, as amended, recites in part:
"...inputting, by the processor set, the decision making content into a trained machine learning (ML) model that utilizes cosine similarity clustering, to generate one or more predicted decision making factor to impact the decision input at the decision making point”.
Applicant submits the cited references fail to teach or suggest at least the amended claim features of Claim 1. Accordingly, even when considered in combination, Skogstad, Durvasula, and Conley fail to teach or suggest the amended features of Claim 1.
Applicant’s arguments have been considered, but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claim Rejections - 35 USC § 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.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 1-6, 8-13, 15-19 are rejected under 35 U.S.C. 103 as being unpatentable over Skogstad et al. (U.S. Pre-Grant Publication No. 20220327422, hereinafter ‘Skogstad'), in view of Durvasula et al. (U.S. Patent No. 11481553, hereinafter 'Durvasula'), and further in view of Anthdm et al. (NPL: "How I used machine learning to classify emails and turn them into insights (part 2).", hereinafter 'Anthdm').
Regarding claim 1 and analogous claim 8, Skogstad teaches A method, comprising:
accessing, by a processor set, a process model comprising a representation of steps in a lifecycle of a process, including a process step associated with a decision making point of the process, wherein a decision input at the decision making point determines a next step in the process from multiple next-step options; obtaining, by the processor set and from one or more digital collaboration platforms, electronic communication data for communications between human participants in the process; analyzing, by the processor set, the electronic communication data to identify decision making content associated with the decision making point of the process (FIG. 1B illustrates a block diagram of an exemplary accessing, by a processor set, a process model comprising a representation of steps in a lifecycle of a process, including a process step associated with a decision making point of the process system 140 for a Decision Support Platform that includes an Input Module 160, a Log Module 162, a Neural Pathway Training Module 164, an Augmented Intuition Module 166, a Situational Response Module 168, and a User Interface (U.I.) Module 170. The system 140 may communicate with one or more user devices 201, 202 to display output, via a user interface 194 generated by an application engine 192. The Prediction Machine Learning Network 130 may communicate with the system 100, and each module, including an Input Module 160, Log Module 162, Neural Pathway Training Module 164, Augmented Intuition Module 166, Situational Response Module 168, and a user interface (U.I.) module 170.; [0076] The type of received obtaining, by the processor set and from one or more digital collaboration platforms data received by the system may include, but is not limited to, individual keystrokes, mouse movements, finger movement, gyroscope events, calendar data, time data, location data, activity data, heart rate data, blood sugar level data, hydration data, blood pressure data, sleep data, weather data, genome data, and neurotechnological data such as electroencephalography (“EEG”) data, magnetoencephalography (“MEG”) data, and functional near-infrared spectroscopy (“fNIRS”) data. Data may be received by devices including, but not limited to, a personal computer (“PC”), a tablet PC, a Personal Digital Assistant (“PDA”), a cellular telephone, a wearable device, a neurotechnological implant device, and internet of things (“IoT”) devices.; [0013] The system may determine a baseline pattern of activity by evaluating previously received event values and determine whether the baseline pattern of activity has changed. The system may determine that a decision has been made and/or a situation has occurred where the baseline pattern of activity has changed. By way of illustration, but not limitation, the system may determine the baseline pattern of activity has been determined to change based on any one or more of the following changes: change in a movement of a device; change in a course or location of a device; change in a user heart rate; change in a user blood pressure; change in a user EEG activity; change in a user EMG activity; change in computational activity of a computing device; change in location of a computing device; change in a temperature; change in computer device usage by a user; change in a light value; change in accelerometer values; change in audio signals; change in usage of a software application; change in sending and/or reading of electronic communication data for communications between human participants in the process electronic communications, comprising email messages, text messages; change in usage of calendaring applications; change in power consumption of electric devices; a change in check-point node; a change in a decision node; and/or a change in a situation node.; [0090] The Prediction Machine Learning Network may analyze the wherein a decision input at the decision making point determines a next step in the process from multiple next-step options determined current need 1210 and next response 1220 to determine a decision support intervention. to identify decision making content associated with the decision making point of the process Determining decision support intervention may be based on one or more Indicators 630 of a data relationship where the second set of analyzing, by the processor set, the electronic communication data received user data is similar or correlated to historic user data.; [0078] In one embodiment, data received by user devices 201, 202 may be normalized and categorized by the Prediction Machine Learning Network 130 described in detail below. In one embodiment, at least a portion of the user data may be input into a database wherein each event corresponds with a timestamp.);
automatically retraining, by the processor set, the trained machine learning (ML) model based on the electronic communication data, including the decision making content ([0102] As described herein, the system may determine the occurrence of one or more decisions based on input of the received data into the trained machine learning (ML) model trained machine learning network. For example, a decision may comprise one or more actions that are made with an intention (e.g., goal-oriented decisions, conscious decisions, unconscious decisions). A decision may comprise a sequence of determined events to be performed.; [0013] The system may determine a baseline pattern of activity by evaluating previously received event values and determine whether the baseline pattern of activity has changed. The system may determine that a decision has been made and/or a situation has occurred where the baseline pattern of activity has changed. By way of illustration, but not limitation, the system may determine the baseline pattern of activity has been determined to change based on any one or more of the following changes: change in a movement of a device; change in a course or location of a device; change in a user heart rate; change in a user blood pressure; change in a user EEG activity; change in a user EMG activity; change in computational activity of a computing device; change in location of a computing device; change in a temperature; change in computer device usage by a user; change in a light value; change in accelerometer values; change in audio signals; change in usage of a software application; change in sending and/or reading of based on the electronic communication data electronic communications, comprising email messages, text messages; change in usage of calendaring applications; change in power consumption of electric devices; a change in check-point node; a change in a decision node; and/or a change in a situation node.; [0090] The Prediction Machine Learning Network may analyze the determined current need 1210 and next response 1220 to determine a decision support intervention. including the decision making content Determining decision support intervention may be based on one or more Indicators 630 of a data relationship where the second set of received user data is similar or correlated to historic user data.); and
Skogstad fails to teach inputting, by the processor set, the decision making content into a trained machine learning (ML) model that utilizes cosine similarity clustering, to generate one or more predicted decision making factors to impact the decision input at the decision making point; automatically retraining, by the processor set, the trained machine learning (ML) model based on the electronic communication data, including the decision making content; and automatically updating, by the processor set, the process model based on the one or more predicted decision making factors, thereby generating an updated process model.
Durvasula teaches inputting, by the processor set, the decision making content into a trained machine learning (ML) model that utilizes cosine similarity clustering, to generate one or more predicted decision making factors to impact the decision input at the decision making point; automatically retraining, by the processor set, the trained machine learning (ML) model based on the electronic communication data, including the decision making content; and automatically updating, by the processor set, the process model based on the one or more predicted decision making factors, thereby generating an updated process model ([Col. 14, Line 60-Col.15, Line 7] The method 200 may include receiving output in a trained continuous learning machine learning model (block 2016). The inputting, by the processor set, the decision making content into a trained machine learning (ML) model that utilizes cosine similarity clustering trained information collection machine learning model may be trained to collect information from the data sources 2002, 2004, 2006 and 2008. The trained extraction and classification machine learning model may be trained to to generate one or more predicted decision making factors to impact the decision input at the decision making point extract and classify information and strategize it for consumption by further machine learning models. The automatically updating, by the processor set, the process model based on the one or more predicted decision making factors, thereby generating an updated process model trained continuous learning machine learning model may continuously learn based on updates (e.g., new services or information) made available from the data sources at blocks 2002, 2004, 2006 and 2008 and the output at block 5000. The trained continuous learning machine learning model at block 2016 may generate one or more indications of inefficiencies and propose better solutions over time.; [Col. 9, Lines 24-30] Supervised learning and/or unsupervised machine learning may also comprise automatically retraining, by the processor set, the trained machine learning (ML) model retraining, relearning, or otherwise updating models with new, or different, information, which may include information received, ingested, generated, or otherwise used over time. The disclosures herein may use one or both of such supervised or unsupervised machine learning techniques.; [Col. 15, Lines 8-16] The machine learning models at block 2010 may generate one or more outputs that may be stored in a knowledge management repository (block 2018) of the knowledge management environment at block 2000. For example, the information generated at block 2018 may be stored in an electronic database, such as the database 126 of FIG. 1, and used to learn approaches and solutions to different types of problems across domains that are implemented through different technologies.; [Col. 15, Lines 33-46] FIG. 3B depicts a block flow diagram of a trained predictive knowledge machine learning model (block 3200). The predictive knowledge machine learning model may predict future outcomes based on data inputs (block 3210). The predictive knowledge machine learning model may generate predictions/forecasts. The predictive knowledge machine learning model may determine frequency of data updates and volume of data (block 3220). The predictive knowledge machine learning model may classify data (block 3230) and classify different patterns (block 3240). In some aspects, the predictive knowledge machine learning model may include a recommendation system (block 3250). The recommendation system may provide recommendation for data solutions.).
Skogstad and Durvasula are considered to be analogous to the claimed invention because they are in the same field of machine learning. In view of the teachings of Skogstad, it would have been obvious for a person of ordinary skill in the art to apply the teachings of Durvasula to Skogstad before the effective filing date of the claimed invention in order to construct and update one or more living documents, for automated client advising based on the one or more living documents (cf. Durvasula, [Col. 3, Lines 20-31] The aspects described herein relate to, inter alia, techniques for knowledge management-driven decision-making models and, more specifically, to methods and systems for using machine learning to construct and update one or more living documents, and for automated client advising based on the one or more living documents. In some aspects of the present techniques, a living document is continuously improved using knowledge gained while consultants deal with client problems of many types (not limited to any particular application or technology sector). The present techniques may be used to provide knowledge management-as-a-service (KMaaS), in some aspects.).
Anthdm teaches inputting, by the processor set, the decision making content into a trained machine learning (ML) model that utilizes cosine similarity clustering, to generate one or more predicted decision making factors to impact the decision input at the decision making point ([Summary] In the first part, I used an inputting, by the processor set, the decision making content into a trained machine learning (ML) model unsupervised clustering algorithm to let the machine group the emails for me. After inspecting those clusters and finding some interesting insights, I used a more supervised approach to group emails related to a particular keyword.; [Finding related emails] After discovering the most popular terms and the most exciting emails due to clustering algorithms, I was looking for a manner to to impact the decision input at the decision making point further group emails related to to generate one or more predicted decision making factors a specific keyword. For example, finding all the emails that are related to salary or expenses, Enron was involved in a scandal for some reason, right? The first thing that came to mind to achieve this was that utilizes cosine similarity clustering cosine similarity. A common technique used to measure cohesion within clusters in the field of data mining. Cosine similarity is a measure of similarity between two non-zero vectors of an inner product space that measures the cosine of the angle between them. The cosine of 0° is 1, and it is less than 1 for any other angle.);
Skogstad, Durvasula, and Anthdm are considered to be analogous to the claimed invention because they are in the same field of machine learning. In view of the teachings of Skogstad and Durvasula, it would have been obvious for a person of ordinary skill in the art to apply the teachings of Anthdm to Skogstad before the effective filing date of the claimed invention in order to group emails related to a particular keyword (cf. Anthdm, [Summary] In the first part, I used an unsupervised clustering algorithm to let the machine group the emails for me. After inspecting those clusters and finding some interesting insights, I used a more supervised approach to group emails related to a particular keyword.).
Regarding claim 2 and analogous claim 9, Skogstad, as modified by Durvasula and Anthdm, teaches The method of claim 1 and The computer program product of claim 8, respectively.
Skogstad teaches further comprising determining, by the processor set, a time period associated with the process step based on stored event logs for the process generated by a process mining computing tool, wherein the obtaining the electronic communication data comprises obtaining the electronic communication data having timestamps within the time period associated with the process step ([0078] In one embodiment, data received by user devices 201, 202 may be normalized and categorized by the Prediction Machine Learning Network 130 described in detail below. In one embodiment, at least a wherein the obtaining the electronic communication data comprises obtaining the electronic communication data having timestamps within the time period associated with the process step portion of the user data may be input into a database wherein each event corresponds with a timestamp.; [0079] for the process generated by a process mining computing tool Data received by the system may be processed and categorized by the Prediction Machine Learning Network 130 into categories and subcategories based on determining, by the processor set, a time period associated with the process step based on stored event logs data type, value, relationship to other data, date, timestamp, universally unique identifier (“UUID”), and/or network identification (“NID”). In one embodiment, one or more NID's may be assigned at the time a datasource is categorized by either the user 101, 102 or the Prediction Machine Learning Network 130. Further, the NID may trigger functions inside one or more graph databases, Prediction Machine Learning Network 130, or cloud management system. In one embodiment the user may assign a category, subcategory, data type, and unit to the data received. For example, if the data received was genomic data, the user may be prompted to assign the data type as “genomic sequence.” Captured data may then be input into a schemaless database, such a NoSQL database (e.g., a graph database), as will be further described in detail below.).
Skogstad, Durvasula, and Anthdm are combinable for the same rationale as set forth above with respect to claim 1.
Regarding claim 3 and analogous claim 10, Skogstad, as modified by Durvasula and Anthdm, teaches The method of claim 2 and The computer program product of claim 9, respectively.
Skogstad teaches wherein the time period associated with the process step comprises a time period from a start of the process step to a conclusion of the process step, with additional buffer time added based on stored rules ([0088] In one embodiment, a Prediction Machine Learning Network 130 may be trained to determine a wherein the time period associated with the process step comprises a time period from a start of the process step to a conclusion of the process step situation based on a set of received user data logged for a particular time frame or for a particular sequence. The with additional buffer time added active situation segment of a user may be based on stored rules based on identified event sequence, which may be a pre-identified event sequence or an event sequence identified in real-time. An active situation segment may be based on Indicators 630 for user data 530, situational setting 540 and situational conditions 520. Situational conditions 520, may comprise event data related to objects that would represent an environment in a situation 410. The situational setting 540 may be determined based the sequence of events logged.; [0190] For instance, a neural network may use words with relationships to transitive verbs and analyze how identified and/or unidentified checkpoints, segments or event sequences may have second-order (entities relationship through each other through another entity), third-order or nth (infinite) order relationship to other nodes, this may be achieved by using timestamps, indicator relationships, pathways such as checkpoints, NID paths or other identified sequential connections to the events.).
Skogstad, Durvasula, and Anthdm are combinable for the same rationale as set forth above with respect to claim 1.
Regarding claim 4 and analogous claims 11, 19, Skogstad, as modified by Durvasula and Anthdm, teaches The method of claim 1, The computer program product of claim 8, and The system of claim 15, respectively.
Skogstad teaches further comprising training, by the processor set, the ML model using historic event logs of the process ([0265] Referring to FIG. 39, the system may generate a graphical user interface to receive a user-generated entity combination as an input on a situation 410 selection component. The flow chart illustrates the system processing and display of the user interface. The received entity combination may be detected as a new combination by a cloud-based system, which further may prompt the user to tag the situation 410 selection. Based on the entity classes within the combination and/or using historic event logs of the process historical data from the user 101, 102, the system may detect the situation class automatically and further proceed to store the newly tagged situation in the user's personal knowledge base, which further is an input into an database received by a cloud based system in communication with a user device 201, 202. In the instances the auto classified and/or auto-labeled situation is or is not changed by the user, training, by the processor set, the ML model a machine learning model may be trained.).
Skogstad, Durvasula, and Anthdm are combinable for the same rationale as set forth above with respect to claim 1.
Regarding claim 5 and analogous claim 12, Skogstad, as modified by Durvasula and Anthdm, teaches The method of claim 1 and The computer program product of claim 8, respectively.
Skogstad teaches wherein the electronic communication data is in a form of text- based data, and the analyzing the electronic communication data comprises identifying the decision making content using natural language processing (NLP) of the text-based data ([0188] The example further shows how ignoring am incoming call decision class also is related to an IGNORE node with deeper connections to a TRANSITIVE node that relates to a VERB node from a NLP node. The NLP node may be a network of analyzing the electronic communication data comprises identifying the decision making content using natural language processing (NLP) of the text-based data NLP or Natural language processing information initiated in the Prediction Machine Learning Network 130. The example then illustrates how is in a form of text- based data words represented by nodes, relationships or properties may be used to identify decisions. Decisions may for instance be identified through wherein the electronic communication data event data, both directly (e.g., within the value or properties of a single record, node, or entity) or indirectly (e.g., through the value or properties of the relationship between one or more records, nodes, or entities).).
Skogstad, Durvasula, and Anthdm are combinable for the same rationale as set forth above with respect to claim 1.
Regarding claim 6 and analogous claim 13, Skogstad, as modified by Durvasula and Anthdm, teaches The method of claim 1 and The computer program product of claim 8, respectively.
Durvasula teaches further comprising generating and sending, by the processor set, a report to a user regarding the one or more predicted decision making factors ([Col. 15, Lines 8-16] The machine learning models at block 2010 may generate one or more outputs that may be stored in a knowledge management repository (block 2018) of the knowledge management environment at block 2000. For example, the information generated at block 2018 may be stored in an electronic database, such as the database 126 of FIG. 1, and used to learn approaches and solutions to different types of problems across domains that are implemented through different technologies.; [Col. 15, Lines 47-62] FIG. 3C depicts a block flow diagram of a trained diagnostic knowledge machine learning model (block 3300). The diagnostic knowledge machine learning model is a building block of the knowledge artificial intelligence model at block 3000 of FIG. 2A. The diagnostic knowledge machine learning model may process data from various sources to understand and diagnose occurrences (block 3310). The diagnostic knowledge machine learning model may drilling down on the data (block 3320). The diagnostic knowledge machine learning model may include data discovery techniques (block 3330). The diagnostic knowledge machine learning model may include data mining techniques (block 3340). The diagnostic knowledge machine learning model may include data correlation (block 3350). The diagnostic knowledge machine learning model may further comprising generating and sending, by the processor set, a report to a user regarding the one or more predicted decision making factors generate one or more reports (block 3360).).
Skogstad, Durvasula, and Anthdm are combinable for the same rationale as set forth above with respect to claim 1.
Regarding claim 15, Skogstad teaches A system comprising: a processor set, one or more computer readable storage media, and program instructions collectively stored on the one or more computer readable storage media, the program instructions executable to ([0155] The present disclosure may be provided as a computer program product, or software, that may include a machine-readable medium having stored thereon instructions, which may be used to program a computer system (or other electronic devices) to perform a process according to the present disclosure. A machine-readable medium includes any mechanism for storing information in a form readable by a machine (e.g., a computer). For example, a machine-readable (e.g., computer-readable) medium includes a machine (e.g., a computer) readable storage medium such as a read-only memory (“ROM”), random access memory (“RAM”), magnetic disk storage media, optical storage media, flash memory devices, etc.):
access a process model comprising a representation of steps in a lifecycle of a process, including a process step associated with a decision making point of the process, wherein a decision input at the decision making point determines a next step in the process from multiple next-step options; obtain, from one or more digital collaboration platforms, electronic communication data for communications between human participants in the process;
analyze the electronic communication data to identify decision making content associated with the decision making point of the process using natural language processing (NLP) (FIG. 1B illustrates a block diagram of an exemplary access a process model comprising a representation of steps in a lifecycle of a process, including a process step associated with a decision making point of the process system 140 for a Decision Support Platform that includes an Input Module 160, a Log Module 162, a Neural Pathway Training Module 164, an Augmented Intuition Module 166, a Situational Response Module 168, and a User Interface (U.I.) Module 170. The system 140 may communicate with one or more user devices 201, 202 to display output, via a user interface 194 generated by an application engine 192. The Prediction Machine Learning Network 130 may communicate with the system 100, and each module, including an Input Module 160, Log Module 162, Neural Pathway Training Module 164, Augmented Intuition Module 166, Situational Response Module 168, and a user interface (U.I.) module 170.; [0076] The type of received obtain, from one or more digital collaboration platforms data received by the system may include, but is not limited to, individual keystrokes, mouse movements, finger movement, gyroscope events, calendar data, time data, location data, activity data, heart rate data, blood sugar level data, hydration data, blood pressure data, sleep data, weather data, genome data, and neurotechnological data such as electroencephalography (“EEG”) data, magnetoencephalography (“MEG”) data, and functional near-infrared spectroscopy (“fNIRS”) data. Data may be received by devices including, but not limited to, a personal computer (“PC”), a tablet PC, a Personal Digital Assistant (“PDA”), a cellular telephone, a wearable device, a neurotechnological implant device, and internet of things (“IoT”) devices.; [0013] The system may determine a baseline pattern of activity by evaluating previously received event values and determine whether the baseline pattern of activity has changed. The system may determine that a decision has been made and/or a situation has occurred where the baseline pattern of activity has changed. By way of illustration, but not limitation, the system may determine the baseline pattern of activity has been determined to change based on any one or more of the following changes: change in a movement of a device; change in a course or location of a device; change in a user heart rate; change in a user blood pressure; change in a user EEG activity; change in a user EMG activity; change in computational activity of a computing device; change in location of a computing device; change in a temperature; change in computer device usage by a user; change in a light value; change in accelerometer values; change in audio signals; change in usage of a software application; change in sending and/or reading of electronic communication data for communications between human participants in the process electronic communications, comprising email messages, text messages; change in usage of calendaring applications; change in power consumption of electric devices; a change in check-point node; a change in a decision node; and/or a change in a situation node.; [0090] The Prediction Machine Learning Network may analyze the wherein a decision input at the decision making point determines a next step in the process from multiple next-step options determined current need 1210 and next response 1220 to determine a decision support intervention. to identify decision making content associated with the decision making point of the process Determining decision support intervention may be based on one or more Indicators 630 of a data relationship where the second set of analyze the electronic communication data received user data is similar or correlated to historic user data.; [0188] The example further shows how ignoring am incoming call decision class also is related to an IGNORE node with deeper connections to a TRANSITIVE node that relates to a VERB node from a NLP node. The NLP node may be a network of using natural language processing (NLP) NLP or Natural language processing information initiated in the Prediction Machine Learning Network 130. The example then illustrates how words represented by nodes, relationships or properties may be used to identify decisions. Decisions may for instance be identified through event data, both directly (e.g., within the value or properties of a single record, node, or entity) or indirectly (e.g., through the value or properties of the relationship between one or more records, nodes, or entities).);
automatically retrain the trained machine learning (ML) model based on the electronic communication data, including the decision making content ([0102] As described herein, the system may determine the occurrence of one or more decisions based on input of the received data into the trained machine learning (ML) model trained machine learning network. For example, a decision may comprise one or more actions that are made with an intention (e.g., goal-oriented decisions, conscious decisions, unconscious decisions). A decision may comprise a sequence of determined events to be performed.; [0013] The system may determine a baseline pattern of activity by evaluating previously received event values and determine whether the baseline pattern of activity has changed. The system may determine that a decision has been made and/or a situation has occurred where the baseline pattern of activity has changed. By way of illustration, but not limitation, the system may determine the baseline pattern of activity has been determined to change based on any one or more of the following changes: change in a movement of a device; change in a course or location of a device; change in a user heart rate; change in a user blood pressure; change in a user EEG activity; change in a user EMG activity; change in computational activity of a computing device; change in location of a computing device; change in a temperature; change in computer device usage by a user; change in a light value; change in accelerometer values; change in audio signals; change in usage of a software application; change in sending and/or reading of based on the electronic communication data electronic communications, comprising email messages, text messages; change in usage of calendaring applications; change in power consumption of electric devices; a change in check-point node; a change in a decision node; and/or a change in a situation node.; [0090] The Prediction Machine Learning Network may analyze the determined current need 1210 and next response 1220 to determine a decision support intervention. including the decision making content Determining decision support intervention may be based on one or more Indicators 630 of a data relationship where the second set of received user data is similar or correlated to historic user data.); and
Skogstad fails to teach input the decision making content into a trained machine learning (ML) model that utilizes cosine similarity clustering, to generate one or more predicted decision making factors to impact the decision input at the decision making point; automatically retrain the trained machine learning (ML) model based on the electronic communication data, including the decision making content; and generate and send a report to a user regarding the one or more predicted decision making factors.
Durvasula teaches input the decision making content into a trained machine learning (ML) model that utilizes cosine similarity clustering, to generate one or more predicted decision making factors to impact the decision input at the decision making point; automatically retrain the trained machine learning (ML) model based on the electronic communication data, including the decision making content; and ([Col. 14, Line 60-Col.15, Line 7] The method 200 may include receiving output in a trained continuous learning machine learning model (block 2016). The input the decision making content into a trained machine learning (ML) model that utilizes cosine similarity clustering
trained information collection machine learning model may be trained to collect information from the data sources 2002, 2004, 2006 and 2008. The trained extraction and classification machine learning model may be trained to to generate one or more predicted decision making factors to impact the decision input at the decision making point extract and classify information and strategize it for consumption by further machine learning models. The trained continuous learning machine learning model may continuously learn based on updates (e.g., new services or information) made available from the data sources at blocks 2002, 2004, 2006 and 2008 and the output at block 5000. The trained continuous learning machine learning model at block 2016 may generate one or more indications of inefficiencies and propose better solutions over time.; [Col. 9, Lines 24-30] Supervised learning and/or unsupervised machine learning may also comprise automatically retrain the trained machine learning (ML) model retraining, relearning, or otherwise updating models with new, or different, information, which may include information received, ingested, generated, or otherwise used over time. The disclosures herein may use one or both of such supervised or unsupervised machine learning techniques.); and
generate and send a report to a user regarding the one or more predicted decision making factors ([Col. 15, Lines 8-16] The machine learning models at block 2010 may generate one or more outputs that may be stored in a knowledge management repository (block 2018) of the knowledge management environment at block 2000. For example, the information generated at block 2018 may be stored in an electronic database, such as the database 126 of FIG. 1, and used to learn approaches and solutions to different types of problems across domains that are implemented through different technologies.; [Col. 15, Lines 47-62] FIG. 3C depicts a block flow diagram of a trained diagnostic knowledge machine learning model (block 3300). The diagnostic knowledge machine learning model is a building block of the knowledge artificial intelligence model at block 3000 of FIG. 2A. The diagnostic knowledge machine learning model may process data from various sources to understand and diagnose occurrences (block 3310). The diagnostic knowledge machine learning model may drilling down on the data (block 3320). The diagnostic knowledge machine learning model may include data discovery techniques (block 3330). The diagnostic knowledge machine learning model may include data mining techniques (block 3340). The diagnostic knowledge machine learning model may include data correlation (block 3350). The diagnostic knowledge machine learning model may generate and send a report to a user regarding the one or more predicted decision making factors generate one or more reports (block 3360).).
Skogstad and Durvasula are combinable for the same rationale as set forth above with respect to claim 1.
Anthdm teaches input the decision making content into a trained machine learning (ML) model that utilizes cosine similarity clustering, to generate one or more predicted decision making factors to impact the decision input at the decision making point ([Summary] In the first part, I used an input the decision making content into a trained machine learning (ML) model unsupervised clustering algorithm to let the machine group the emails for me. After inspecting those clusters and finding some interesting insights, I used a more supervised approach to group emails related to a particular keyword.; [Finding related emails] After discovering the most popular terms and the most exciting emails due to clustering algorithms, I was looking for a manner to to impact the decision input at the decision making point further group emails related to to generate one or more predicted decision making factors a specific keyword. For example, finding all the emails that are related to salary or expenses, Enron was involved in a scandal for some reason, right? The first thing that came to mind to achieve this was that utilizes cosine similarity clustering cosine similarity. A common technique used to measure cohesion within clusters in the field of data mining. Cosine similarity is a measure of similarity between two non-zero vectors of an inner product space that measures the cosine of the angle between them. The cosine of 0° is 1, and it is less than 1 for any other angle.);
Skogstad, Durvasula, and Anthdm are combinable for the same rationale as set forth above with respect to claim 1.
Regarding claim 16, Skogstad, as modified by Durvasula and Anthdm, teaches The system of claim 15.
Skogstad teaches wherein the program instructions are further executable to determine a time period associated with the process step based on stored event logs for the process generated by a process mining computing tool, wherein the time period associated with the process step comprises a time period from a start of the process step to a conclusion of the process step, with additional buffer time added based on stored rules, and wherein the obtaining the electronic communication data comprises obtaining electronic communication data having timestamps within the time period ([0078] In one embodiment, data received by user devices 201, 202 may be normalized and categorized by the Prediction Machine Learning Network 130 described in detail below. In one embodiment, at least a wherein the wherein the obtaining the electronic communication data comprises obtaining electronic communication data having timestamps within the time period portion of the user data may be input into a database wherein each event corresponds with a timestamp.; [0079] for the process generated by a process mining computing tool received by the system may be processed and categorized by the Prediction Machine Learning Network 130 into categories and subcategories based on to determine a time period associated with the process step based on stored event logs data type, value, relationship to other data, date, timestamp, universally unique identifier (“UUID”), and/or network identification (“NID”). In one embodiment, one or more NID's may be assigned at the time a datasource is categorized by either the user 101, 102 or the Prediction Machine Learning Network 130. Further, the NID may trigger functions inside one or more graph databases, Prediction Machine Learning Network 130, or cloud management system. In one embodiment the user may assign a category, subcategory, data type, and unit to the data received. For example, if the data received was genomic data, the user may be prompted to assign the data type as “genomic sequence.” Captured data may then be input into a schemaless database, such a NoSQL database (e.g., a graph database), as will be further described in detail below.; [0088] In one embodiment, a Prediction Machine Learning Network 130 may be trained to determine a wherein the time period associated with the process step comprises a time period from a start of the process step to a conclusion of the process step situation based on a set of received user data logged for a particular time frame or for a particular sequence. The additional buffer time added active situation segment of a user may be based on stored rules based on identified event sequence, which may be a pre-identified event sequence or an event sequence identified in real-time. An active situation segment may be based on Indicators 630 for user data 530, situational setting 540 and situational conditions 520. Situational conditions 520, may comprise event data related to objects that would represent an environment in a situation 410. The situational setting 540 may be determined based the sequence of events logged.; [0190] For instance, a neural network may use words with relationships to transitive verbs and analyze how identified and/or unidentified checkpoints, segments or event sequences may have second-order (entities relationship through each other through another entity), third-order or nth (infinite) order relationship to other nodes, this may be achieved by using timestamps, indicator relationships, pathways such as checkpoints, NID paths or other identified sequential connections to the events.).
Skogstad, Durvasula, and Anthdm are combinable for the same rationale as set forth above with respect to claim 1.
Regarding claim 17, Skogstad, as modified by Durvasula and Anthdm, teaches The system of claim 15.
Durvasula teaches wherein the program instructions are further executable to automatically update the process model to include the one or more predicted decision making factors, to generate an updated process model ([Col. 14, Line 60-Col.15, Line 7] The method 200 may include receiving output in a trained continuous learning machine learning model (block 2016). The trained information collection machine learning model may be trained to collect information from the data sources 2002, 2004, 2006 and 2008. The trained extraction and classification machine learning model may be trained to extract and classify information and strategize it for consumption by further machine learning models. The automatically update the process model to include the one or more predicted decision making factors, to generate an updated process model trained continuous learning machine learning model may continuously learn based on updates (e.g., new services or information) made available from the data sources at blocks 2002, 2004, 2006 and 2008 and the output at block 5000. The trained continuous learning machine learning model at block 2016 may generate one or more indications of inefficiencies and propose better solutions over time.; [Col. 15, Lines 8-16] The machine learning models at block 2010 may generate one or more outputs that may be stored in a knowledge management repository (block 2018) of the knowledge management environment at block 2000. For example, the information generated at block 2018 may be stored in an electronic database, such as the database 126 of FIG. 1, and used to learn approaches and solutions to different types of problems across domains that are implemented through different technologies.; [Col. 15, Lines 33-46] FIG. 3B depicts a block flow diagram of a trained predictive knowledge machine learning model (block 3200). The predictive knowledge machine learning model may predict future outcomes based on data inputs (block 3210). The predictive knowledge machine learning model may generate predictions/forecasts. The predictive knowledge machine learning model may determine frequency of data updates and volume of data (block 3220). The predictive knowledge machine learning model may classify data (block 3230) and classify different patterns (block 3240). In some aspects, the predictive knowledge machine learning model may include a recommendation system (block 3250). The recommendation system may provide recommendation for data solutions.).
Skogstad, Durvasula, and Anthdm are combinable for the same rationale as set forth above with respect to claim 1.
Regarding claim 18, Skogstad, as modified by Durvasula and Anthdm, teaches The system of claim 17.
Durvasula teaches wherein the program instructions are further executable to repeat the accessing, obtaining, analyzing, inputting, and updating steps to iteratively, automatically, update the updated process model over time ([Col. 14, Line 60-Col.15, Line 7] The wherein the program instructions are further executable to repeat the accessing, obtaining, analyzing, inputting, and updating steps to iteratively, automatically, update the updated process model over time method 200 may include receiving output in a trained continuous learning machine learning model (block 2016). The trained information collection machine learning model may be trained to collect information from the data sources 2002, 2004, 2006 and 2008. The trained extraction and classification machine learning model may be trained to extract and classify information and strategize it for consumption by further machine learning models. The trained continuous learning machine learning model may continuously learn based on updates (e.g., new services or information) made available from the data sources at blocks 2002, 2004, 2006 and 2008 and the output at block 5000. The trained continuous learning machine learning model at block 2016 may generate one or more indications of inefficiencies and propose better solutions over time.).
Skogstad, Durvasula, and Anthdm are combinable for the same rationale as set forth above with respect to claim 1.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Di Ciccio et al. (NPL: “MailOfMine– Analyzing Mail Messages for Mining Artful Collaborative Processes”) teaches MailOfMine approach, to automatically build, on top of a collection of email messages, a set of workflow models that represent the artful processes laying behind the knowledge workers activities.
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any extension fee pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the date of this final action.
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/MM/Examiner, Art Unit 2129
/MICHAEL J HUNTLEY/Supervisory Patent Examiner, Art Unit 2129
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