DETAILED ACTION
This action is in response to Applicant’s Amendment ("Response”) received on March 30, 2026 in response to the Office Action dated December 30, 2025. This action is made Final.
Claims 1-20 are pending in the case.
Claims 1, 9, and 17 are independent claims.
Claims 1-4, 8-12, and 16-18 are rejected.
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 .
Applicant’s Response
In Applicant’s Response, Applicant amended claims 1, 3, 4, 7-12, and 15-18, and submitted arguments against the prior art in the Office Action dated December 30, 2025.
Based on the amendments to claims 10 and 17, the Examiner withdraws the corresponding objections.
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 1-4, 8-12, and 16-18 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
Step 1: Independent claims 1, 9, and 17 are directed towards a method, program product comprising one or more computer readable storage media (see Spec 0036), and system comprising a processor set and storage media, respectively. These claims, as well as their dependent claims, are directed towards one of the four statutory categories (process, machine (i.e. apparatus), manufacture, or composition of matter.
With respect to claim 1:
2A Prong 1:
Claim 1 recites the following judicial exceptions:
receiving … a natural language problem statement corresponding to application modernization needs of a user, the natural language problem statement including at least one technical entity, business constraint, and disposition information (mental process –can be performed in the human mind, or by a human using a pen and paper (e.g. a person may receive a type of problem statement that includes technical entities, business related constraints, and disposition information.).
providing … extracted structured information by extracting information from the natural problem statement (mental process –can be performed in the human mind, or by a human using a pen and paper (e.g. a person may receive a type of problem statement that includes technical entities, business related constraints, and disposition information.).
generating … standardized technical entities, standardized business entities, and standardized dispositions by inputting the extracted structured information (mental process –can be performed in the human mind, or by a human using a pen and paper (e.g. a person may generate standard entities and dispositions using extracted structured information as input.).
generating … at least one recommended disposition of at least one technical entity to a second technical entity based at least on a business constraint corresponding to the natural language problem statement using the standardized technical entities, standardized business entities, and standardized dispositions (see mental process –can be performed in the human mind, or by a human using a pen and paper (e.g. a person may generate suggestions or recommendations for one entity to a second entity using constraints in the problem statement and the standardized entities and dispositions.).
2A Prong 2: The additional elements recited in the claim do not integrate the judicial exception into a practical application.
Additional elements:
… by a processor set …; … by the processor set …; by the processor set; …by the processor set… (mere instructions to apply the exception or implement the exception on a computer (e.g. – using a computer including processor and memory to models and/or algorithms; see MPEP §2106.05(f).). The additional elements do not effectively integrate the abstract idea into a practical application. 2B: revisiting the additional elements, the additional elements do not amount to significantly more than the judicial exception – recited high level of generality and corresponds to storing and retrieving information in memory and performing calculations.).
using a neural word segmentation method (generally linking the use of a judicial exception to a particular technological environment or field of use (e.g. using machine learning model such as neural or nodal model; see MPEP §2106.05(h).). The additional elements do not effectively integrate the abstract idea into a practical application. 2B: revisiting the additional elements, the additional elements do not amount to significantly more than the judicial exception – recited high level of generality and corresponds to storing and retrieving information and performing calculations.).
to at least one machine learning model (generally linking the use of a judicial exception to a particular technological environment or field of use (e.g. using machine learning model for processing and generation; see MPEP §2106.05(h).). The additional elements do not effectively integrate the abstract idea into a practical application. 2B: revisiting the additional elements, the additional elements do not amount to significantly more than the judicial exception – recited high level of generality and corresponds to storing and retrieving information and performing calculations.).
With respect to claim 2:
2A Prong 1:
Claim 2 recites the following judicial exceptions:
wherein the at least one recommended disposition corresponds to one or more possible target environments along with explanation generated based on the business constraints and mentions of technical entities present in the natural language problem statment (mental process –can be performed in the human mind, or by a human using a pen and paper (e.g. a person may receive a type of problem statement that includes technical entities, business related constraints, and disposition information and produce a suggestion with explanation regarding the constraints and mentions of entities.).
With respect to claim 3:
2A Prong 2: The additional elements recited in the claim do not integrate the judicial exception into a practical application.
Additional elements:
retrieving structured knowledge corresponding to the natural language problem statement from at least one knowledge database by querying the at least one knowledge database with the extracted structured information wherein at least one of the at least one knowledge database includes use case knowledge bootstrapped from use case studies that contain disposition recommendations from one technical entity to another given a business constraint, or a publicly available database having crowdsourced information corresponding to technical entities, business constraints and disposition solutions (mere instructions to apply the exception or implement the exception on a computer (e.g. – using a particular query to retrieve data from a particular database; see MPEP §2106.05(f).). The additional elements do not effectively integrate the abstract idea into a practical application. 2B: revisiting the additional elements, the additional elements do not amount to significantly more than the judicial exception – recited high level of generality and corresponds to storing and retrieving information in memory.).
With respect to claim 4:
2A Prong 1:
Claim 4 recites the following judicial exceptions:
wherein the natural language problem statement includes user disposition preferences including at least one of a preferred modernization target environment and a preferred transformation path, and further comprising: generating the at least one recommendation disposition corresponding to the natural language problem statement to consider the at least one of a preferred modernization target environment and a preferred transformation path, and the user disposition preferences, the generating including validating the user disposition preferences against recommended dispositions provided by the disposition recommender processes; and modifying the at least one recommended disposition to accommodate the user disposition preferences (mental process –can be performed in the human mind, or by a human using a pen and paper (e.g. a person may generate recommendations corresponding to the problem statement while considering preferences, validate the preferences against the recommendations, and modify the recommendations accordingly.).
With respect to claim 8:
2A Prong 2: The additional elements recited in the claim do not integrate the judicial exception into a practical application.
Additional elements:
training the at least one machine learning model with the structured information, wherein the at least one machine learning model are pre-trained using a publicly available large-scale dataset (generally linking the use of a judicial exception to a particular technological environment or field of use (e.g. training and using a machine learning model or algorithm; see MPEP §2106.05(h).). The additional elements do not effectively integrate the abstract idea into a practical application. 2B: revisiting the additional elements, the additional elements do not amount to significantly more than the judicial exception – recited high level of generality and corresponds to storing and retrieving information and performing calculations.).
With respect to claim 9:
2A Prong 1:
Claim 9 recites the following judicial exceptions:
receive a natural language problem statement corresponding to application modernization needs of a user, the natural language problem statement including at least one technical entity, business constraint and disposition information (mental process –can be performed in the human mind, or by a human using a pen and paper (e.g. a person may receive a type of problem statement that includes technical entities, business related constraints, and disposition information.).
provide extracted structured information by extracting information from the natural problem statement (mental process –can be performed in the human mind, or by a human using a pen and paper (e.g. a person may receive a type of problem statement that includes technical entities, business related constraints, and disposition information.).
generate standardized technical entities, standardized business entities, and standardized dispositions by inputting the extracted structured information (mental process –can be performed in the human mind, or by a human using a pen and paper (e.g. a person may generate standard entities and dispositions using extracted structured information as input.).
generate at least one recommended disposition of at least one technical entity to a second technical entity based at least on a business constraint corresponding to the natural language problem statement using the standardized technical entities, standardized business entities, and standardized dispositions the at least one recommendation disposition corresponding to one or more possible target environments along with explanation generated based on the business constraints and mentions of technical entities present in the natural language problem statement (see mental process –can be performed in the human mind, or by a human using a pen and paper (e.g. a person may generate suggestions or recommendations for one entity to a second entity using constraints in the problem statement and the standardized entities and dispositions; a person may receive a type of problem statement that includes technical entities, business related constraints, and disposition information and produce a suggestion with explanation regarding the constraints and mentions of entities.).
2A Prong 2: The additional elements recited in the claim do not integrate the judicial exception into a practical application.
Additional elements:
a computer program product comprising one or more computer readable storage media having program instructions collectively stored on the one or more computer readable storage media, the program instructions executable to (mere instructions to apply the exception or implement the exception on a computer (e.g. – using a computer including processor and memory to models and/or algorithms; see MPEP §2106.05(f).). The additional elements do not effectively integrate the abstract idea into a practical application. 2B: revisiting the additional elements, the additional elements do not amount to significantly more than the judicial exception – recited high level of generality and corresponds to storing and retrieving information in memory and performing calculations.).
using a neural word segmentation method (generally linking the use of a judicial exception to a particular technological environment or field of use (e.g. using machine learning model such as neural or nodal model; see MPEP §2106.05(h).). The additional elements do not effectively integrate the abstract idea into a practical application. 2B: revisiting the additional elements, the additional elements do not amount to significantly more than the judicial exception – recited high level of generality and corresponds to storing and retrieving information and performing calculations.).
to at least one machine learning model (generally linking the use of a judicial exception to a particular technological environment or field of use (e.g. using machine learning model for processing and generation; see MPEP §2106.05(h).). The additional elements do not effectively integrate the abstract idea into a practical application. 2B: revisiting the additional elements, the additional elements do not amount to significantly more than the judicial exception – recited high level of generality and corresponds to storing and retrieving information and performing calculations.).
With respect to claim 10:
2A Prong 2: The additional elements recited in the claim do not integrate the judicial exception into a practical application.
Additional elements:
retrieve structured knowledge corresponding to the natural language problem statement from at least one knowledge database by querying the at least one knowledge database with the extracted structured information (mere instructions to apply the exception or implement the exception on a computer (e.g. – using a particular query to retrieve data from a particular database; see MPEP §2106.05(f).). The additional elements do not effectively integrate the abstract idea into a practical application. 2B: revisiting the additional elements, the additional elements do not amount to significantly more than the judicial exception – recited high level of generality and corresponds to storing and retrieving information in memory.).
With respect to claim 11:
2A Prong 2: The additional elements recited in the claim do not integrate the judicial exception into a practical application.
Additional elements:
wherein at least one of the at least one knowledge database includes use case knowledge bootstrapped from use case studies that contain disposition recommendations from one technical entity to another given a business constraint (mere instructions to apply the exception or implement the exception on a computer (e.g. – using a particular query to retrieve data from a particular database; see MPEP §2106.05(f).). The additional elements do not effectively integrate the abstract idea into a practical application. 2B: revisiting the additional elements, the additional elements do not amount to significantly more than the judicial exception – recited high level of generality and corresponds to storing and retrieving information in memory.).
With respect to claim 12:
2A Prong 1:
Claim 12 recites the following judicial exceptions:
wherein the natural language problem statement includes user disposition preferences including at least one of a preferred modernization target environment and a preferred transformation path, and further comprising program instruction executable to: generate the at least one recommendation disposition corresponding to the natural language problem statement to consider the at least one of a preferred modernization target environment and a preferred transformation path, and the user disposition preferences, the generating including validating the user disposition preferences against recommended dispositions provided by the disposition recommender processes; and modifying the at least one recommended disposition to accommodate the user disposition preferences (mental process –can be performed in the human mind, or by a human using a pen and paper (e.g. a person may generate recommendations corresponding to the problem statement while considering preferences, validate the preferences against the recommendations, and modify the recommendations accordingly.).
With respect to claim 16:
2A Prong 2: The additional elements recited in the claim do not integrate the judicial exception into a practical application.
Additional elements:
train the at least one machine learning model with the structured information, wherein the at least one machine learning model are pre-trained using a publicly available large-scale dataset (generally linking the use of a judicial exception to a particular technological environment or field of use (e.g. training and using a machine learning model or algorithm; see MPEP §2106.05(h).). The additional elements do not effectively integrate the abstract idea into a practical application. 2B: revisiting the additional elements, the additional elements do not amount to significantly more than the judicial exception – recited high level of generality and corresponds to storing and retrieving information and performing calculations.).
With respect to claim 17:
2A Prong 1:
Claim 17 recites the following judicial exceptions:
receive a natural language problem statement corresponding to application modernization needs of a user, the natural language problem statement including at least one technical entity, business constraint (mental process –can be performed in the human mind, or by a human using a pen and paper (e.g. a person may receive a type of problem statement that includes technical entities, business related constraints, and disposition information.).
provide extracted structured information by extracting information from the natural problem statement (mental process –can be performed in the human mind, or by a human using a pen and paper (e.g. a person may receive a type of problem statement that includes technical entities, business related constraints, and disposition information.).
generate standardized technical entities, standardized business entities, by inputting the extracted structured information (mental process –can be performed in the human mind, or by a human using a pen and paper (e.g. a person may generate standard entities and dispositions using extracted structured information as input.).
generate at least one recommended disposition of at least one technical entity to a second technical entity based at least on the structured knowledge corresponding to the natural language problem statement from at least one knowledge database, a business constraint corresponding to the natural language problem statement using the standardized technical entities and standardized business entities (see mental process –can be performed in the human mind, or by a human using a pen and paper (e.g. a person may generate suggestions or recommendations for one entity to a second entity using structured knowledge and constraints corresponding to the problem statement and the standardized entities and dispositions; a person may receive a type of problem statement that includes technical entities, business related constraints, and disposition information and produce a suggestion regarding the constraints and mentions of entities.).
2A Prong 2: The additional elements recited in the claim do not integrate the judicial exception into a practical application.
Additional elements:
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 (mere instructions to apply the exception or implement the exception on a computer (e.g. – using a computer including processor and memory to models and/or algorithms; see MPEP §2106.05(f).). The additional elements do not effectively integrate the abstract idea into a practical application. 2B: revisiting the additional elements, the additional elements do not amount to significantly more than the judicial exception – recited high level of generality and corresponds to storing and retrieving information in memory and performing calculations.).
using a neural word segmentation method (generally linking the use of a judicial exception to a particular technological environment or field of use (e.g. using machine learning model such as neural or nodal model; see MPEP §2106.05(h).). The additional elements do not effectively integrate the abstract idea into a practical application. 2B: revisiting the additional elements, the additional elements do not amount to significantly more than the judicial exception – recited high level of generality and corresponds to storing and retrieving information and performing calculations.).
to at least one machine learning model (generally linking the use of a judicial exception to a particular technological environment or field of use (e.g. using machine learning model for processing and generation; see MPEP §2106.05(h).). The additional elements do not effectively integrate the abstract idea into a practical application. 2B: revisiting the additional elements, the additional elements do not amount to significantly more than the judicial exception – recited high level of generality and corresponds to storing and retrieving information and performing calculations.).
retrieve structured knowledge corresponding to the natural language problem statement from at least one knowledge database by querying the at least one knowledge database with the extracted structured information (mere instructions to apply the exception or implement the exception on a computer (e.g. – using a particular query to retrieve data from a particular database; see MPEP §2106.05(f).). The additional elements do not effectively integrate the abstract idea into a practical application. 2B: revisiting the additional elements, the additional elements do not amount to significantly more than the judicial exception – recited high level of generality and corresponds to storing and retrieving information in memory.).
With respect to claim 18:
2A Prong 1:
Claim 18 recites the following judicial exceptions:
wherein the at least one recommended disposition corresponds to one or more possible target environments along with explanation generated based on the business constraints and mentions of technical entities present in the natural language problem statement, and further comprising program instructions executable to: generate the at least one recommendation disposition corresponding to the natural language problem statement to consider the at least one of a preferred modernized target environment and a preferred transformation path, and user disposition preferences, the generating including validating the user disposition preferences against recommended dispositions provided by the disposition recommender process, wherein the natural language problem statement includes user disposition preferences including at least one of a preferred modernized target environment and a preferred transformation path; and modify the at least one recommended disposition to accommodate the user disposition preferences (mental process –can be performed in the human mind, or by a human using a pen and paper (e.g. a person may generate recommendations corresponding to the problem statement while considering preferences, validate the preferences against the recommendations, and modify the recommendations accordingly.).
2B continued: After considering all claim elements individually and as an ordered combination, it is determined that the claims do not include any additional elements that are sufficient to amount to significantly more than the 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.
Claim(s) 1-4, 9, 10, 12, 17, and 18 is/are rejected under 35 U.S.C. 103 as being unpatentable over Marzorati et al., US Publication 2023/0367644 (“Marzorati”), and further in view of Kim et al., US Publication 2020/0311207 (“Kim”).
Claim 1:
Marzorati teaches or suggests a method, comprising:
recieving, by a processor set, a natural language problem statement corresponding to application modernization needs of a user, the natural language problem statement including at least one technical entity, business constraint, and disposition information (see para. 0016 - recommendations are based on text (alphanumeric characters) received from the user describing item(s) (e.g., an application) for possible migration to a different computing environment, for example, with cloud feature(s), the text including structured and unstructured text; para. 0025 - client or customer ("user") wants to find a multi-provider solution with cloud feature(s) for their Business Intelligence (BI) application; para. 0026 - features or controls that drive the solution including cost, RAM, storage space, coverage of service, performance, etc.; para. 0034 - are based on a description (e.g., alphanumeric characters) of an item(s) the user is considering to migrate to, or otherwise launch on, cloud provider; network(s). The item(s) may include the following non-limiting examples: an application; an agreement (e.g., a cloud service provider agreement); a service; and a business process; para. 0036 - set of common features that is considered for mapping include those in Table A; para. 0037 - Special features - Modernization and feature upgrades expected - Modernization of cloud service.);
providing, by the processor set, extracted structured information by extracting information from the natural language problem statement (see para. 0016 - recommendations are based on text (alphanumeric characters) received from the user describing item(s) (e.g., an application) for possible migration to a different computing environment, for example, with cloud feature(s), the text including structured and unstructured text; para. 0016 - Using text mining, feature(s) of the item(s) are extracted. Text portions are also extracted to identify entity(ies); para. 0017 - Intents and entities are the building blocks of natural language understanding (previously referred to as "Natural Language Processing") or NLP; para. 0018 - preparing a list of label(s), domain data and feature(s) of the item, the list having multiple listings. For each listing in the list, a predetermined amount of the unstructured text that includes a given listing, i.e., a label, domain data or an item feature, is extracted. natural language processing can be applied. Given this context, an entity can be predicted for each unstructured text portion; para. 0028 - Any structured text can be conventionally processed; natural language processing can be applied. Given this context, an entity can be predicted for each unstructured text portion; para. 0074 - structured text can be processed 712 conventionally, for example, simply applying predetermined labels based on the structure. performed by identifying 720 tags/keywords for each item feature and labelling the item features with the tags/keywords. Text is extracted and mapped to relevant tags, to identify completeness of data using these tags; para. 0088 - employ data structuring processes, e.g., processing for transforming unstructured data into a form optimized for computerized processing.);
generating, by the processing set, standardized technical entities, standardized business entities, and standardized dispositions by inputting the extracted structured information to at least one machine learning model (see para. 0029 - available cloud features and providers may be predetermined. Successful mappings of an entity or an item feature with a cloud feature may be stored, for example, in a mappings database. With such a database, machine learning can be employed using the mappings in the database, for example, periodically, to iteratively improve future mappings and, hence, the cloud feature recommendation based there on; para. 0036 - set of common features that is considered for mapping include those in Table A; para. 0037 - Special features - Modernization and feature upgrades expected - Modernization of cloud service; para. 0038 - database of mapping may be used and iteratively enhanced based on successfully mapped application features or entities to cloud provider solution features; para. 0088 - Decision data structures as set forth herein can be updated by machine learning so that accuracy and reliability is iteratively improved over time without resource consuming rules intensive processing. collecting rich data into a data repository and additional particular arrangements for updating such data and for use of that data to drive artificial intelligence decision making; para. 0099 - periodically applying machine learning using the stored mapping results to iteratively improve the mapping.); and
generating, by the processor set, at least one recommended disposition of at least one technical entity to a second technical entity based at least one a business constraint corresponding to the natural language problem statement using the standardized technical entities, the standardized business entities, and the standardized dispositions (see para. 0016 - recommendations are based on text (alphanumeric characters) received from the user describing item(s) (e.g., an application) for possible migration to a different computing environment, for example, with cloud feature(s), the text including structured and unstructured text; para. 0025 - client or customer ("user") wants to find a multi-provider solution with cloud feature(s) for their Business Intelligence (BI) application; para. 0026 - features or controls that drive the solution including cost, RAM, storage space, coverage of service, performance, etc.; para. 0028 - mapped cloud feature(s) are then used to provide cloud feature recommendation(s) 190 to the user for optional consideration thereby; para. 0034 - providing one or more recommendation for cloud feature(s) of available cloud provider(s) solution(s) (or just "cloud solution(s)") for optional consideration by a customer or client ("user") in making a work or employment related decision. are based on a description (e.g., alphanumeric characters) of an item(s) the user is considering to migrate to, or otherwise launch on, cloud provider; network(s). The item(s) may include the following non-limiting examples: an application; an agreement (e.g., a cloud service provider agreement); a service; and a business process; para. 0036 - set of common features that is considered for mapping include those in Table A; para. 0037 - Special features - Modernization and feature upgrades expected - Modernization of cloud service; para. 0043 - recommendation(s) may be formulated 610 based on the mapped cloud feature(s); para. 0082 - provide recommendations for migrating item(s).).
Marzorati does not explicitly disclose using a neural word segmentation method.
Kim teaches or suggests using a neural word segmentation method (see Fig. 5; para. 0004 - facilitating text segmentation by identifying segmentation points that indicate a location at which to segment a text based on relevant context. One method available for creating such a system is using a neural network. Neural networks can be trained to assist in identifying segmentation points. In particular, a text segmentation neural network system can be trained to focus on relevant context while discounting irrel-evant context. This is advantageous because focusing segmentation prediction on relevant content increases the accuracy of the prediction; para. 0017 - reduce the amount of manual time and effort spent to generate pipeline-based text segmentation systems, techniques have been developed using neural network-based models; para. 0018 - facilitating text segmentation using a neural network system specifically trained for identifying segmentation points in a text by focusing on relevant context while discounting irrelevant context; para. 0019 – text segmentation neural network system takes advantage of the fact that text often comprises topically coherent subparts.).
Accordingly, it would have been obvious to one having ordinary skill before the effective filing date of the claimed invention to modify the system and method, taught in Marzorati, to include using a neural word segmentation method for the purpose of efficiently segmenting text using a neural network and focusing on relevant textual context, automating and reducing text processing time while increasing prediction accuracy, as taught by Kim (0004 and 17).
Claim 2:
Marzorati further teaches or suggests wherein the at least one recommendation disposition corresponds to one or more possible target environments along with explanation generated based on the business constraints and mentions of technical entities present in the natural language problem statement (see para. 0016 - recommendations are based on text (alphanumeric characters) received from the user describing item(s) (e.g., an application) for possible migration to a different computing environment, for example, with cloud feature(s), the text including structured and unstructured text; para. 0025 - client or customer ("user") wants to find a multi-provider solution with cloud feature(s) for their Business Intelligence (BI) application; para. 0026 - features or controls that drive the solution including cost, RAM, storage space, coverage of service, performance, etc.; para. 0028 - mapping aspect employs, for example, an Artificial Intelligence eXplainability (AIX) model 185. The mapped cloud feature(s) are then used to provide cloud feature recommendation(s) 190 to the user for optional consideration thereby; para. 0034 - providing one or more recommendation for cloud feature(s) of available cloud provider(s) solution(s) (or just "cloud solution(s)") for optional consideration by a customer or client ("user") in making a work or employment related decision. are based on a description (e.g., alphanumeric characters) of an item(s) the user is considering to migrate to, or otherwise launch on, cloud provider; network(s). The item(s) may include the following non-limiting examples: an application; an agreement (e.g., a cloud service provider agreement); a service; and a business process; para. 0035 - recommendation(s) may first be validated 270, for example, against an explainability model(s). Once validated, the recommendation(s) may be provided to the user for optional consideration, including an explanation(s) to the user; para. 0036 - set of common features that is considered for mapping include those in Table A; para. 0037 - Special features - Modernization and feature upgrades expected - Modernization of cloud service; para. 0043 - recommendation(s) may be formulated 610 based on the mapped cloud feature(s). may be validated 620, for example, against an explainability model(s), the output of which explains, in some fashion, why a particular cloud feature was chosen; para. 0082 - provide recommendations for migrating item(s).).
Claim 3:
Marzorati further teaches or suggests retrieving structured knowledge corresponding to the natural language problem statement from at least one knowledge database by querying the at least one knowledge database with the extracted structured information, wherein the at least one knowledge database includes use case knowledge bootstrapped from use case studies that contain disposition recommendations from one technical entity to another given a business constraint, or a publicly available database having crowdsourced information corresponding to technical entities, business constraints and disposition solutions (see para. 0029 - available cloud features and providers may be predetermined. Successful mappings of an entity or an item feature with a cloud feature may be stored, for example, in a mappings database. With such a database, machine learning can be employed using the mappings in the database, for example, periodically, to iteratively improve future mappings and, hence, the cloud feature recommendation based there on; para. 0032 - Structured data usually resides in relational databases (RDBMS). format is eminently searchable, both with human-generated queries and via algorithms using types of data and field names, such as alphabetical or numeric, currency, or date; para. 0036 - set of common features that is considered for mapping include those in Table A; para. 0037 - Special features that can be considered for mapping include those in Table B below; para. 0038 - database of mapping may be used and iteratively enhanced based on successfully mapped application features or entities to cloud provider solution features; para. 0099 - storing in a mapping database successful results of mapping the one or more item feature or the one or more entity to cloud feature(s), resulting in stored mapping results; and periodically applying machine learning using the stored mapping results to iteratively improve the mapping.).
Claim 4:
Marzorati further teaches or suggest wherein the natural language problem statement includes user disposition preferences including at least one of a preferred modernization target environment and a preferred transformation path, and further comprising: generating the at least one recommended disposition corresponding to the natural language problem statement to consider the at least one of a preferred modernization target environment and a preferred transformation path, and the user disposition preferences, the generating including validating the user disposition preferences against recommended dispositions provided by the disposition recommender processes; and modifying the at least one recommended disposition to accommodate the user disposition preferences (see para. 0025 - client or customer ("user") wants to find a multi-provider solution with cloud feature(s) for their Business Intelligence (BI) application; para. 0027 - mapping between these common features and entities or the remaining features or special features that they want within their cloud service agreement; para. 0028 - inquiry 140 is made as to whether the extraction, for a given listing, resulted in no extracted text portion. If so, the system invokes an automated dialog 145 with the user to find a text portion for the given listing; para. 0035 - the user needs an application to support fifty users, an application may be chosen that has a storage capacity of approximately 50 GB to store data for these users; para. 0043 - A recommendation(s) may be formulated 610 based on the mapped cloud feature(s). In one embodiment, the formulated recommendation(s) may be validated 620, for example, against an explainability model(s), the output of which explains, in some fashion, why a particular cloud feature was chosen. provides the user an understanding of how the requirements of cloud features were mapped with the extracted item feature(s) or entity(ies) to achieve the recommendation. Once validated, the recommendation(s) may be provided 630 to the user for optional consideration, the recommendation(s) including an explanation(s) to the user; para. 0075 - an automated dialog with the user is performed to try to get such missing data if any. Thus, the completeness of the data may be evaluated using the necessary tags that are required to evaluate the application; para. 0076 - some manner of follow up with the user is made to get that information. In one example, automated communications with the user are automatically initiated via, for example, a chat bot using, for example, a dialog-based engagement or a question-answer based engagement.).
Claim 9:
Marzorati teaches or suggests a computer program product comprising one or more computer readable storage media having program instructions collectively stored on the one or more computer storage media, the program instructions executable to:
receive a natural language problem statement corresponding to the application modernization needs of a user, the natural language problem statement including at least one technical entity, business constraint and a disposition information (see para. 0016 - recommendations are based on text (alphanumeric characters) received from the user describing item(s) (e.g., an application) for possible migration to a different computing environment, for example, with cloud feature(s), the text including structured and unstructured text; para. 0025 - client or customer ("user") wants to find a multi-provider solution with cloud feature(s) for their Business Intelligence (BI) application; para. 0026 - features or controls that drive the solution including cost, RAM, storage space, coverage of service, performance, etc.; para. 0034 - are based on a description (e.g., alphanumeric characters) of an item(s) the user is considering to migrate to, or otherwise launch on, cloud provider; network(s). The item(s) may include the following non-limiting examples: an application; an agreement (e.g., a cloud service provider agreement); a service; and a business process; para. 0036 - set of common features that is considered for mapping include those in Table A; para. 0037 - Special features - Modernization and feature upgrades expected - Modernization of cloud service.);
provide extracted structured information by extracting information from the natural language problem statement (see para. 0016 - recommendations are based on text (alphanumeric characters) received from the user describing item(s) (e.g., an application) for possible migration to a different computing environment, for example, with cloud feature(s), the text including structured and unstructured text; para. 0016 - Using text mining, feature(s) of the item(s) are extracted. Text portions are also extracted to identify entity(ies); para. 0017 - Intents and entities are the building blocks of natural language understanding (previously referred to as "Natural Language Processing") or NLP; para. 0018 - preparing a list of label(s), domain data and feature(s) of the item, the list having multiple listings. For each listing in the list, a predetermined amount of the unstructured text that includes a given listing, i.e., a label, domain data or an item feature, is extracted. natural language processing can be applied. Given this context, an entity can be predicted for each unstructured text portion; para. 0028 - Any structured text can be conventionally processed; natural language processing can be applied. Given this context, an entity can be predicted for each unstructured text portion; para. 0074 - structured text can be processed 712 conventionally, for example, simply applying predetermined labels based on the structure. performed by identifying 720 tags/keywords for each item feature and labelling the item features with the tags/keywords. Text is extracted and mapped to relevant tags, to identify completeness of data using these tags; para. 0088 - employ data structuring processes, e.g., processing for transforming unstructured data into a form optimized for computerized processing.);
generating standardized technical entities, standardized business entities, and standardized dispositions by inputting the extracted structured information to at least one machine learning model (see para. 0029 - available cloud features and providers may be predetermined. Successful mappings of an entity or an item feature with a cloud feature may be stored, for example, in a mappings database. With such a database, machine learning can be employed using the mappings in the database, for example, periodically, to iteratively improve future mappings and, hence, the cloud feature recommendation based there on; para. 0036 - set of common features that is considered for mapping include those in Table A; para. 0037 - Special features - Modernization and feature upgrades expected - Modernization of cloud service; para. 0038 - database of mapping may be used and iteratively enhanced based on successfully mapped application features or entities to cloud provider solution features; para. 0088 - Decision data structures as set forth herein can be updated by machine learning so that accuracy and reliability is iteratively improved over time without resource consuming rules intensive processing. collecting rich data into a data repository and additional particular arrangements for updating such data and for use of that data to drive artificial intelligence decision making; para. 0099 - storing in a mapping database successful results of mapping the one or more item feature or the one or more entity to cloud feature(s), resulting in stored mapping results. periodically applying machine learning using the stored mapping results to iteratively improve the mapping.); and
generate at least one recommended disposition of at least one technical entity to a second technical entity based at least one a business constraint corresponding to the natural language problem statement using the standardized technical entities, the standardized business entities, and the standardized dispositions, the at least one recommended disposition corresponding to one or more possible target environments along with explanation generated based on the business constraints and mentions of technical entities present in the natural language problem statement (see para. 0016 - recommendations are based on text (alphanumeric characters) received from the user describing item(s) (e.g., an application) for possible migration to a different computing environment, for example, with cloud feature(s), the text including structured and unstructured text; para. 0025 - client or customer ("user") wants to find a multi-provider solution with cloud feature(s) for their Business Intelligence (BI) application; para. 0026 - features or controls that drive the solution including cost, RAM, storage space, coverage of service, performance, etc.; para. 0028 - mapping aspect employs, for example, an Artificial Intelligence eXplainability (AIX) model 185. The mapped cloud feature(s) are then used to provide cloud feature recommendation(s) 190 to the user for optional consideration thereby; para. 0034 - providing one or more recommendation for cloud feature(s) of available cloud provider(s) solution(s) (or just "cloud solution(s)") for optional consideration by a customer or client ("user") in making a work or employment related decision. are based on a description (e.g., alphanumeric characters) of an item(s) the user is considering to migrate to, or otherwise launch on, cloud provider; network(s). The item(s) may include the following non-limiting examples: an application; an agreement (e.g., a cloud service provider agreement); a service; and a business process; para. 0035 - recommendation(s) may first be validated 270, for example, against an explainability model(s). Once validated, the recommendation(s) may be provided to the user for optional consideration, including an explanation(s) to the user; para. 0036 - set of common features that is considered for mapping include those in Table A; para. 0037 - Special features - Modernization and feature upgrades expected - Modernization of cloud service; para. 0043 - recommendation(s) may be formulated 610 based on the mapped cloud feature(s). may be validated 620, for example, against an explainability model(s), the output of which explains, in some fashion, why a particular cloud feature was chosen; para. 0082 - provide recommendations for migrating item(s).).
Marzorati does not explicitly disclose using a neural word segmentation method.
Kim teaches or suggests using a neural word segmentation method (see Fig. 5; para. 0004 - facilitating text segmentation by identifying segmentation points that indicate a location at which to segment a text based on relevant context. One method available for creating such a system is using a neural network. Neural networks can be trained to assist in identifying segmentation points. In particular, a text segmentation neural network system can be trained to focus on relevant context while discounting irrel-evant context. This is advantageous because focusing segmentation prediction on relevant content increases the accuracy of the prediction; para. 0017 - reduce the amount of manual time and effort spent to generate pipeline-based text segmentation systems, techniques have been developed using neural network-based models; para. 0018 - facilitating text segmentation using a neural network system specifically trained for identifying segmentation points in a text by focusing on relevant context while discounting irrelevant context; para. 0019 – text segmentation neural network system takes advantage of the fact that text often comprises topically coherent subparts.).
Accordingly, it would have been obvious to one having ordinary skill before the effective filing date of the claimed invention to modify the system and method, taught in Marzorati, to include using a neural word segmentation method for the purpose of efficiently segmenting text using a neural network and focusing on relevant textual context, automating and reducing text processing time while increasing prediction accuracy, as taught by Kim (0004 and 17).
Claim 10:
Marzorati further teaches or suggests retrieve structured knowledge corresponding to the natural language problem statement from at least one knowledge database by querying the at least one knowledge database with the extracted structured information (see para. 0029 - available cloud features and providers may be predetermined. Successful mappings of an entity or an item feature with a cloud feature may be stored, for example, in a mappings database. With such a database, machine learning can be employed using the mappings in the database, for example, periodically, to iteratively improve future mappings and, hence, the cloud feature recommendation based there on; para. 0032 - Structured data usually resides in relational databases (RDBMS). format is eminently searchable, both with human-generated queries and via algorithms using types of data and field names, such as alphabetical or numeric, currency, or date; para. 0036 - set of common features that is considered for mapping include those in Table A; para. 0037 - Special features that can be considered for mapping include those in Table B below; para. 0038 - database of mapping may be used and iteratively enhanced based on successfully mapped application features or entities to cloud provider solution features; para. 0099 - storing in a mapping database successful results of mapping the one or more item feature or the one or more entity to cloud feature(s), resulting in stored mapping results.).
Claim 12:
Marzorati further teaches or suggests wherein the natural language problem statement includes user disposition preferences including at least one of a preferred modernized target environment and a preferred transformation path, and further comprising program instructions executable to: generate at least one recommended disposition corresponding to the natural language problem statement, based at least on the at least one of a preferred modernization target environment and a preferred transformation path, and user disposition preferences, the generating including validating the user disposition preferences against recommended dispositions provided by the disposition recommender process; and modify the at least one recommendation disposition to accommodate the user disposition preferences (see para. 0025 - client or customer ("user") wants to find a multi-provider solution with cloud feature(s) for their Business Intelligence (BI) application; para. 0027 - mapping between these common features and entities or the remaining features or special features that they want within their cloud service agreement; para. 0028 - inquiry 140 is made as to whether the extraction, for a given listing, resulted in no extracted text portion. If so, the system invokes an automated dialog 145 with the user to find a text portion for the given listing; para. 0035 - the user needs an application to support fifty users, an application may be chosen that has a storage capacity of approximately 50 GB to store data for these users; para. 0043 - A recommendation(s) may be formulated 610 based on the mapped cloud feature(s). In one embodiment, the formulated recommendation(s) may be validated 620, for example, against an explainability model(s), the output of which explains, in some fashion, why a particular cloud feature was chosen. provides the user an understanding of how the requirements of cloud features were mapped with the extracted item feature(s) or entity(ies) to achieve the recommendation. Once validated, the recommendation(s) may be provided 630 to the user for optional consideration, the recommendation(s) including an explanation(s) to the user; para. 0075 - an automated dialog with the user is performed to try to get such missing data if any. Thus, the completeness of the data may be evaluated using the necessary tags that are required to evaluate the application; para. 0076 - some manner of follow up with the user is made to get that information. In one example, automated communications with the user are automatically initiated via, for example, a chat bot using, for example, a dialog-based engagement or a question-answer based engagement.).
Claim 17:
Marzorati teaches or suggests 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:
receive a natural language problem statement corresponding to application modernization needs of a user, the natural language problem statement including at least one technical entity and business constraint (see para. 0016 - recommendations are based on text (alphanumeric characters) received from the user describing item(s) (e.g., an application) for possible migration to a different computing environment, for example, with cloud feature(s), the text including structured and unstructured text; para. 0025 - client or customer ("user") wants to find a multi-provider solution with cloud feature(s) for their Business Intelligence (BI) application; para. 0026 - features or controls that drive the solution including cost, RAM, storage space, coverage of service, performance, etc.; para. 0034 - are based on a description (e.g., alphanumeric characters) of an item(s) the user is considering to migrate to, or otherwise launch on, cloud provider; network(s). The item(s) may include the following non-limiting examples: an application; an agreement (e.g., a cloud service provider agreement); a service; and a business process; para. 0036 - set of common features that is considered for mapping include those in Table A; para. 0037 - Special features - Modernization and feature upgrades expected - Modernization of cloud service.);
provide extracted structured information by extracting information from the natural language problems statement (see para. 0016 - recommendations are based on text (alphanumeric characters) received from the user describing item(s) (e.g., an application) for possible migration to a different computing environment, for example, with cloud feature(s), the text including structured and unstructured text; para. 0016 - Using text mining, feature(s) of the item(s) are extracted. Text portions are also extracted to identify entity(ies); para. 0017 - Intents and entities are the building blocks of natural language understanding (previously referred to as "Natural Language Processing") or NLP; para. 0018 - preparing a list of label(s), domain data and feature(s) of the item, the list having multiple listings. For each listing in the list, a predetermined amount of the unstructured text that includes a given listing, i.e., a label, domain data or an item feature, is extracted. natural language processing can be applied. Given this context, an entity can be predicted for each unstructured text portion; para. 0028 - Any structured text can be conventionally processed; natural language processing can be applied. Given this context, an entity can be predicted for each unstructured text portion; para. 0074 - structured text can be processed 712 conventionally, for example, simply applying predetermined labels based on the structure. performed by identifying 720 tags/keywords for each item feature and labelling the item features with the tags/keywords. Text is extracted and mapped to relevant tags, to identify completeness of data using these tags; para. 0088 - employ data structuring processes, e.g., processing for transforming unstructured data into a form optimized for computerized processing.);
generate standardized technical entities, and standardized business entities, by inputting the extracted structured information to at least one machine learning model (see para. 0029 - available cloud features and providers may be predetermined. Successful mappings of an entity or an item feature with a cloud feature may be stored, for example, in a mappings database. With such a database, machine learning can be employed using the mappings in the database, for example, periodically, to iteratively improve future mappings and, hence, the cloud feature recommendation based there on; para. 0036 - set of common features that is considered for mapping include those in Table A; para. 0037 - Special features - Modernization and feature upgrades expected - Modernization of cloud service; para. 0038 - database of mapping may be used and iteratively enhanced based on successfully mapped application features or entities to cloud provider solution features; para. 0088 - Decision data structures as set forth herein can be updated by machine learning so that accuracy and reliability is iteratively improved over time without resource consuming rules intensive processing. collecting rich data into a data repository and additional particular arrangements for updating such data and for use of that data to drive artificial intelligence decision making; para. 0099 - storing in a mapping database successful results of mapping the one or more item feature or the one or more entity to cloud feature(s), resulting in stored mapping results. periodically applying machine learning using the stored mapping results to iteratively improve the mapping.);
retrieve structured knowledge corresponding to the natural language problem statement from at least one knowledge database by querying the at least one knowledge database with the extracted structured information (see para. 0029 - available cloud features and providers may be predetermined. Successful mappings of an entity or an item feature with a cloud feature may be stored, for example, in a mappings database. With such a database, machine learning can be employed using the mappings in the database, for example, periodically, to iteratively improve future mappings and, hence, the cloud feature recommendation based there on; para. 0032 - Structured data usually resides in relational databases (RDBMS). format is eminently searchable, both with human-generated queries and via algorithms using types of data and field names, such as alphabetical or numeric, currency, or date; para. 0036 - set of common features that is considered for
mapping include those in Table A; para. 0037 - Special features that can be considered for mapping include those in Table B below; para. 0038 - database of mapping may be used and iteratively enhanced based on successfully mapped application features or entities to cloud provider solution features; para. 0099 - storing in a mapping database successful results of mapping the one or more item feature or the one or more entity to cloud feature(s), resulting in stored mapping results.); and
generate at least one recommended disposition of at least one technical entity to a second technical entity to a second technical entity based at least on the structured knowledge corresponding to the natural language problem statement from the at least one knowledge database, a business constraint corresponding to the natural language problem statement using the standardized technical entities and standardized business entities (see para. 0016 - recommendations are based on text (alphanumeric characters) received from the user describing item(s) (e.g., an application) for possible migration to a different computing environment, for example, with cloud feature(s), the text including structured and unstructured text; para. 0025 - client or customer ("user") wants to find a multi-provider solution with cloud feature(s) for their Business Intelligence (BI) application; para. 0026 - features or controls that drive the solution including cost, RAM, storage space, coverage of service, performance, etc.; para. 0028 - mapping aspect employs, for example, an Artificial Intelligence eXplainability (AIX) model 185. The mapped cloud feature(s) are then used to provide cloud feature recommendation(s) 190 to the user for optional consideration thereby; para. 0034 - providing one or more recommendation for cloud feature(s) of available cloud provider(s) solution(s) (or just "cloud solution(s)") for optional consideration by a customer or client ("user") in making a work or employment related decision. are based on a description (e.g., alphanumeric characters) of an item(s) the user is considering to migrate to, or otherwise launch on, cloud provider; network(s). The item(s) may include the following non-limiting examples: an application; an agreement (e.g., a cloud service provider agreement); a service; and a business process; para. 0035 - recommendation(s) may first be validated 270, for example, against an explainability model(s). Once validated, the recommendation(s) may be provided to the user for optional consideration, including an explanation(s) to the user; para. 0036 - set of common features that is considered for mapping include those in Table A; para. 0037 - Special features - Modernization and feature upgrades expected - Modernization of cloud service; para. 0043 - recommendation(s) may be formulated 610 based on the mapped cloud feature(s). may be validated 620, for example, against an explainability model(s), the output of which explains, in some fashion, why a particular cloud feature was chosen; para. 0082 - provide recommendations for migrating item(s).).
Marzorati does not explicitly disclose using a neural word segmentation method.
Kim teaches or suggests using a neural word segmentation method (see Fig. 5; para. 0004 - facilitating text segmentation by identifying segmentation points that indicate a location at which to segment a text based on relevant context. One method available for creating such a system is using a neural network. Neural networks can be trained to assist in identifying segmentation points. In particular, a text segmentation neural network system can be trained to focus on relevant context while discounting irrel-evant context. This is advantageous because focusing segmentation prediction on relevant content increases the accuracy of the prediction; para. 0017 - reduce the amount of manual time and effort spent to generate pipeline-based text segmentation systems, techniques have been developed using neural network-based models; para. 0018 - facilitating text segmentation using a neural network system specifically trained for identifying segmentation points in a text by focusing on relevant context while discounting irrelevant context; para. 0019 – text segmentation neural network system takes advantage of the fact that text often comprises topically coherent subparts.).
Accordingly, it would have been obvious to one having ordinary skill before the effective filing date of the claimed invention to modify the system and method, taught in Marzorati, to include using a neural word segmentation method for the purpose of efficiently segmenting text using a neural network and focusing on relevant textual context, automating and reducing text processing time while increasing prediction accuracy, as taught by Kim (0004 and 17).
Claim 18:
Marzorati further teaches or suggests wherein the at least one recommended disposition corresponds to one or more possible target environments along with explanation generated based on the business constraints and mentions of technical entities present in the natural language problem statement, and further comprising program instructions executable to: generate the at least one recommended disposition corresponding to the natural language problem statement to consider the at least one of a preferred modernization target environment and a preferred transformation path, and user disposition preferences, the generating including validating the user disposition preferences against the recommended dispositions provided by the disposition recommender process, wherein the natural language problem statement includes user disposition preferences including at least one of a preferred modernized target environment and a preferred transformation path; and modify the at least one recommended disposition to accommodate the user disposition preferences (see para. 0025 - client or customer ("user") wants to find a multi-provider solution with cloud feature(s) for their Business Intelligence (BI) application; para. 0027 - mapping between these common features and entities or the remaining features or special features that they want within their cloud service agreement; para. 0028 - inquiry 140 is made as to whether the extraction, for a given listing, resulted in no extracted text portion. If so, the system invokes an automated dialog 145 with the user to find a text portion for the given listing; para. 0035 - the user needs an application to support fifty users, an application may be chosen that has a storage capacity of approximately 50 GB to store data for these users; para. 0043 - A recommendation(s) may be formulated 610 based on the mapped cloud feature(s). In one embodiment, the formulated recommendation(s) may be validated 620, for example, against an explainability model(s), the output of which explains, in some fashion, why a particular cloud feature was chosen. provides the user an understanding of how the requirements of cloud features were mapped with the extracted item feature(s) or entity(ies) to achieve the recommendation. Once validated, the recommendation(s) may be provided 630 to the user for optional consideration, the recommendation(s) including an explanation(s) to the user; para. 0075 - an automated dialog with the user is performed to try to get such missing data if any. Thus, the completeness of the data may be evaluated using the necessary tags that are required to evaluate the application; para. 0076 - some manner of follow up with the user is made to get that information. In one example, automated communications with the user are automatically initiated via, for example, a chat bot using, for example, a dialog-based engagement or a question-answer based engagement.).
Claim(s) 8 and 16 is/are rejected under 35 U.S.C. 103 as being unpatentable over Marzorati, in view of Kim, and further in view of Bayomi et al., US Publication 2023/0145463 (“Bayomi”).
Claim 8:
Marzorati further teaches or suggests training the at least one machine learning model with the structured information (see para. 0029 - available cloud features and providers may be predetermined. Successful mappings of an entity or an item feature with a cloud feature may be stored, for example, in a mappings database. With such a database, machine learning can be employed using the mappings in the database, for example, periodically, to iteratively improve future mappings and, hence, the cloud feature recommendation based there on; para. 0036 - set of common features that is considered for mapping include those in Table A; para. 0037 - Special features - Modernization and feature upgrades expected - Modernization of cloud service; para. 0038 - database of mapping may be used and iteratively enhanced based on successfully mapped application features or entities to cloud provider solution features; para. 0088 - Decision data structures as set forth herein can be updated by machine learning so that accuracy and reliability is iteratively improved over time without resource consuming rules intensive processing. collecting rich data into a data repository and additional particular arrangements for updating such data and for use of that data to drive artificial intelligence decision making; para. 0099 - storing in a mapping database successful results of mapping the one or more item feature or the one or more entity to cloud feature(s), resulting in stored mapping results. periodically applying machine learning using the stored mapping results to iteratively improve the mapping.).
Marzorati does not explicitly disclose wherein the at least one machine learning model is pre-trained using a publicly available large-scale dataset.
Bayomi teaches or suggests wherein the at least one machine learning model is pre-trained using a publicly available large-scale dataset (see para. 0022 - Chart Profiler accomplishes this task by utilizing the structured medical documents (within a corporate or a publicly available dataset) to train a ML model to classify segments of the medical chart. The classified segments are then indexed in the Profile Index where they can be used by downstream tasks (e.g., adaptive summarizer, chart navigator, search engine, and/or the like).
Accordingly, it would have been obvious to one having ordinary skill before the effective filing date of the claimed invention to modify the system and method, taught in Marzorati, to include wherein the at least one machine learning model is pre-trained using a publicly available large-scale dataset for the purpose of efficiently supplying a machine learning model with adequate training data by using both corporate and publicly available datasets, improving model training and performance, as taught by Bayomi (0022).
Claim 16:
Marzorati further teaches or suggests train the at least one machine learning model with the structured information (see para. 0029 - available cloud features and providers may be predetermined. Successful mappings of an entity or an item feature with a cloud feature may be stored, for example, in a mappings database. With such a database, machine learning can be employed using the mappings in the database, for example, periodically, to iteratively improve future mappings and, hence, the cloud feature recommendation based there on; para. 0036 - set of common features that is considered for mapping include those in Table A; para. 0037 - Special features - Modernization and feature upgrades expected - Modernization of cloud service; para. 0038 - database of mapping may be used and iteratively enhanced based on successfully mapped application features or entities to cloud provider solution features; para. 0088 - Decision data structures as set forth herein can be updated by machine learning so that accuracy and reliability is iteratively improved over time without resource consuming rules intensive processing. collecting rich data into a data repository and additional particular arrangements for updating such data and for use of that data to drive artificial intelligence decision making; para. 0099 - storing in a mapping database successful results of mapping the one or more item feature or the one or more entity to cloud feature(s), resulting in stored mapping results. periodically applying machine learning using the stored mapping results to iteratively improve the mapping.).
Marzorati does not explicitly disclose wherein the at least one machine learning model are pre-trained using a publicly available large-scale dataset.
Bayomi teaches or suggests wherein the at least one machine learning model is pre-trained using a publicly available large-scale dataset (see para. 0022 - Chart Profiler accomplishes this task by utilizing the structured medical documents (within a corporate or a publicly available dataset) to train a ML model to classify segments of the medical chart. The classified segments are then indexed in the Profile Index where they can be used by downstream tasks (e.g., adaptive summarizer, chart navigator, search engine, and/or the like).
Accordingly, it would have been obvious to one having ordinary skill before the effective filing date of the claimed invention to modify the system and method, taught in Marzorati, to include wherein the at least one machine learning model is pre-trained using a publicly available large-scale dataset for the purpose of efficiently supplying a machine learning model with adequate training data by using both corporate and publicly available datasets, improving model training and performance, as taught by Bayomi (0022).
Claim(s) 11 is/are rejected under 35 U.S.C. 103 as being unpatentable over Marzorati, in view of Kim, in view of Bayomi, and further in view of Mass et al., US Publication 2020/0012719 (“Mass”).
Claim 11:
Marzorati further teaches or suggests wherein at least one of the at least one knowledge database includes use case knowledge bootstrapped from use case studies that contain disposition recommendations from one technical entity to another given a business constraint, and wherein at least one of the at least one knowledge database includes … information corresponding to technical entities, business constraints, and disposition solutions (see para. 0029 - available cloud features and providers may be predetermined. Successful mappings of an entity or an item feature with a cloud feature may be stored, for example, in a mappings database. With such a database, machine learning can be employed using the mappings in the database, for example, periodically, to iteratively improve future mappings and, hence, the cloud feature recommendation based there on; para. 0032 - Structured data usually resides in relational databases (RDBMS). format is eminently searchable, both with human-generated queries and via algorithms using types of data and field names, such as alphabetical or numeric, currency, or date; para. 0036 - set of common features that is considered for mapping include those in Table A; para. 0037 - Special features that can be considered for mapping include those in Table B below; para. 0038 - database of mapping may be used and iteratively enhanced based on successfully mapped application features or entities to cloud provider solution features; para. 0099 - storing in a mapping database successful results of mapping the one or more item feature or the one or more entity to cloud feature(s), resulting in stored mapping results; and periodically applying machine learning using the stored mapping results to iteratively improve the mapping.).
Marzorati does not explicitly disclose a publicly available database having crowdsourced information.
Bayomi teaches or suggests a publicly available database (see para. 0022 - Chart Profiler accomplishes this task by utilizing the structured medical documents (within a corporate or a publicly available dataset) to train a ML model to classify segments of the medical chart. The classified segments are then indexed in the Profile Index where they can be used by downstream tasks (e.g., adaptive summarizer, chart navigator, search engine, and/or the like).
Accordingly, it would have been obvious to one having ordinary skill before the effective filing date of the claimed invention to modify the system and method, taught in Marzorati, to include using a neural word segmentation method for the purpose of efficiently supplying a machine learning model with adequate training data by using both corporate and publicly available datasets, improving model training and performance, as taught by Bayomi (0022).
Mass further teaches or suggests having crowdsourced information (see para. 0006 - identifying, for at least one candidate named entity in said set of candidate named entities, an article in a knowledge base of articles, wherein a title of said article matches said candidate named entity; para. 0016 - e knowledge base is a crowd-sourced knowledge base; para. 0019 - said knowledge base, said titles of said articles are selected based, at least in part, on a crowd-sourced consensus-based selection methodology; para. 0038 – the present invention may be able to take advantage of the fact that article titles in Wikipedia (and similar knowledge bases) represent a consensus-based, continuously-refined, crowdsourced accumulation of knowledge.).
Accordingly, it would have been obvious to one having ordinary skill before the effective filing date of the claimed invention to modify the system and method, taught in Marzorati, to include having crowdsourced information for the purpose of efficiently leveraging consensus-based, continuously-refined, crowd-sourced accumulation of knowledge, improving knowledge-based retrieval, as taught by Mass (0019 and 0038).
Allowable Subject Matter
Claims 5-7, 13-15, 19, and 20 are objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
Response to Arguments
Rejections under 35 USC §101:
Applicant argues when properly considered as a whole, independent claim 1 is not directed to a mental process, but instead to a specific technical architecture. The Examiner respectfully disagrees.
As indicated above, and the Examiner notes, that “generating ... standardized technical entities, standardized business entities, and standardized dispositions by inputting the extracted structured information ... generating ... at least one recommended disposition of at least one technical entity to a second technical entity” are limitations corresponding to mental processes –can be performed in the human mind, or by a human using a pen and paper (e.g. a person may generate suggestions or recommendations for one entity to a second entity using constraints in the problem statement and the standardized entities and dispositions.). Further, the “inputting ... to at least one machine learning model” corresponds to generally linking the use of a judicial exception to a particular technological environment or field of use (e.g. using machine learning model for processing and generation; see MPEP §2106.05(h).). Here, the additional elements are recited at a high level of generality. Accordingly, the additional elements do not effectively integrate the abstract idea into a practical application and do not amount to significantly more than the mental processes. The Examiner further notes “using a neural word segmentation method” and “using at least one machine learning model” are similarly recited at a high level of generality, do not effectively integrate the abstract idea into a practical application, and do not amount to significantly more than the mental processes. Further, because the “inputting ... to at least one machine learning model,” “using a neural word segmentation method,” and “using at least one machine learning model” are recited at a high level of generality, it is not apparent that there is a significant change or improvement to how these systems are operated. Accordingly, it is apparent that claim 1, with corresponding claims 9 and 17, is directed to a mental process or processes.
Rejections under 35 USC §103:
Applicant argues Marzorati does not teach or suggest “at least one recommended disposition of at least one technical entity to a second technical entity based at least on a business constraint.”
The Examiner respectfully disagrees.
Marzorati teaches client or customer ("user") wants to find a multi-provider solution with cloud feature(s) for their Business Intelligence (BI) application. Para. 0025. Further, features or controls that drive the solution including cost, RAM, storage space, coverage of service, performance, etc. Para. 0026. Further, mapped cloud feature(s) are then used to provide cloud feature recommendation(s) 190 to the user for optional consideration thereby. Para. 0028. Further, providing one or more recommendation for cloud feature(s) of available cloud provider(s) solution(s) (or just "cloud solution(s)") for optional consideration by a customer or client ("user") in making a work or employment related decision. are based on a description (e.g., alphanumeric characters) of an item(s) the user is considering to migrate to, or otherwise launch on, cloud provider; network(s). The item(s) may include the following non-limiting examples: an application; an agreement (e.g., a cloud service provider agreement); a service; and a business process. Para. 0034. Further, set of common features that is considered for mapping include those in Table A. Para. 0036. Further, Special features - Modernization and feature upgrades expected - Modernization of cloud service. Para. 0037. Further, recommendation(s) may be formulated 610 based on the mapped cloud feature(s). Para. 0043. Further, provide recommendations for migrating item(s). Para. 0082. The Examiner notes Marzorati describes recommendations for the disposition of technical entities to second technical entities in modernization, upgrading, and migration contexts, and based on constraints such as cost, RAM, storage space, coverage of service, performance, etc. Accordingly, Marzorati teaches or suggests “at least one recommended disposition of at least one technical entity to a second technical entity based at least on a business constraint.”
Conclusion
THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
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/ANDREW T MCINTOSH/Primary Examiner, Art Unit 2144
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