DETAILED ACTION
This action is responsive to claims filed on 30 November 2022.
Claims 1-20 are pending for examination.
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 30 November 2022 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered and attached 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, abstract idea, without significantly more.
Step 1: This part of the eligibility analysis evaluates whether the claim(s) falls within any statutory
category. MPEP 2106.03:
According to the first part of the Alice analysis, in the instant case, the claims were determined
to be directed to one of the four statutory categories: an article of manufacture, a method/process (Claims 1-7), a machine/system/product (Claims 8-20), and a composition of matter. Based on the claims being determined to be within of the four categories (i.e., process, machine, manufacture, or composition of matter), (Step 1), it must be determined if the claims are directed to a judicial exception (i.e., law of nature, natural phenomenon, and abstract idea).
Step 2A Prong One: This part of the eligibility analysis evaluates whether the claim(s) recites a
judicial exception.
Regarding independent claims 1, 8, 15, the claims recite a judicial exception (i.e., an abstract idea enumerated in the 2019 PEG) without significantly more (Step-2A: Prong One). The applicant's claim limitations under broadest reasonable interpretation covers activities classified under mental processes - concepts performed in the human mind (including an observation, evaluation, judgment, opinion) (see MPEP § 2106.04(a)(2), subsection Ill) and the 2019 PEG. As evaluated below:
Claims 1, 8:
“analyzing, by the processor set, the electronic communication data to identify decision making content associated with the decision making point of the process” (mental process of judgement)
If the identified limitation(s) falls within at least one of the groupings of abstract ideas, it is
reasonable to conclude that the claim(s) recites an abstract idea in Step 2A Prong One.
Step 2A Prong Two: This part of the eligibility analysis evaluates whether the claim(s) as a whole integrates the recited judicial exception into a practical application of the exception. As evaluated below:
“obtaining, by the processor set and from one or more digital collaboration platforms, electronic communication data for communications between human participants in the process”
“inputting, by the processor set, the decision making content to a trained machine learning (ML) model”
“thereby generating an output of a decision making factor predicted to impact the decision input at the decision making point”
These recitations are deemed insufficient to transform the judicial exception to a patentable invention because the recitation is directed to instructions for mere data gathering or data output, see MPEP 2106.05(g).
“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”
“automatically updating, by the processor set, the process model based on the predicted decision making factor, thereby generating an updated process model”
These recitations are deemed insufficient to transform the judicial exception to a patentable invention because the recitation is directed to instructions merely indicating a field of use or technological environment in which to apply a judicial exception, see MPEP 2106.05(h).
Accordingly, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea when considered as an ordered combination and as a whole.
Step 2B: This part of the eligibility analysis evaluates whether the claim, as a whole, amounts to
significantly more than the recited exception, i.e., whether any additional element, or combination of
additional elements, adds an inventive concept to the claim. MPEP 2106.05.
First, the additional elements considered as part of the preamble and the additional elements
directed to the use of computer technology are deemed insufficient to transform the judicial exception
to a patentable invention to a patentable invention because they generally link the judicial exception to
the technology environment, see MPEP 2106.05(h).
Second, the additional elements directed to mere application of the abstract idea or mere instructions to implement an abstract idea on a computer are deemed insufficient to transform the judicial exception to a patentable invention to a patentable invention because the limitations generally apply the use of a generic computer and/or process with the judicial exception, see MPEP 2106.05(f).
Third, the claims are directed to instructions merely indicating a field of use or technological environment in which to apply a judicial exception. The courts have found these types of limitations insufficient to transform the judicial exception to a patentable invention, see MPEP 2106.05(g).
Lastly, the claims directed to data gathering activity as noted above, are deemed directed to an insignificant extra-solution activity. The courts have found these types of limitations insufficient to
qualify as "significantly more", see MPEP 2106.05(g).
Furthermore, when considering evidence in view of Berkheimer v. HP, Inc., 881 F.3d 1360, 1368, 125 USPQ2d 1649, 1654 (Fed. Cir. 2018), see USPTO Berkheimer Memorandum (April 2018). Examiner notes Berkheimer: Option 2 - A citation to one or more of the court decisions discussed in MPEP § 2106.05(d}(II} as noting the well understood, routine, conventional nature of the additional element (s) (e.g., limitations directed to mere data gathering):
The courts have recognized the following computer functions as well understood, routine, and conventional functions when they are claimed in a merely generic manner (e.g., at a high level of generality) or as insignificant extra-solution activity, see MPEP 2106.05(d).
The additional limitations, as analyzed, failed to integrate a judicial exception into a practical application at Step 2A and provide an inventive concept in Step 2B, per the analysis above. Thus, considering the additional elements individually and in combination and the claims as a whole, the additional elements do not provide significantly more than the abstract idea. This claim is not patent eligible. Therefore, in examining elements as recited by the limitations individually and as an ordered combination, as a whole, claims 1, 8 do not recite what the courts have identified as "significantly more".
Claim 15:
“analyze the electronic communication data to identify decision making content associated with the decision making point of the process using natural language processing (NLP)” (mental process of judgement)
If the identified limitation(s) falls within at least one of the groupings of abstract ideas, it is
reasonable to conclude that the claim(s) recites an abstract idea in Step 2A Prong One.
Step 2A Prong Two: This part of the eligibility analysis evaluates whether the claim(s) as a whole integrates the recited judicial exception into a practical application of the exception. As evaluated below:
“obtain, from one or more digital collaboration platforms, electronic communication data for communications between human participants in the process”
“input the decision making content to a trained machine learning (ML) model”
“thereby generating an output of a decision making factor predicted to impact the decision input at the decision making point”
“generate and send a report to a user regarding the predicted decision making factor”
These recitations are deemed insufficient to transform the judicial exception to a patentable invention because the recitation is directed to instructions for mere data gathering or data output, see MPEP 2106.05(g).
“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”
These recitations are deemed insufficient to transform the judicial exception to a patentable invention because the recitation is directed to instructions merely indicating a field of use or technological environment in which to apply a judicial exception, see MPEP 2106.05(h).
Accordingly, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea when considered as an ordered combination and as a whole.
Step 2B: This part of the eligibility analysis evaluates whether the claim, as a whole, amounts to
significantly more than the recited exception, i.e., whether any additional element, or combination of
additional elements, adds an inventive concept to the claim. MPEP 2106.05.
First, the additional elements considered as part of the preamble and the additional elements
directed to the use of computer technology are deemed insufficient to transform the judicial exception
to a patentable invention to a patentable invention because they generally link the judicial exception to
the technology environment, see MPEP 2106.05(h).
Second, the additional elements directed to mere application of the abstract idea or mere instructions to implement an abstract idea on a computer are deemed insufficient to transform the judicial exception to a patentable invention to a patentable invention because the limitations generally apply the use of a generic computer and/or process with the judicial exception, see MPEP 2106.05(f).
Third, the claims are directed to instructions merely indicating a field of use or technological environment in which to apply a judicial exception. The courts have found these types of limitations insufficient to transform the judicial exception to a patentable invention, see MPEP 2106.05(g).
Lastly, the claims directed to data gathering activity as noted above, are deemed directed to an insignificant extra-solution activity. The courts have found these types of limitations insufficient to
qualify as "significantly more", see MPEP 2106.05(g).
Furthermore, when considering evidence in view of Berkheimer v. HP, Inc., 881 F.3d 1360, 1368, 125 USPQ2d 1649, 1654 (Fed. Cir. 2018), see USPTO Berkheimer Memorandum (April 2018). Examiner notes Berkheimer: Option 2 - A citation to one or more of the court decisions discussed in MPEP § 2106.05(d}(II} as noting the well understood, routine, conventional nature of the additional element (s) (e.g., limitations directed to mere data gathering):
The courts have recognized the following computer functions as well understood, routine, and conventional functions when they are claimed in a merely generic manner (e.g., at a high level of generality) or as insignificant extra-solution activity, see MPEP 2106.05(d).
The additional limitations, as analyzed, failed to integrate a judicial exception into a practical application at Step 2A and provide an inventive concept in Step 2B, per the analysis above. Thus, considering the additional elements individually and in combination and the claims as a whole, the additional elements do not provide significantly more than the abstract idea. This claim is not patent eligible. Therefore, in examining elements as recited by the limitations individually and as an ordered combination, as a whole, claim 15 does not recite what the courts have identified as "significantly more".
Furthermore, regarding dependent claims 2-7, which depend from claim 1, claims 9-14, which depend from claim 8, and claims 16-20, which depend from claim 15, the claims are directed to a judicial exception (i.e., an abstract idea enumerated in the 2019 PEG, a law of nature, or a natural phenomenon) without significantly more as highlighted below in the claim limitations by evaluating the claim limitations under the Step2A and 2B:
Claims 2, 9:
Incorporates the rejections of claims 1, 8, respectively.
“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” (mental process of judgement)
The recitation is directed to mere instructions to implement an abstract idea on a computer, or
merely uses a computer as a tool to perform an abstract idea and are considered to adding the words "apply it" (or an equivalent) with the judicial exception, See MPEP 2106.05(f).
“wherein the obtaining the electronic communication data comprises obtaining the electronic communication data having timestamps within the time period associated with the process step”
These recitations are deemed insufficient to transform the judicial exception to a patentable invention because the recitation is directed to instructions merely indicating a field of use or technological environment in which to apply a judicial exception, see MPEP 2106.05(h).
Limitations directed to mere instructions to implement an abstract idea on a computer/using computer as a tool or directed to instructions merely indicating a field of use or technological environment in which to apply a judicial exception cannot integrate a judicial exception into a practical application at Step 2A or provide an inventive concept in Step 2B.
Claims 3, 10:
Incorporates the rejections of claims 2, 9, respectively.
“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”
These recitations are deemed insufficient to transform the judicial exception to a patentable invention because the recitation is directed to instructions merely indicating a field of use or technological environment in which to apply a judicial exception, see MPEP 2106.05(h).
Limitations directed to mere instructions indicating a field of use or technological environment in which to apply a judicial exception cannot integrate a judicial exception into a practical application at Step 2A or provide an inventive concept in Step 2B.
Claims 4, 11, 19:
Incorporates the rejections of claims 1, 8, 15, respectively.
“training, by the processor set, the ML model using historic event logs of the process”
These recitations are deemed insufficient to transform the judicial exception to a patentable invention because the recitation is directed to instructions merely indicating a field of use or technological environment in which to apply a judicial exception, see MPEP 2106.05(h).
Limitations directed to mere instructions indicating a field of use or technological environment in which to apply a judicial exception cannot integrate a judicial exception into a practical application at Step 2A or provide an inventive concept in Step 2B.
Claims 5, 12:
Incorporates the rejections of claims 1, 8, respectively.
“analyzing the electronic communication data comprises identifying the decision making content using natural language processing (NLP) of the text-based data” (mental process of judgement)
The recitation is directed to mere instructions to implement an abstract idea on a computer, or
merely uses a computer as a tool to perform an abstract idea and are considered to adding the words "apply it" (or an equivalent) with the judicial exception, See MPEP 2106.05(f).
“electronic communication data is in a form of text- based data”
These recitations are deemed insufficient to transform the judicial exception to a patentable invention because the recitation is directed to instructions merely indicating a field of use or technological environment in which to apply a judicial exception, see MPEP 2106.05(h).
Limitations directed to mere instructions to implement an abstract idea on a computer/using computer as a tool or directed to instructions merely indicating a field of use or technological environment in which to apply a judicial exception cannot integrate a judicial exception into a practical application at Step 2A or provide an inventive concept in Step 2B.
Claims 6, 13:
Incorporates the rejections of claims 1, 8, respectively.
“generating and sending, by the processor set, a report to a user regarding the predicted decision making factor”
These recitations are deemed insufficient to transform the judicial exception to a patentable invention because the recitation is directed to instructions for mere data gathering or data output, see MPEP 2106.05(g).
Limitations directed to instructions for mere data gathering or data output cannot integrate a judicial exception into a practical application at Step 2A or provide an inventive concept in Step 2B.
Claims 7, 14, 20:
Incorporates the rejections of claims 1, 8, 15, respectively.
“ML model utilizes cosine similarity clustering to identify the predicted decision making factor”
These recitations are deemed insufficient to transform the judicial exception to a patentable invention because the recitation is directed to instructions merely indicating a field of use or technological environment in which to apply a judicial exception, see MPEP 2106.05(h).
Limitations directed to mere instructions indicating a field of use or technological environment in which to apply a judicial exception cannot integrate a judicial exception into a practical application at Step 2A or provide an inventive concept in Step 2B.
Claim 16:
Incorporates the rejection of claim 15.
“determine a time period associated with the process step based on stored event logs for the process generated by a process mining computing tool” (mental process of judgement)
The recitation is directed to mere instructions to implement an abstract idea on a computer, or
merely uses a computer as a tool to perform an abstract idea are considered to adding the words "apply it" (or an equivalent) with the judicial exception, See MPEP 2106.05(f).
“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”
These recitations are deemed insufficient to transform the judicial exception to a patentable invention because the recitation is directed to instructions merely indicating a field of use or technological environment in which to apply a judicial exception, see MPEP 2106.05(h).
“wherein the obtaining the electronic communication data comprises obtaining electronic communication data having timestamps within the time period”
These recitations are deemed insufficient to transform the judicial exception to a patentable invention because the recitation is directed to instructions for mere data gathering or data output, see MPEP 2106.05(g).
Limitations directed to mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea or directed to mere instructions indicating a field of use or technological environment in which to apply a judicial exception or directed to instructions for mere data gathering or data output cannot integrate a judicial exception into a practical application at Step 2A or provide an inventive concept in Step 2B.
Claim 17:
Incorporates the rejection of claim 15.
“program instructions are further executable to automatically update the process model to include the predicted decision making factor, thereby generating an updated process model”
These recitations are deemed insufficient to transform the judicial exception to a patentable invention because the recitation is directed to instructions merely indicating a field of use or technological environment in which to apply a judicial exception, see MPEP 2106.05(h).
Limitations directed to mere instructions indicating a field of use or technological environment in which to apply a judicial exception cannot integrate a judicial exception into a practical application at Step 2A or provide an inventive concept in Step 2B.
Claim 18:
Incorporates the rejection of claim 17.
“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”
These recitations are deemed insufficient to transform the judicial exception to a patentable invention because the recitation is directed to instructions merely indicating a field of use or technological environment in which to apply a judicial exception, see MPEP 2106.05(h).
Limitations directed to mere instructions indicating a field of use or technological environment in which to apply a judicial exception cannot integrate a judicial exception into a practical application at Step 2A or provide an inventive concept in Step 2B.
The dependent claims as analyzed above, do not recite limitations that integrated the judicial exception into a practical application. In addition, the claim limitations do not include additional elements that are sufficient to amount to significantly more than the judicial exception (Step-2B). Therefore, the claims do not recite any limitations, when considered individually or as a whole, that recite what have the courts have identified as "significantly more", see MPEP 2106.05; and therefore, as a whole the claims are not patent eligible. As shown above, the dependent claims do not provide any additional elements that when considered individually or as an ordered combination, amount to significantly more than the abstract idea identified. Therefore, as a whole, the dependent claims do not recite what have the courts have identified as "significantly more" than the recited judicial exception. Therefore, claims 2-7, 9-14, and claims 16-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception and does not recite, when claim elements are examined individually and as a whole, elements that the courts have identified as "significantly more" than the recited judicial exception.
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').
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.);
Skogstad fails to teach inputting, by the processor set, the decision making content to a trained machine learning (ML) model, thereby generating an output of a decision making factor predicted to impact the decision input at the decision making point; automatically updating, by the processor set, the process model based on the predicted decision making factor, thereby generating an updated process model.
Durvasula teaches inputting, by the processor set, the decision making content to a trained machine learning (ML) model, thereby generating an output of a decision making factor predicted to impact the decision input at the decision making point; automatically updating, by the processor set, the process model based on the predicted decision making factor, 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 to a trained machine learning (ML) model 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 generating an output of a decision making factor predicted 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 predicted decision making factor, 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. 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.).
Regarding claim 2 and analogous claim 9, Skogstad, as modified by Durvasula, 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 and Durvasula 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, 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 and Durvasula 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, 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 and Durvasula 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, 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 and Durvasula 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, 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 predicted decision making factor ([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 predicted decision making factor 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.
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).);
Skogstad fails to teach input the decision making content to a trained machine learning (ML) model, thereby generating an output of a decision making factor predicted to impact the decision input at the decision making point; and generate and send a report to a user regarding the predicted decision making factor.
Durvasula teaches input the decision making content to a trained machine learning (ML) model, thereby generating an output of a decision making factor predicted to impact the decision input at the decision making point ([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 to a trained machine learning (ML) model 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 thereby generating an output of a decision making factor predicted 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. 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.); and
generate and send a report to a user regarding the predicted decision making factor ([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 predicted decision making factor 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.
Regarding claim 16, Skogstad, as modified by Durvasula, 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 and Durvasula are combinable for the same rationale as set forth above with respect to claim 1.
Regarding claim 17, Skogstad, as modified by Durvasula, teaches The system of claim 15.
Durvasula teaches wherein the program instructions are further executable to automatically update the process model to include the predicted decision making factor, 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 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 predicted decision making factor, 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. 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 combinable for the same rationale as set forth above with respect to claim 1.
Regarding claim 18, Skogstad, as modified by Durvasula, 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 and Durvasula are combinable for the same rationale as set forth above with respect to claim 1.
Claims 7, 14, 20 are rejected under 35 U.S.C. 103 as being unpatentable over Skogstad, in view of Durvasula, and further in view of Conley et al. (U.S. Pre-Grant Publication No. 20200125678, hereinafter 'Conley').
Regarding claim 7 and analogous claims 14, 20, Skogstad, as modified by Durvasula, teaches The method of claim 1, The computer program product of claim 8, and The system of claim 15, respectively.
Skogstad, as modified by Durvasula, fails to teach wherein the ML model utilizes cosine similarity clustering to identify the predicted decision making factor.
Conley teaches wherein the ML model utilizes cosine similarity clustering to identify the predicted decision making factor ([0036] There are a large number of approaches the invention could take to model the engagement decision within engagement classifier 86. Two illustrative methods include long short-term memory and cosine similarity, but other techniques may be used as well. A long short-term memory (LSTM) network is a kind of recurrent neural network. Cells in an LSTM network remember values over arbitrary time intervals and their gates regulate the flow of information into and out of the cell. For purposes of the present invention, an LSTM network can be used to pass messages through to maintain the “state” of a conversation, then outputting a binary (yes/no) decision on whether to respond, given the users knowledge embedding. ML model utilizes cosine similarity clustering Cosine similarity is a measure of similarity between two vectors of an inner product space that measures the cosine of the angle between them. In this case, the vectors measured by the cosine similarity to identify the predicted decision making factor represent the conversation history and the knowledge embedding of the participant. Sufficiently small measurements (relative to the sample) represent a positive decision to engage.).
Skogstad, Durvasula, and Conley 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 Conley to Skogstad before the effective filing date of the claimed invention in order to build knowledge representations for human users and using these knowledge representations to construct ground truths in order to build domain knowledge to determine influence in decision making (cf. Conley, [0017] It would, therefore, be desirable to devise a method of determining how a chatbot should make the initial decision of whether or not to even engage in group conversation. It would be further advantageous if the method could leverage the implicit dataset generated by humans engaging in both directs messages as well as group conversations. The present invention achieves these and other advantages by building knowledge representations for human users and using these knowledge representations to construct ground truths for an engagement classifier. In a human-to-human direct message, the parties are always expected to respond. Therefore, this data can be used as an approximate representation of the domain knowledge and expertise of each user. The present invention then assumes that the decision to engage in a group conversation is based on that domain knowledge.).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Kumar et al. (U.S. Patent No. 10395169) teaches approaches, techniques, and mechanisms for generating, enhancing, applying and updating knowledge neurons for providing decision making information to a wide variety of client applications.
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/MM/Examiner, Art Unit 2129
/MICHAEL J HUNTLEY/Supervisory Patent Examiner, Art Unit 2129