DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Status of Claims
Claims 1-20 remain pending and are ready for examination.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 04/03/2025, was filed. The submission is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1:
Independent claim 1 recites a system. Therefore, step 1 is satisfied for claims 1-20.
Step 2A Prong One:
The claim(s) recite(s) mental process steps of:
identify one or more of: an anomaly in the processed data; or two or more correlated events in the processed data; (this step recite abstract mental processes that can be performed by the human mind or practicably with pen and paper. MPEP § 2106.04(a)(2)(II). The concept of determining/identifying an anomaly in the processed data; or two or more correlated events in the processed data is a mental process (e.g., observations, evaluations, judgments, and opinions) that is applied and performed in a computing environment—i.e., an abstract idea. See MPEP § 2106.04(a)(2)(I]); see also Elec. Power Grp., 830 F.3d at 1354 (“[A]nalyzing information by steps people go through in their minds, or by mathematical algorithms, without more, [are] essentially mental processes within the abstract-idea category.”’). ).
Step 2A Prong Two:
The claim/s recites the combination of the additional elements, the additional elements in the claim are:
a data shipper configured to ingest raw data;
a data preprocessor configured to receive the raw data from the data shipper and processes the raw data to generate processed data;
a database that stores the processed data; and
a machine learning engine in communication with the database;
The above bold elements are directed to mere insignificant extra-solution activity. See MPEP 2106.04(d)(I) and 2106.05(g). The act of transmitting data based on the abstract idea fails to integrate the judicial exception into a practical application as it does not differ from those actions that have previously been held to be extra-solution activity, such as “presenting offers to potential customers and gathering statistics generated based on the testing about how potential customers responded to the offers; the statistics are then used to calculate an optimized price”, “selecting information, based on types of information and availability of information in a power-grid environment, for collection, analysis and display”, and “requiring a request from a user to view an advertisement and restricting public access.” The judicial exception is not integrated into a practical application because the remaining additional elements amount to nothing more than generic components recited at a high-level of generality such that it amounts to no more than mere instructions to apply the exception using generic computer components. See MPEP 2106.04(d)(I) and 2106.05(f).
Step 2B:
The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above, the additional elements amount to nothing more than mere instructions to apply the exception using generic computer component(s) and insignificant extra-solution activity. These cannot provide an inventive concept, and thus the claims are patent-ineligible.
Claims 2-20 directed to the same abstract idea without significantly more. The claims either recite an additional insignificant extra-solution activity OR recite an additional mental process to evaluate and judge using pen and paper. There are no additional elements recited in these claims that integrates the abstract idea into a practical application or amounts to significantly more than the abstract idea. Therefore, the claims are rejected under the same abstract idea as claim 1.
Claim Rejections - 35 USC § 102
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale or otherwise available to the public before the effective filing date of the claimed invention.
Claims 1-20 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Gutierrez et al., U.S. Pub No: US 20220309037 A1 (Hereinafter “Gutierrez”).
Gutierrez discloses:
1. A system comprising:
a data shipper configured to ingest raw data (Para [0488] “One or more universal syncers may be used to ingest every data type available from an integrated system or service (data source) according to the methods or mappings defined in the service translation file(s) and/or service translation API."; Para [0750]- This will allow an authenticated client, like a system administrator of the analytics server, to input knowledge derived from raw data into a nodal data structure on the user's behalf.");
a data preprocessor configured to receive the raw data from the data shipper and processes the raw data to generate processed data (Para [0292] “As information reaches analytics server from integrated systems, data is processed and may be associated with a given context.");
a database that stores the processed data (Para [0014] “At least a first part of the context data may be stored in a first data repository and a second part of the context data may be stored within a second data repository."); and
a machine learning engine in communication with the database; wherein the machine learning engine executes a large language model algorithm on the processed data to identify one or more of: an anomaly in the processed data; or two or more correlated events in the processed data (Para [0376]-"In some
configurations, the analytics server may rank the suggested content using a predetermined attribute (e.g., relevance factor, closeness in nodal graph, timestamps). In some configurations, AI and machine learning methods described above can also be used to improve related and suggested content."; Para [0491]-"The analytics server may achieve this by leveraging raw metadata and behavioral data to connect information across third party applications and systems, which is achieved via using one or more data correlation algorithms. These algorithms may estimate relatedness between data to (1) empower a page-rank style algorithm for search and (2) provide users with suggestions of related data (e.g., summaries, analyses, and/or related person, project, message, etc.) whenever they are viewing/accessing electronic content (e.g., a file)."; Para [0501]-"The analytics server may also improve the correlation detection by incorporating more standardized and useful behavioral data. Activity events may be predominantly based on the explicit activity events obtained from integrated systems and services (data sources), which in some cases may be other nodal data structures.").
2. The system of claim 1, wherein the raw data ingested by the data shipper comprises a log file for a network application (Para [0631]-"The analytics server may, via the nodal data structure, build and maintain a digital memory for each user that can be shared, and includes a knowledge graph, activity logs, and tables of relationships for specific types of data like files, messages, and contacts across everything a user does online (and offline).").
3. The system of claim 1, wherein processing the raw data by the data preprocessor comprises amending the raw data to reduce one or more of a storage requirement or a processor requirement for executing the large language model to identify the anomaly in the processed data (Para [0106]-" As a result, the analytics may deliver results in a faster and more efficient manner than provided by conventional and existing file management methods."; Para [0215]-"This method also requires less computing power because fewer nodes are analyzed."
).
4. The system of claim 3, wherein processing the raw data by the data preprocessor comprises one or more of: abbreviating one or more terms in the raw data; substituting a large length identifier in the raw data with a shorter length identifier; or suppressing duplicate information identified in the raw data (Para [0073]-"As described herein, the analytics server 110 may also execute various predetermined protocols to generate unique identifiers for the above-described files/data, identify related files, create a nodal data structure, periodically scan the electronic data repositories, update the nodal data structure, and display related files and context information on the above-described GUI."; Para [0601]-"The summaries may also be aggregated, such that the summaries correspond to larger blocks of time. For instance, rather than getting a summary every 30 minutes, the analytics server may generate a summary for the day (e.g., this is a summary of what the user generally did this day). These larger summaries could also be generated by creating summaries of summaries."; Para [0753]-"In addition to defining storage standards, the nodal data structure protocol may need to implement a standard for how data and entities can be linked, versioned, de-duplicated, or correlated across different nodal data structures").
5. The system of claim 1, wherein processing the raw data by the data preprocessor comprises: identifying two or more pre-processed correlated events in the raw data, wherein the identifying comprises one or more of clustering or classification (Para [0501]-"The analytics server may also improve the correlation detection by incorporating more standardized and useful behavioral data. Activity events may be predominantly based on the explicit activity events obtained from integrated systems and services (data sources), which in some cases may be other nodal data structures."; Para [0601]-"The summaries may also be aggregated, such that the summaries correspond to larger blocks of time. For instance, rather than getting a summary every 30 minutes, the analytics server may generate a summary for the day (e.g., this is a summary of what the user generally did this day). These larger summaries could also be generated by creating summaries of summaries."; Para [0737]-"Graph traversal and clustering algorithms can be used to evaluate the closeness of nodes."); and
suppressing the two or more pre-processed correlated events (Para [0501]-"The analytics server may also improve the correlation detection by incorporating more standardized and useful behavioral data. Activity events may be predominantly based on the explicit activity events obtained from integrated systems and services (data sources), which in some cases may be other nodal data structures."; Para [0601]-"The summaries may also be aggregated, such that the summaries correspond to larger blocks of time. For instance, rather than getting a summary every 30 minutes, the analytics server may generate a summary for the day (e.g., this is a summary of what the user generally did this day). These larger summaries could also be generated by creating summaries of summaries."; Para [0737]-"Graph traversal and clustering algorithms can be used to evaluate the closeness of nodes.").
6. The system of claim 1, wherein processing the raw data by the data preprocessor comprises suppressing an event based on a user configuration, wherein the user configuration comprises an indication of an event type that should be removed from the raw data (Para [0292]-"Each user may then accordingly further customize the GUIs by adding/removing files, applications, and other content from each customized GUI (context)."; Para [0401]-"The GUI 2000 may display contextual associations (data). The GUI 2000 may include contextual actions (e.g., "open in Comake" button) that allow the user to access, move, copy, send, link, add task, or otherwise interact with the identified file in its native content service or other connected/integrated service (opened by the analytics server), depending on the type of action.").
7. The system of claim 1, further comprising a request processor in communication with the machine learning engine, wherein the request processor receives an event output from the machine learning engine, and wherein the event output comprises the one or more of the identified anomaly or the identified two or more correlated events (Para [0167]-"The analytics server may then use a file correlation algorithm and/or scoring algorithm to generate a score that represents a distance between two nodes. In order to achieve this, the analytics server may execute various scoring algorithms and or AI/ML models.").
8. The system of claim 7, wherein the request processor is configured to generate a notification relating to the event output, and wherein the notification comprises: a plaintext notification explaining the event output (Para [0145]-" The GUI 600, including graphical component 640, thereby allows a user to consolidate and manage all updates, notifications, contacts, messages, tasks, and content on a single user interface in a more user-friendly manner."); and
a plaintext recommendation relating to the event output, wherein the plaintext recommendation comprises an indication of a possible action to be taken by a user to assess or resolve the event output (Para[0356]-Non-limiting examples of messages, as used herein, may include text messages, chat messages, email messages, comments, and threads."; Para [0487]-" The analytics server may also, depending on the configuration and the triggers, choose to run additional processes such as virus scanning of files, choose whether or not (or to what level) to deduplicate data, choose to summarize text base content, and SO on. Additionally and alternatively, the analytics server may receive notifications that may prompt certain processes and/or configurations of processes to run.").
9. The system of claim 8, wherein the notification is generated by a large language model (Para [0173]-"The analytics server may generate a set of predetermined features/indicators that indicate whether to nodes may be related. This initial labeling may create a weaker/noisier signal that the analytics server may improve using ML and reinforced learning. The type of nodes (files, messages, users, tasks, notes, etc.) may have some impact on how the analytics server generates the initial labeling. The analytics server may generate an implicit feedback dataset. The analytics server may execute various analytical protocols (e.g., scoring and AI/ML models) using the implicit feedback dataset to identify whether two nodes are related."; Para [0196]-"The AI/ML models may be trained using training datasets (e.g., ground truth datasets) that represent known related nodes. Once trained, the models can be executed to identify whether two nodes are related. The analytics server may also periodically monitor the model's outcome to retrain the models based on identifying false positive and revising various algorithms utilized by the models.").
10. The system of claim 1, further comprising an archive database that is independent of the database that stores the processed data, wherein the archive database receives a stream comprising the raw data, and wherein the archive database stores a copy of the raw data (Para [0024]-"A first node from the nodal data structure may be stored in a first data repository and a second node from the nodal data structure may be stored in a second data repository." Para [0633]-"Notice the GUI 6000 shows a section for "current selection" along with custom actions that may pertain to the data type that is selected. For example, the user may also copy and/or send the content to another user (which would ultimately cause the analytics server to identify new relationships and revise the nodal data structure accordingly). The user may also view related data associated to the user's selection. The user can search anything within the nodal data structure based on and/or using the user's selection. The user can also search third party sources (e.g., search platforms and/or other applications could such a CRM software). Similarly, the user may be able to choose from various third-party digital processes that can work with data type "text", such as a text summarization service from AWS, a sentence restructuring service from WORDTUNE, and so on.").
11. The system of claim 1, further comprising a domain-specific database in communication with the machine learning engine, wherein the domain-specific database stores context information provided to the machine learning engine to improve execution of the large language model algorithm (Para [0173]- "The analytics server may generate a set of predetermined features/indicators that indicate whether to nodes may be related. This initial labeling may create a weaker/noisier signal that the analytics server may improve using ML and reinforced learning. The type of nodes (files, messages, users, tasks, notes, etc.) may have some impact on how the analytics server generates the initial labeling. The analytics server may generate an implicit feedback dataset. The analytics server may execute various analytical protocols (e.g., scoring and AI/ML models) using the implicit feedback dataset to identify whether two nodes are related."; Para [0485]-"The analytics server may store this information as standardized data types within the nodal data structure and provide a variety of interfaces, processes, and actions from a multitude of sources that can be used with any given standardized data type. Similarly, the standardized data types may be stored using one or more different database technologies to make up the nodal data structure.").
12. The system of claim 11, wherein the machine learning engine executes the large language model algorithm based on the processed data and additionally based on the context information stored in the domain-specific database (Para [0195]-"After initial labeling, the analytics server may calculate a compiled relative score between nodes (e.g., between files and other files, between users and other users, and items to identify whether they are related). As described above, the analytics server may generate an implicit feedback dataset that includes all retrieved context/session data (step 1110 in FIG. 11). The analytics server may then execute a scoring algorithm to identify whether the nodes are related (step 1110 in FIG. 11). The analytics server may also execute one or more computer models configured to identify whether the nodes are related. The models may employ artificial intelligence and machine learning algorithms to determine a likelihood of relatedness for each pair of nodes using the implicit data previously retrieved.").
13. The system of claim 1, further comprising a feedback database that stores user feedback pertaining to outputs generated by the machine learning engine (Para [0195]- initial labeling, the analytics server may calculate a compiled relative score between nodes (e.g., between files and other files, between users and other users, and items to identify whether they are related). As described above, the analytics server may generate an implicit feedback dataset that includes all retrieved context/session data (step 1110 in FIG. 11). The analytics server may then execute a scoring algorithm to identify whether the nodes are related (step 1110 in FIG. 11). The analytics server may also execute one or more computer models configured to identify whether the nodes are related. The models may employ artificial intelligence and machine learning algorithms to determine a likelihood of relatedness for each pair of nodes using the implicit data previously retrieved.").
14. The system of claim 1, further comprising a request processor in communication with the machine learning engine, wherein the request processor is configured to execute instructions comprising: receiving a Boolean query relating to data stored on a query database (Para [0350]-"For instance, when the analytics server identifies that an electronic device has accessed XYZ.com, the analytics server may query all context data to identify whether XYZ.com is relevant to any nodes, or is itself a node, within the nodal data structure. The analytics server may use hard factors and/or soft factors to identify relevant nodes to the units of work, including electronic content, presented on or through the electronic device."), wherein the query database is in communication with the request processor and the machine learning engine (see paragraph [0350, 0421]);
execute the Boolean query on the query database (Para [0421]-"The methods and systems described herein can also be integrated with other searching methods, such that the user can query the data within the nodal data structure. For instance, the analytics server may be in communication with a search engine where the analytics server receives query terms, performs the methods described herein to identify appropriate nodes and their corresponding data (query results), and transmits the query results back to the search engine. Additionally or alternatively, the analytics server may display the query results as described herein. ;Para [0427]-" The analytics server may then apply natural language processing algorithms and other analytical protocols (e.g., the artificial intelligence model discussed above that may be operated by the analytics server or a third party) to identify query terms. The analytic server may then query the nodal data structure accordingly. The analytics server may then display the query results using various graphical elements, vernal, or other and components described herein." ;Para [0542]-"The text content could be plain or structured"); and
provide a plaintext response to the Boolean query (Para [0421]-"The methods and systems described herein can also be integrated with other searching methods, such that the user can query the data within the nodal data structure. For instance, the analytics server may be in communication with a search engine where the analytics server receives query terms, performs the methods described herein to identify appropriate nodes and their corresponding data (query results), and transmits the query results back to the search engine. Additionally or alternatively, the analytics server may display the query results as described herein. ;Para [0427]-" The analytics server may then apply natural language processing algorithms and other analytical protocols (e.g., the artificial intelligence model discussed above that may be operated by the analytics server or a third party) to identify query terms. The analytic server may then query the nodal data structure accordingly. The analytics server may then display the query results using various graphical elements, vernal, or other and components described herein." ;Para [0542]-"The text content could be plain or structured");
wherein the plaintext response is generated by a large language model (Para [0421]-"The methods and systems described herein can also be integrated with other searching methods, such that the user can query the data within the nodal data structure. For instance, the analytics server may be in communication with a search engine where the analytics server receives query terms, performs the methods escribed herein to identify appropriate nodes and their corresponding data (query results), and transmits the query results back to the search engine. Additionally or alternatively, the analytics server may display the query results as described herein. ;Para [0427]-" The analytics server may then apply natural language processing algorithms and other analytical protocols (e.g., the artificial intelligence model discussed above that may be operated by the analytics server or a third party) to identify query terms. The analytic server may then query the nodal data structure accordingly. The analytics server may then display the query results using various graphical elements, vernal, or other and components described herein." ;Para [0542]-"The text content could be plain or structured").
15. The system of claim 1, further comprising a request processor in communication with the machine learning engine, wherein the request processor is configured to execute instructions comprising: receiving a plaintext inquiry relating to data stored on a query database, wherein the query database is in communication with the request processor and the machine learning engine; provide the plaintext inquiry to a large language model configured to assess the plaintext inquiry; and provide a plaintext response to the plaintext inquiry, wherein the plaintext response is generated by the machine learning engine (Para [0421]-"The methods and systems described herein can also be integrated with other searching methods, such that the user can query the data within the nodal data structure. For instance, the analytics server may be in communication with a search engine where the analytics server receives query terms, performs the methods described herein to identify appropriate nodes and their corresponding data (query results), and transmits the query results back to the search engine. Additionally or alternatively, the analytics server may display the query results as described herein.' Para [0427]-" The analytics server may then apply natural language processing algorithms and other analytical protocols (e.g., the artificial intelligence model discussed above that may be operated by the analytics server or a third party) to identify query terms. The analytic server may then query the nodal data structure accordingly. The analytics server may then display the query results using various graphical elements, vernal, or other and components described herein. ";Para [0501]-"To provide better recommendations, the analytics server may create a framework for validating the quality of recommendations. In one example, the analytics server may inquire users to rate how meaningful the suggested files are to their work. Another example may include gathering implicit feedback by tracking the number of clicks that the suggested files garner, implying their usefulness to the user. ";Para [0542]-"The text content could be plain or structured").
16. The system of claim 1, further comprising a request processor in communication with the machine learning engine, wherein the request processor is configured to render a graphical representation of an indication of a quantity of anomalies identified in the processed data (Para [0384]-"The analytics server may rank users by number of occurrences across these units of work. In some configurations, the analytics server may only show a limited number of users related to the identified node/electronic content.' Para [0403]-" some configurations, the analytics server may rank the users based on the number of occurrences across these sources.").
17. The system of claim 1, wherein the identified anomaly comprises a threshold-based detection, and wherein the threshold-based detection is based on a predefined threshold for a log attribute, and wherein the log attribute comprises one or more of response time, error rate, or CPU usage (Para [0168]-" For instance, the analytics server may execute various analytical protocols described herein to identify whether two nodes are related. If the likelihood of two nodes being related satisfies a predetermined threshold (e.g., the analytics server can confidently determine that the two nodes relate to each other), the analytics server may link the corresponding notes."; Para [0229]-"For instance, the analytics server may monitor a "response time" associated with each communication event.").
18. The system of claim 1, wherein the machine learning engine is configured to identify the anomaly based on established normal behavior, wherein the established normal behavior is determined based on historical data, and wherein the anomaly constitutes a deviation from the established normal behavior (Para [0281]-"The methods/systems described herein can be used to generate user behavior patterns. For instance, the analytics server may continuously/periodically monitor how a user accesses various files, tasks, and other workflow components. Consequently, the analytics server may generate a model that represents the patterns of behavior associated with each user. The analytics server may use the generated behavior model to predict whether a user's account or device has been compromised, as well as flag potentially dangerous intentional actions by the user. The methods/systems described herein can be utilized to generate news alerts, customized for users based on each user's unique working patterns and relationships to other users.").
19. The system of claim 18, wherein the machine learning engine is configured to identify the deviation from the established normal behavior utilizing one or more of a clustering algorithm or a classification algorithm (Para [0736]-"These processing methods could be further enhanced using NLP techniques (e.g., text classification or named-entity recognition) or computer vision methods (e.g., object classification or facial recognition).").
20. The system of claim 1, wherein the machine learning engine is configured to identify the two or more correlated events in the processed data by performing sequence analysis on the processed data (Para [0111]-" Referring now to FIG. 4, another illustration of a nodal data structure is illustrated. Nodal data structure 400 represents a clustered nodal data structure where the analytics server clusters related files into data clusters 410 and 420. As described above, each node within the nodal data structure 400 represents an identified file. Each node within the nodal data structure 400 may include metadata associated with each respective file (e.g., indicating the location, type, historical data, and context data associated with the file). Upon identifying relationships between files, the analytics server may generate a cluster that represents all related nodes/files.").
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to MAHER N ALGIBHAH whose telephone number is (571)272-0718. The examiner can normally be reached on Monday-Thursday.
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/MAHER N ALGIBHAH/Primary Examiner , Art Unit 2165
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