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 1-4, 6-15, 18 and 20 have been amended and are hereby entered.
Claims 1-20 are pending and have been examined.
This action is made FINAL.
Response to Arguments
Applicant's arguments filed February 6, 2026 have been fully considered but they are not persuasive.
Regarding the applicant's arguments against the 101 rejection of pending claims on pages 14-18: Applicant’s arguments directed to the 101 analysis were considered. However, these arguments are not persuasive and the examiner respectfully disagrees for the following reasons:
For Step 2A-Prong 1 starting in p. 14: The Applicant argues that the pending claims are not directed to any of the abstract ideas identified because “the amended independent claims recite a technological data processing pipeline that uses a large language model to generate and analyze document embeddings (or content items)” as disclosed in the amended limitations and “no human activity involves such processes”. However, the Examiner finds these arguments unpersuasive and respectfully disagrees. Because based on the MPEP 2106 for the 101 analysis in step 2A Prong 1, the Examiner must analyze and determine what the “applicant has invented by reviewing the entire application disclosure and construing the claims in accordance with their broadest reasonable interpretation (BRI)” for Step 2A-Prong 1 and 2 (See MPEP § 2106, subsection II, for more information about the importance of understanding what the applicant has invented, and MPEP § 2111 for more information about the BRI). Moreover, the claim language in each claim “recites” a judicial exception when the judicial exception is “set forth” or “described” in the claim (see MPEP 2106.04, subsection II). Thus, the Examiner closely examined all claim limitations individually and as a whole, and found that the steps fell under a certain method of organizing human activity and mental processes as abstract ideas identified. Specifically, there are certain steps that recite the abstract idea when “generating” digital documents to be shared within an organization and “generating” information flow pattern between teams in an organization which encompasses legal interactions related to handling agreements in the form of contracts as well as commercial interactions directed to managing business relations. Similarly, these same steps also falls under the abstract idea sub-group of “managing personal behavior or relationships or interactions between people” since these the generation of information flow patterns further involves utilizing “the plurality of information flow patterns to determine an information flow pattern” between the first and second team (see claim 8) to “provide information corresponding to the modification of the first document to one or more user accounts” which encompasses monitoring user social activities to follow rules or instructions to further show such document modifications for all teams in the organization. Thus, human activity is clearly involved in the claim language.
As for the argument related to the claims not being directed to a mental process, this is unpersuasive. Because at least the step of “utilize the plurality of information flow patterns to determine an information flow pattern between a first team and a second team” in claim 8, requires observation, evaluation and judgement. Thus, this particular claim still recite the abstract idea of a mental process even if it requires at least one of: (B) physical aid (e.g. pen and paper) and/or (C) a computer (see MPEP 2106.04(a)(2)(III)(B & C)). Also, this step can be done with the help of physical aid which does not negate the mental nature of the limitation(s), even when using other generic computer components with a large language model (LLM) to provide “recommended action to one or more user accounts associated with the second team within the organization account” that is based on textual description of the document or content item modifications relevant to multiple teams, as the Applicant asserts in p. 15 from Remarks.
For Step 2A-Prong 2 and Step 2B starting in p. 16: The Applicant alleges in the second and third reasons in pp. 16 and 19 from Remarks that the claims integrate, the judicial exception identified, into a practical application. The Applicant further alleges that the amended claims are reciting “elements that improve the functioning of a computer, or an improvement to another technology or technical field” as the specification explains and “provide an improvement to a technical problem specific to the operation of computing systems by improving flexibility, accuracy, and efficiency relative to existing digital content systems”. Also, the claimed invention improves the “efficiency relative to existing digital content systems” since existing systems are inefficient and “store and maintain multiple inaccurate and/or obsolete versions of content items across hierarchical layers, resulting in wasted storage and repeated reprocessing when errors arise from incompatible versions”. However, the Examiner finds these arguments unpersuasive and respectfully disagrees. Because the identified limitations in the claims did not integrate a judicial exception into a practical application since the steps were merely reciting the words "apply it" (or an equivalent) with the judicial exception, or merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea (see MPEP 2106.05(f) and 2106.04(d)(I)). Specifically, the claims’ limitations are reciting the use of a generic computer that further “uses” a large language model (LLM) that is generally/broadly recited, that further “extract” modification data as well as indications of content item modifications and corresponding timestamps and “analyze” document embeddings and content item data for patterns in modifications a plurality of information flow patterns to achieve the intended result of providing “recommended action(s)” to user accounts associated with a second team. Also, “claiming the improved speed or efficiency inherent with applying the abstract idea on a computer" does not integrate a judicial exception into a practical application or provide an inventive concept” (see MPEP 2106.05(f)(2); TLC communications). Lastly, these limitations and their additional elements, individually and in combination, are not “significantly more” as these are recited in a high level of generality that cannot provide an inventive concept at Step 2B, and are not integrating the abstract idea into a practical application. (see MPEP 2106.05).
Thus, for all the reasons stated above, the Examiner respectfully disagrees, and maintains 35 USC § 101 rejection for these pending claims.
Regarding to Applicant's arguments of rejection under 35 USC §102 and 35 USC §103 for the pending claims on pages 18 – 24: Applicant’s arguments regarding these amended limitation steps in the pending claims are not persuasive and the Examiner respectfully disagrees. Because upon re-evaluation of the prior art previously referenced for Majumdar and Somech, their combination still reasonably teaches the new amended steps that the Applicant alleges not being taught. Thus, the claims are no longer rejected under 35 USC §102 rejection and any allegations are considered moot. As for the arguments directed to 35 USC §103 rejection, Applicant is focusing on each prior art teachings, rather than focusing on the actual language claimed in each claim limitation and how their corresponding limitation steps are different from the prior art teachings. Rather, the steps disclose a broader language that the prior art combination of Majumdar and Somech, still reasonably satisfies and teaches in light of the broadest reasonable interpretation (BRI) of the claim language. Please, refer to the Claim Rejections - 35 USC § 103 section for further details. Therefore, the Examiner respectfully disagrees, and maintains 35 USC § 103 rejection for these pending claims.
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. The analysis of this claimed invention recited in the claims begins in view of independent claims 8 and 15, the most representative claim of the independent claims set 1, 8 and 15, as follows:
At Step 1: Claims 1 – 7 falls under statutory category of a process, claims 8 – 14 are directed to an article of manufacture, and claims 15 – 20 are directed to method considered a machine.
At Step 2A Prong 1: Claim 15 (representative of claim 1) and claim 8 recite an abstract idea, which is defined in the following limitations:
For claim 15 (representative of claim 1):
…generate…to extract modification data from digital documents shared within an organization account…, the document embeddings comprising indications of document modifications and corresponding timestamps;
generate…to analyze the document embeddings for modifications referenced in communications between a first team and a second team within the organization account, an information flow pattern between a first team and a second team;
generate, based on analyzing the information flow pattern, a recommended action comprising text describing a modification of a first document of the digital documents relevant to the first team and the second team; and
provide, based on the text describing the modification of the first document relevant to the second team, the recommended action to the modification of the first document to one or more user accounts associated with the second team within the organization account.
For claim 8:
generate…to extract one or more indications of content item modifications and corresponding timestamps from content items shared within an organization account…, content item data comprising a data package including the one or more indications of content item modifications and the corresponding timestamps;
generate…to analyze the content item data for patterns in modifications, a plurality of information flow patterns corresponding to the content items;
utilize the plurality of information flow patterns to determine an information flow pattern between a first team and a second team within the organization account;
generate, based on analyzing the information flow pattern, a recommended action comprising text describing a modification of a first content item of the content items relevant to the first team and the second team; and
provide based on the text describing the modification of the first content item relevant to the second team, the recommended action to one or more user accounts associated with the second team within the organization account.
Generally, and as disclosed in the specification in ¶0018 and ¶0029, this claimed invention “can extract content item embeddings in response to detecting a modification and use a large language model to process the content item embeddings to determine information flow patterns for projects” and “flexibly propagates changes and synchronizes relevant portions of content items and projects across an organization, including across disparate teams separated by many layers in an organizational hierarchy.” However, the abstract idea(s) of a certain method of organizing human activity (See MPEP 2106.04(a)(2), subsection II) is recited in claims 8 and 15 in the form of “commercial or legal interactions”. Specifically, the abstract idea is recited in part in the steps of “generate…document embeddings for digital documents shared within an organization account” and “generate…an information flow pattern between a first team and a second team”. Because generating digital documents to be shared within an organization and generating information flow pattern between teams in an organization at least encompasses legal interactions related to handling agreements in the form of contracts as well as commercial interactions directed to managing business relations. Similarly, these steps also falls under the abstract idea sub-group of “managing personal behavior or relationships or interactions between people” since these the generation of information flow patterns further involves “utilize the plurality of information flow patterns to determine an information flow pattern” between the first and second team (see claim 8) to “provide information corresponding to the modification of the first document to one or more user accounts” which encompasses monitoring user social activities to follow rules or instructions to further show such document modifications for all teams in the organization.
At least the step of “utilize the plurality of information flow patterns to determine an information flow pattern between a first team and a second team” in claim 8 falls under the abstract idea of mental processes that can be practically be performed in the human mind or in pen and paper (See MPEP 2106.04(a)(2), subsection III). Because utilizing the flow pattern information to determine this information between teams encompass observation, evaluation and judgement. Also, this step can either be done with the help of physical aid such as pen and paper or can be performed by humans without or with the assistance (e.g. tool) a computer. Thus, this step does not negate and further still reads in the mental nature of the limitation(s), when determining such modifications and information, as well as the concept is merely claimed to be performed on a generic computer and is merely using a computer as a tool to perform the concept of providing “information corresponding to the modification” of documents to the users (see MPEP 2106.04(a)(2)(III)(B & C)).
At Step 2A Prong 2: For independent claims 1, 8 and 15, The judicial exception(s) or abstract idea previously identified is not integrated into a practical application (see MPEP 2106.04 (d)). The claims recite the additional element(s) of at least one processor, (from claims 8 and 15) at least one non-transitory computer-readable storage medium (from claim 15); a content management system and a large language model (from claims 1, 8 and 15). These additional elements, individually and in combination, and while considering the claims as a whole, are merely used as a tool to perform the abstract idea (See MPEP 2106.05(f)). Specifically, steps of “generate” document embeddings for digital documents shared within an organization account, “content item data” from content items shared within an organization account (from claim 8 only), “generate…the document embeddings” and “generate an information flow pattern” and “provide” recommended action corresponding to the modification of the first content item to one or more user accounts” (from claims 1, 8 and 15) are recited as being performed by the computer and a large language model. The computer and the large language model used are recited at a high level of generality that is being used as a tool to perform the generic computer functions for providing document modifications to users. Thus, these steps mentioned above are further describing and applying the abstract idea without placing any limits on how the technological components are being improved, while distinguishing in the claim language, the performing limitations from functions that generic computer components can perform.
As for the steps of “…to extract” “modification data from digital documents” and “one or more indications of content item modifications and corresponding timestamps”, these are also broadly recited is performed generally to apply the abstract idea without placing any limits on how the “extraction” of content item data is performed distinctively from generic computer components and without the function being generally be invoked as an “apply it” to a computer.
Finally, the steps of “provide” recommended action corresponding to the modification of the first content item to one or more user accounts in the representative claims are really nothing more than links to computer for implementing the use of ordinary capacity for economic or other tasks (e.g., to receive, store, or transmit data) or simply adding a general-purpose computer or computer components (refer to MPEP 2106.05 f (2)). Thus, in these limitation steps, the computer is used to perform an abstract idea, as discussed above in Step 2A, Prong One, such that it amounts to no more than mere instructions to apply the exception using a generic computer.
Therefore, this analysis is indicative of the fact that even when viewed in combination, the claims’ additional elements do not integrate the abstract idea or judicial exception into a practical application.
Step 2B: For independent claims 1, 8 and 15, these claims do not provide an inventive concept. The recited additional elements of the claim(s) are the following: at least one processor, (from claims 8 and 15) at least one non-transitory computer-readable storage medium (from claim 15); a content management system and a large language model (from claims 1, 8 and 15), including the “extract” step for content item data. These additional elements are not sufficient to amount significantly more than the judicial exception or abstract idea (see MPEP 2106.05). Because, as indicated in Step 2A Prong 2, these additional element(s) claimed are merely, instructions to “apply” the abstract ideas, which cannot provide an inventive concept. Also, the recitation of a computer to perform the claim limitations amounts to no more than mere instructions to apply the exception using a generic computer component. Thus, even when considered in combination, these additional elements represent mere instructions to implement an abstract idea or other exception on a computer, which do not provide an inventive concept at Step 2B.
For dependent claims 2-7, 9-14 and 16 - 20, the same analysis is incorporated. Due to their dependency to the independent claims analyzed, these claims cover or fall under the same abstract idea(s) of a method of organizing human activity and mental processes. They describe additional limitations steps of:
Claims 2-7, 9-14 and 16 - 20: further describes the abstract idea of the method for “providing information corresponding to the modification of the first document to one or more user accounts” and further discloses indications of the document modification (e.g. timestamps) as well as their identification, mapping the document embeddings in an embedding space to identify similar projects and determine the information flow pattern as well as other type of user information, generate and providing update propagating communication including project report documents and summaries from extracted data, detect communication formats and providing user notifications about document modifications for digital access. Thus, being directed to the abstract idea groups of “commercial or legal interactions” as these are related to handling agreements in the form of contracts as well as commercial interactions directed to manage business relations and are also directed “managing personal behavior or relationships or interactions between people” as it involves monitoring user social activities to follow rules or instructions to further show such document modifications for all teams in the organization that also require observation, evaluation and judgement when identifying data modifications that is a mental process.
Step 2A Prong 2 and Step 2B: For dependent claims 2-7, 9-14 and 16 - 20, these claims do not include additional elements, but further instruct one to practice the abstract idea by using general computer components that merely are used as a tool. Thus, it amounts no more than mere instructions to apply the exception using a generic computer component (MPEP 2106.05(f) and (f)(2)). Accordingly, for the same reasons stated above, these additional element(s) claimed cannot provide an inventive concept at Step 2B.
Finally, the additional elements previously mentioned above, are nothing more than descriptive language about the elements that define the abstract idea, and these claims remain rejected under 101 as well.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1 - 20 are rejected under 35 U.S.C. 103 as being unpatentable over Majumdar (U.S. Pub No. 20220350810 A1) in view of Somech (U.S. Pub No. 20180218734 A1).
Regarding claims 1 and 15:
This independent claim set is represented by claim 15
Majumdar teaches:
at least one processor; and at least one non-transitory computer-readable storage medium storing instructions that, when executed by the at least one processor, cause the system to: (See Fig. 2 (222) and Fig. 7 (702): Refer to ¶0071 – 72 and ¶0101 for more details.)
generate using a large language model to extract modification data from digital documents shared within an organization account of a content management system, the document embeddings comprising indications of document modifications and corresponding timestamps; (In ¶0114 – 115; Fig. 8 (802 – 804): teaches that “at 802, information relating to a group of entities, and respective relationships between respective entities of the group of entities, can be extracted from electronic documents, tables, and databases in a desired structured format, based at least in part on an analysis of the electronic documents, tables, and databases, and entity-related information relating to the entities”, in accordance to the “extracting” definition given in ¶0019 from Applicant disclosure. Then, “at 804, the respective entities and the respective relationships between the respective entities can be embedded in a common representation to create an embedding model that can be trained to be representative of the respective entities and the respective relationships between the respective entities, based at least in part on the results of an analysis of the information relating to the entities and the respective relationships between the respective entities and/or the entity-related information”, in accordance to the “document embedding” definition given in ¶0035 from Applicant disclosure. Finally, the indications of document modifications that the document embeddings comprised of, as claimed are directed to the system being able to “identify the correct full (e.g., expanded) meaning of the abbreviation or acronym of a column name in a table or database, based on embeddings of similar columns, their relationships with other data elements, and/or the context of use of the abbreviation or acronym, and can suggest a desirable candidate data modification that can provide a more informative column name or description (e.g., a correct full column name or description identified and expanded from the abbreviation or acronym) for the column in the table or database” as well as receive “data dictionaries 110 and metadata 112 relating to the tables 106 and databases 108, and the columns, rows, and data samples (e.g., data elements or items of data)” that further “provide definitional information, contextual information, or other information that can define or provide context for the tables 106, the databases 108, and/or at least some of the data elements of the tables 106 or databases 108 (e.g., a particular data dictionary can indicate that, in a particular table, a column name “carbs” is an abbreviation of the word “carbohydrates”)” (see ¶0032 and ¶0039 – 40). See ¶0027 for the storage of the “information relating to the modification of the new entity, the feedback information, the results of evaluating the feedback information, and/or information relating to the updated embedding model in the version control repository” and see ¶0030 and ¶0048 for the AI techniques (i.e. machine learning) such as “information extraction model” the system can employ for information analysis and relationship determination between data items. However, this prior art does not teach the corresponding timestamps claimed.)
generate, using the large language model to analyze the document embeddings for modifications referenced in communications between a first team and a second team within the organization account, an information flow pattern between a first team and a second team; (In ¶0115 – 116; Fig. 8 (804 – 806): teaches “the DMC can employ the AI component to perform an AI analysis on the information relating to the respective entities and the respective relationships between the respective entities and/or the entity-related information, and can create the embedding model (e.g., a trained AI-based embedding model) based at least in part on the results of the AI analysis” and “at 806, with regard to a new (e.g., subsequent) entity associated with an electronic document (e.g., a new entity of or associated with a table, database, or freeform information of the electronic document) that is received subsequent to the group of electronic documents, a relationship between the new entity and one or more entities of the group of entities can be predicted based at least in part on the embedding model” which is directed to generating information flow patterns between teams in an organization account based on the analysis of document embeddings for modifications referenced in communications, in accordance to the examples given in ¶0023, ¶0056 – 59 and ¶0094 as well as the “information flow pattern” definition given in ¶0039 from Applicant disclosure. Refer to ¶0058 wherein the “the model component 202, employing the embedding model, and/or the AI component 204 can analyze the selection information and/or other feedback information to facilitate determining whether any modifications (e.g., adjustments or changes) are to be made to the embedding model, the relationships 118 between entities 116 associated with the embedding model, and/or the weights associated with the entities 116 or relationships 118.”)
generate, based on analyzing the information flow pattern, a recommended action comprising text describing a modification of a first document of the digital documents relevant to the first team and the second team; and (In ¶0119; Fig. 8 (812): teaches at step 812 that the “DMC can present (e.g., communicate or display) or facilitate presenting the data modification information relating to the ranking as an output (e.g., via a communication device or interface component) for evaluation by the user or an evaluation component of the DMC” wherein the “data modification information” includes attributes such as “demographic and/or sensitive attributes, based at least in part on the data elements of the databases being managed by the DMC 102, the new data under consideration, and the context associated with the new data, the bias management component 126 can determine that there can or may be undesired bias, or can or may be a threshold level of bias, associated with the operations being performed by the DMC 102 and the data modifications being performed or proposed by the DMC 102” (see ¶0068). Refer to ¶0093 – 94 wherein the system can infer relevant entities and events that can be further recommended to the recipient and to ¶0087 – 90 for an example wherein “analysis results, including the identified entities and relationships, and the embedding model that can be created in part from such analysis results, which can include contextual information regarding the context of the use of “CBA” as the column name” of a table in an electronic document. See ¶0070 for alerts sent to the user that are indications of analyzed patterns of relevant information the user needs to know about the quality of a data stream.)
provide, based on the text describing the modification of the first document relevant to the second team, the recommended action to one or more user accounts associated with the second team within the organization account. (In ¶0119; Fig. 8 (810 – 812): teaches “at 812, data modification information relating to the ranking of the candidate data modifications associated with the new entity can be presented as an output” wherein the “DMC can present (e.g., communicate or display) or facilitate presenting the data modification information relating to the ranking as an output (e.g., via a communication device or interface component) for evaluation by the user or an evaluation component of the DMC”.)
Majumdar does not explicitly teach the ability of having document embeddings that specifically comprises of timestamps of the indications of document modifications. However, Somech teaches:
…document embeddings comprising indications of document modifications and corresponding timestamps (In ¶0051; Fig. 2 (256, 236 and 284); Fig. 5 (506): teaches that an “contextual information extractor 284” can “determine contextual information in relation to project entities” which are “data objects” such as “files, documents, emails, events, calendar events, meetings”, etc. (see ¶0033) and that further include “contextual information about the location, such as venue information (e.g., this is the user's office location, home location, conference room, library, school, restaurant, move theater, etc.), time, day, and/or date, which may be represented as a time stamp associated with the event”. Refer to ¶0097 and ¶0099 for more details of the “project determiner 236” identifying “common time-related features for the clustering algorithms” per each “project entity” and grouping “time slots” for each “project entity”.)
It would have been obvious to one of ordinary skill in the art before the earliest effective filing date of the claimed invention to modify Majumdar to provide the ability of having document embeddings that specifically comprises of timestamps of the indications of document modifications, as taught by Somech in order to “reliably capture and track a significant portion of project-related information” in order to be “properly leveraged by computing systems” and provide “computationally efficient access to this tagged data for various applications, including personalizing content to users.”(¶0002 and ¶0006; Somech).
Regarding claim 8:
Majumdar teaches:
generate, using a large language model to extract one or more indications of content item modifications and corresponding timestamps from content items shared within an organization account of a content management system, … (In ¶0114 – 115; Fig. 8 (802 – 804): teaches that “at 802, information relating to a group of entities, and respective relationships between respective entities of the group of entities, can be extracted from electronic documents, tables, and databases in a desired structured format, based at least in part on an analysis of the electronic documents, tables, and databases, and entity-related information relating to the entities”, in accordance to the “extracting” definition given in ¶0019 from Applicant disclosure. Then, “at 804, the respective entities and the respective relationships between the respective entities can be embedded in a common representation to create an embedding model that can be trained to be representative of the respective entities and the respective relationships between the respective entities, based at least in part on the results of an analysis of the information relating to the entities and the respective relationships between the respective entities and/or the entity-related information. Finally, the indications of document modifications that the document embeddings comprised of, as claimed are directed to the system being able to “identify the correct full (e.g., expanded) meaning of the abbreviation or acronym of a column name in a table or database, based on embeddings of similar columns, their relationships with other data elements, and/or the context of use of the abbreviation or acronym, and can suggest a desirable candidate data modification that can provide a more informative column name or description (e.g., a correct full column name or description identified and expanded from the abbreviation or acronym) for the column in the table or database” as well as receive “data dictionaries 110 and metadata 112 relating to the tables 106 and databases 108, and the columns, rows, and data samples (e.g., data elements or items of data)” that further “provide definitional information, contextual information, or other information that can define or provide context for the tables 106, the databases 108, and/or at least some of the data elements of the tables 106 or databases 108 (e.g., a particular data dictionary can indicate that, in a particular table, a column name “carbs” is an abbreviation of the word “carbohydrates”)” (see ¶0032 and ¶0039 – 40). See ¶0027 for the storage of the “information relating to the modification of the new entity, the feedback information, the results of evaluating the feedback information, and/or information relating to the updated embedding model in the version control repository” and see ¶0030 and ¶0048 for the AI techniques (i.e. machine learning) such as “information extraction model” the system can employ for information analysis and relationship determination between data items. However, this prior art does not teach the corresponding timestamps claimed.)
generate, using the large language model to analyze the content item data for patterns in modifications a plurality of information flow patterns corresponding to the content items; (In ¶0115 – 116; Fig. 8 (804 – 806): teaches “the DMC can employ the AI component to perform an AI analysis on the information relating to the respective entities and the respective relationships between the respective entities and/or the entity-related information, and can create the embedding model (e.g., a trained AI-based embedding model) based at least in part on the results of the AI analysis” and “at 806, with regard to a new (e.g., subsequent) entity associated with an electronic document (e.g., a new entity of or associated with a table, database, or freeform information of the electronic document) that is received subsequent to the group of electronic documents, a relationship between the new entity and one or more entities of the group of entities can be predicted based at least in part on the embedding model” which is directed to generating information flow patterns corresponding to the content items, in accordance to the examples given in ¶0023, ¶0056 – 59 and ¶0094 as well as the “information flow pattern” definition given in ¶0039 from Applicant disclosure. Refer to ¶0058 wherein the “the model component 202, employing the embedding model, and/or the AI component 204 can analyze the selection information and/or other feedback information to facilitate determining whether any modifications (e.g., adjustments or changes) are to be made to the embedding model, the relationships 118 between entities 116 associated with the embedding model, and/or the weights associated with the entities 116 or relationships 118.”)
utilize the plurality of information flow patterns to determine an information flow pattern between a first team and a second team within the organization account; (In ¶0041 Fig. 8 (804 – 806): teaches “the model component 202 can determine, create, and/or train the information extraction model, wherein the information extraction model can receive the information from the data sources, analyze the information, and extract a group of entities 116 and respective relationships 118 between respective entities 116, and information relating to the respective entities 116 and respective relationships 118, from the documents 104, tables 106, databases 108, data dictionaries 110, metadata 112, and/or external information 114, in the desired structured format, based at least in part on the results of the analysis of such information (as indicated at reference numeral 306 of the data management process 300)”. Also, in ¶0047 – 48, discloses that “model component 202 and/or AI component 204, in connection with creating and utilizing the embedding model, can apply the respective additional structural constraints in connection with respective relationships 118 between respective entities 116 to make the embeddings of the respective entities 116 and the respective relationships 118 between respective entities 116 context-specific, which can enhance the embeddings of the respective entities 116 and the respective relationships 118 between respective entities 116”. Similarly, “the DMC 102 can utilize the information extraction model to determine and extract new (e.g., subsequent) relationships between entities 116 of the group of entities or new entities identified in new data (e.g., new or subsequently received data with regard to the DMC 102)”.)
generate, based on analyzing the information flow pattern, a recommended action comprising text describing a modification of a first content item of the content items relevant to the first team and the second team; and (In ¶0119; Fig. 8 (812): teaches at step 812 that the “DMC can present (e.g., communicate or display) or facilitate presenting the data modification information relating to the ranking as an output (e.g., via a communication device or interface component) for evaluation by the user or an evaluation component of the DMC” wherein the “data modification information” includes attributes such as “demographic and/or sensitive attributes, based at least in part on the data elements of the databases being managed by the DMC 102, the new data under consideration, and the context associated with the new data, the bias management component 126 can determine that there can or may be undesired bias, or can or may be a threshold level of bias, associated with the operations being performed by the DMC 102 and the data modifications being performed or proposed by the DMC 102” (see ¶0068). Refer to ¶0093 – 94 wherein the system can infer relevant entities and events that can be further recommended to the recipient and to ¶0087 – 90 for an example wherein “analysis results, including the identified entities and relationships, and the embedding model that can be created in part from such analysis results, which can include contextual information regarding the context of the use of “CBA” as the column name” of a table in an electronic document. See ¶0070 for alerts sent to the user that are indications of analyzed patterns of relevant information the user needs to know about the quality of a data stream.)
provide based on the text describing the modification of the first content item relevant to the second team, the recommended action to one or more user accounts associated with the second team within the organization account. (In ¶0119; Fig. 8 (810 – 812): teaches “at 812, data modification information relating to the ranking of the candidate data modifications associated with the new entity can be presented as an output” wherein the “DMC can present (e.g., communicate or display) or facilitate presenting the data modification information relating to the ranking as an output (e.g., via a communication device or interface component) for evaluation by the user or an evaluation component of the DMC”.)
Majumdar does not explicitly teach the ability of having content item data that specifically comprises of data packages including content item modifications and corresponding timestamps. However, Somech further teaches:
… content item data comprising a data package including the one or more indications of content item modifications and the corresponding timestamps; (In ¶0051; Fig. 2 (256, 236 and 284); Fig. 5 (506): teaches that an “contextual information extractor 284” can “determine contextual information in relation to project entities” which are “data objects” such as “files, documents, emails, events, calendar events, meetings”, etc. (see ¶0033) and that further include “contextual information about the location, such as venue information (e.g., this is the user's office location, home location, conference room, library, school, restaurant, move theater, etc.), time, day, and/or date, which may be represented as a time stamp associated with the event”. Refer to ¶0097 and ¶0099 for more details of the “project determiner 236” identifying “common time-related features for the clustering algorithms” per each “project entity” and grouping “time slots” for each “project entity”.)
It would have been obvious to one of ordinary skill in the art before the earliest effective filing date of the claimed invention to modify Majumdar to provide the ability of having content item data that specifically comprises of data packages including content item modifications and corresponding timestamps, as taught by Somech in order to “reliably capture and track a significant portion of project-related information” in order to be “properly leveraged by computing systems” and provide “computationally efficient access to this tagged data for various applications, including personalizing content to users.”(¶0002 and ¶0006; Somech).
Regarding claims 2 and 9:
The combination of Majumdar and Somech, as shown in the rejection above, discloses the limitations of claims 1 and 8, respectively.
This dependent claim set is represented by claim 2
Majumdar further teaches:
wherein generating the document embeddings further comprises, extracting a first document embedding comprising a first indication of the modification of the digital document and a first corresponding timestamp. (In ¶0114; Fig. 8 (802): teaches “at 802, information relating to a group of entities, and respective relationships between respective entities of the group of entities, can be extracted from electronic documents, tables, and databases in a desired structured format, based at least in part on an analysis of the electronic documents, tables, and databases, and entity-related information relating to the entities”, in accordance to the “extracting” definition given in ¶0019 from Applicant disclosure. Refer to ¶0041, ¶0048 and ¶0110 for more extraction details that are performed by an “information extraction model (e.g., knowledge extraction model)”. Finally, in the indications of document modifications that the document embeddings comprised of, as claimed are directed to the system being able to “identify the correct full (e.g., expanded) meaning of the abbreviation or acronym of a column name in a table or database, based on embeddings of similar columns, their relationships with other data elements, and/or the context of use of the abbreviation or acronym, and can suggest a desirable candidate data modification that can provide a more informative column name or description (e.g., a correct full column name or description identified and expanded from the abbreviation or acronym) for the column in the table or database” as well as receive “data dictionaries 110 and metadata 112 relating to the tables 106 and databases 108, and the columns, rows, and data samples (e.g., data elements or items of data)” that further “provide definitional information, contextual information, or other information that can define or provide context for the tables 106, the databases 108, and/or at least some of the data elements of the tables 106 or databases 108 (e.g., a particular data dictionary can indicate that, in a particular table, a column name “carbs” is an abbreviation of the word “carbohydrates”)” (see ¶0032 and ¶0039 – 40). See ¶0027 for the storage of the “information relating to the modification of the new entity, the feedback information, the results of evaluating the feedback information, and/or information relating to the updated embedding model in the version control repository”. However, this prior art does not teach the corresponding timestamps claimed.)
Majumdar does not explicitly teach the ability of having a first document embedding that specifically comprises of first timestamps of the first indications of the first document modifications. However, Somech further teaches:
…a first document embedding comprising a first indication of the modification of the digital document and a first corresponding timestamp (In ¶0051; Fig. 2 (256, 236 and 284); Fig. 5 (506): teaches that an “contextual information extractor 284” can “determine contextual information in relation to project entities” which are “data objects” such as “files, documents, emails, events, calendar events, meetings”, etc. (see ¶0033) and that further include “contextual information about the location, such as venue information (e.g., this is the user's office location, home location, conference room, library, school, restaurant, move theater, etc.), time, day, and/or date, which may be represented as a time stamp associated with the event”. Refer to ¶0097 and ¶0099 for more details of the “project determiner 236” identifying “common time-related features for the clustering algorithms” per each “project entity” and grouping “time slots” for each “project entity”.)
It would have been obvious to one of ordinary skill in the art before the earliest effective filing date of the claimed invention to modify Majumdar to provide the ability of having document embeddings that specifically comprises of timestamps of the indications of document modifications, as taught by Somech in order to “reliably capture and track a significant portion of project-related information” in order to be “properly leveraged by computing systems” and provide “computationally efficient access to this tagged data for various applications, including personalizing content to users.”(¶0002 and ¶0006; Somech).
Regarding claims 3, 10 and 17:
The combination of Majumdar and Somech, as shown in the rejection above, discloses the limitations of claims 2, 9 and 15, respectively.
This dependent claim set is represented by claim 10
Majumdar further teaches:
further comprising instructions that, when executed by the at least one processor, cause the computer system to determine the information flow pattern by: mapping the content item data into an embedding space; (In ¶0046: teaches that “the DMC 102 can utilize the AI component 204” (e.g. such as “Word2vec, Seq2vec, Sentence2vec, Dot2vec, fastText, or another desired AI technique or algorithm”) which “can perform an AI analysis on the information of or relating to the entities 116, the respective relationships 118 between the respective entities 116, and/or the auxiliary information to map the structured or unstructured information relating to the entities 116 and relationships 118 to the desired common representation (e.g., a desired common structured format)” and the DMC can further “input the structured information relating to the entities 116 and relationships 118, represented in the common representation, into the embedding model for analysis (e.g., AI, machine learning, or neural network analysis”, in accordance to the “embedding space” definition given in ¶0041 from Applicant disclosure. Refer to ¶0094 – 96 for more details of the “AI component 204” and the “classifier” used to “map an input attribute vector”.)
identifying a similar project from the content item data for the content items by determining that a distance between the content item data and additional content item data corresponding to the similar project satisfies a similarity threshold; and (In ¶0122; Fig. 8 (808 – 810) and Fig. 9 (902 – 904): teaches that “based at least in part on the results of evaluating the data modification information, the DMC can determine whether a probability (or corresponding quality score) associated with a candidate data modification (e.g., a highest ranking candidate data modification) of the candidate data modifications associated with the new entity satisfies (e.g., meets or exceeds; is greater than or equal to) the defined threshold probability (or a corresponding defined threshold quality score)”. Refer to ¶0051 for more details regarding “ranking scores”, “probability values” and/or “quality scores” wherein “quality scores (e.g., ranking scores) associated with the respective candidate data modifications that can indicate the respective or relative qualities (e.g., respective or relative suitabilities) of the respective candidate data modifications based at least in part on the respective probabilities that the respective candidate data modifications of the group of candidate data modifications are the desired candidate data modification”)
determining the information flow pattern based on one or more information flow patterns corresponding to the similar project. (In ¶0123; Fig. 9 (904 – 906): teaches that “at 904, a determination can be made that the candidate data modification is the correct data modification to be selected for use in modifying information of or associated with the new entity” and once selected, the “DMC can modify the information associated with the new entity based at least in part on the candidate data modification” wherein the “electronic document, comprising the new entity that has been modified with the correct data modification, can be stored in the data store”.)
Regarding claims 4 and 11:
Majumdar, as shown in the rejection above, discloses the limitations of claims 3 and 10, respectively.
This dependent claim set is represented by claim 11 while incorporating claim 4 language in brackets.
Majumdar further teaches:
further comprising instructions that, when executed by the at least one processor, cause the computer system to: in response to determining [AND generating] the information flow pattern, generate an update propagation communication [comprising the recommended action] by extracting data relevant to the modification from the [digital document] first content item and relevant to the second team; and provide the update propagation communication to a user account associated with the [digital document] first content item. (In ¶0069: teaches “the DMC 102 can comprise an alert component 218 that can generate alert or notification messages relating to evaluations of candidate data modifications and/or data modification decisions with regard to entities 116 made by the decision component 212, and can communication such alert or notification messages to a user(s), such as user 122 (e.g., via communication device 120), to inform or notify the user(s) of the evaluations of candidate data modifications and/or data modification decisions with regard to entities 116 made by the decision component 212, so that the user(s) can review such evaluations or decisions, if and as desired”, in accordance to the “update propagation communication” definition given in ¶0044 from Applicant disclosure. Also, “the DMC 102 can aggregate information regarding the problems relating to the new data and the source(s) of the new data, and the alert component 218 can include the aggregated information in the alert or notification message.” Finally, “The DMC 102 (e.g., the model component 202, AI component 204, or alert component 218) can analyze information relating to the alerts associated with that data stream and/or other relevant information to determine or infer whether there is a pattern to the alerts being generated for that data stream and/or information that can indicate what the quality issue is or may be”. Refer to ¶0060 wherein the “DMC 102 can comprise a version control component 214 that can store version information relating to the modifications to the information of or associated with the entities (e.g., data elements) in the data store 216 (e.g., in a version control system, repository, or database of the data store 216)” for users to further review version changes.)
Regarding claims 5 and 12:
Majumdar, as shown in the rejection above, discloses the limitations of claims 4 and 11, respectively.
This dependent claim set is represented by claim 5
Majumdar further teaches:
wherein generating the update propagation communication further comprises: determining contact information for the one or more user accounts associated with the second team; (In ¶0093: teaches that the “AI component 204 can employ artificial intelligence techniques and algorithms, and/or machine learning techniques and algorithms, to facilitate determining or inferring users (e.g., social contacts) associated with a recipient user that are to be selected to invite to participate in a pool associated with the recipient user in connection with an event, determining or inferring merchants associated with a recipient user that are to be selected to invite to participate in a pool associated with the recipient user in connection with an event, determining or inferring a gift item (e.g., gift for a good or service associated with a merchant, or a gift in the form of an offer or discount for a good or service provided by a merchant) that can be selected, purchased, or recommended with regard to a pool associated with the recipient user in connection with an event, and/or automating one or more functions or features of the disclosed subject matter”.)
detecting a communication format corresponding to the second team; and (In ¶0094 – 95: teaches that “the AI component 204 can examine the entirety or a subset of the data” to determine and/or identify “a specific context or action” (e.g. directed detecting communication format) that can employ techniques for “composing higher-level events from a set of events and/or data” and result “in the construction of new events or actions from a set of observed events and/or stored event data, whether or not the events are correlated in close temporal proximity, and whether the events and data come from one or several event and data sources”, in accordance to the “communication format” examples given in ¶0042 and ¶0104 from Applicant disclosure.)
generating the update propagation communication in the communication format and comprising the contact information for the one or more user accounts associated with the second team. (In ¶0069: teaches “the DMC 102 can comprise an alert component 218 that can generate alert or notification messages relating to evaluations of candidate data modifications and/or data modification decisions with regard to entities 116 made by the decision component 212, and can communication such alert or notification messages to a user(s), such as user 122 (e.g., via communication device 120), to inform or notify the user(s) of the evaluations of candidate data modifications and/or data modification decisions with regard to entities 116 made by the decision component 212, so that the user(s) can review such evaluations or decisions, if and as desired”, in accordance to the “update propagation communication” definition given in ¶0044 from Applicant disclosure.)
Regarding claim 6 and 20:
The combination of Majumdar and Somech, as shown in the rejection above, discloses the limitations of claims 1 and 15, respectively.
This dependent claim set is represented by claim 6 while incorporating claim 20 language in brackets.
Majumdar further teaches:
further comprising: identifying one or more additional teams within the organization account based on the information flow pattern; (In ¶0093; Fig. 1 (102, 116 and 118); Fig. 4: teaches that the “AI component 204 can employ artificial intelligence techniques and algorithms, and/or machine learning techniques and algorithms, to facilitate determining or inferring users (e.g., social contacts) associated with a recipient user that are to be selected to invite to participate in a pool associated with the recipient user in connection with an event”. Refer to ¶0019 wherein the “DMC” system can “determine a group of entities (e.g., nodes), and respective relationships (e.g., edges) between respective entities, in documents (e.g., electronic documents), tables, and databases” and refer to ¶0116 for details regarding step 806 for predicting “relationship between the new entity and one or more entities of the group of entities associated with the group of electronic documents based at least in part on the embedding model”.)
Majumdar teaches recommended actions by notifying “data modification decisions with regard to entities” in order for the users to be able to review such decisions (see ¶0069; Majumdar). But, Majumdar does not explicitly teach the abilities of generating a project report document from the document relevant to the second team and additional teams to provide its digital access to the first team and their user accounts. However, Somech teaches:
generating a project report document comprising information from the digital document relevant to the second team and the one or more additional teams [and including recommended action]; and (In ¶0163; Fig. 2 (262): teaches “history determiner 262 summarizes the meetings provided by meeting analyzer 290” wherein the summary includes “meeting keywords that were mapped to the project topic(s)” and “one or more lists of entities and one or more indications of how those entities were associated with the meeting”. For example, “lists of documents accessed during the meeting, lists of participants detected from an analysis of the conversation, etc.”)
providing digital access to the project report document to one or more user accounts associated with the first team, the one or more user accounts associated with the second team, and one or more user accounts associated with the one or more additional teams. (In ¶0169 – 170; Fig. 2 (260 and 220): teaches that the “information from project or meeting models may be displayed or made accessible via interface manager 260 and presentation component 220” for the user to be “able to access aspects of and view his or her project or meeting models”. Further, “history determiner 262 may identify a new or recent participant to join a project”. For example, “the participant may be detected in a meeting (e.g., conversation)” and “based on history determiner 262 determining the participant does not correspond to a characteristic project participant, interface manager 260 may provide access to one or more documents or files, summaries, or other information associated with the project”.)
It would have been obvious to one of ordinary skill in the art before the earliest effective filing date of the claimed invention to modify Majumdar to provide the abilities of generating a project report document from the document relevant to the second team and additional teams to provide its digital access to the first team and their user accounts, as taught by Somech in order to “reliably capture and track a significant portion of project-related information” in order to be “properly leveraged by computing systems” and provide “computationally efficient access to this tagged data for various applications, including personalizing content to users.”(¶0002 and ¶0006; Somech).
Regarding claim 7:
The combination of Majumdar and Somech, as shown in the rejection above, discloses the limitations of claim 1.
Majumdar further teaches:
further comprising: in response to identifying the modification of the digital document, modifying a related document corresponding to the second team to reflect the modification of the digital document; and (In ¶0054: teaches that “in response to selection of the desired candidate data modification, the data modification component 208 can modify the information of or associated with the entity 116 (e.g., new entity) under consideration based at least in part on the candidate data modification” and “decision component 212 also can communicate information relating to the selection of the candidate data modification and/or other feedback information relating thereto to the embedding model of the model component 202 (as indicated at reference numeral 316 of the data management process 300)”.)
providing a notification to the one or more user accounts associated with the second team. (In ¶0055: teaches “the DMC 102 (e.g., the model component 202, data modification component 208, decision component 212, or other component of the DMC 102) can communicate the information relating to the group of candidate data modifications associated with the entity 116 as an output to the communication device 120 (as indicated at reference numeral 312 of the data management process 300)” and/or “the DMC 102 or the communication device 120 can communicate the information relating to the group of candidate data modifications associated with the entity 116 to desired communication devices associated with desired users to have such users evaluate the information relating to the group of candidate data modifications associated with the entity 116 and provide their selection of a desired candidate data modification or other feedback information regarding the group of candidate data modifications associated with the entity 116 to the DMC 102 and/or the communication device 120 associated with the user 122.”)
Regarding claim 13:
The combination of Majumdar and Somech, as shown in the rejection above, discloses the limitations of claim 8.
Majumdar further teaches:
further comprising instructions that, when executed by the at least one processor, cause the computer system to: in response to identifying the modification of the first content item, identify a related content item corresponding to the modification of the first content item and the second team; (In ¶0117; Fig. 8 (808): teaches that the “DMC can determine the candidate data modifications (e.g., potential, recommended, or suggested data modifications) associated with the new entity based at least in part on the determined relationship between the new entity and the one or more entities” directed to identifying a related content item. Refer to ¶0053 wherein “the decision component 212 can evaluate the data modification information regarding the candidate data modifications to determine (e.g., automatically determine) which of the candidate data modifications (if any) can be the desired (e.g., correct, accurate, suitable, or optimal) candidate data modification to be selected to use to modify information associated with the entity 116”)
determine that the related content item does not reflect the modification of the first content item; (In ¶0055: teaches that “If, based at least in part on the results of the evaluation, the decision component 212 determines that none of the probabilities or quality scores associated with the candidate data modifications of the group of candidate data modifications satisfy the defined threshold probability or the defined threshold quality score, the decision component 212 can determine that it is not to select a candidate data modification with respect to the entity 116” which is directed to determining that the related content item is not reflecting the modification of the first content item which further does not hold any patentable weight. Then the system can further “determine that information relating to the group of candidate data modifications is to be forwarded (e.g., communicated) to the communication device 120 associated with the user 122 for evaluation by the user 122”.)
modify the related content item to reflect the modification of the first content item; and (In ¶0054: teaches that “in response to selection of the desired candidate data modification, the data modification component 208 can modify the information of or associated with the entity 116 (e.g., new entity) under consideration based at least in part on the candidate data modification” and “decision component 212 also can communicate information relating to the selection of the candidate data modification and/or other feedback information relating thereto to the embedding model of the model component 202 (as indicated at reference numeral 316 of the data management process 300)”.)
provide a notification to the one or more user accounts associated with the second team. (In ¶0055: teaches “the DMC 102 (e.g., the model component 202, data modification component 208, decision component 212, or other component of the DMC 102) can communicate the information relating to the group of candidate data modifications associated with the entity 116 as an output to the communication device 120 (as indicated at reference numeral 312 of the data management process 300)” and/or “the DMC 102 or the communication device 120 can communicate the information relating to the group of candidate data modifications associated with the entity 116 to desired communication devices associated with desired users to have such users evaluate the information relating to the group of candidate data modifications associated with the entity 116 and provide their selection of a desired candidate data modification or other feedback information regarding the group of candidate data modifications associated with the entity 116 to the DMC 102 and/or the communication device 120 associated with the user 122.”)
Regarding claim 14:
Majumdar, as shown in the rejection above, discloses the limitations of claim 13.
Majumdar teaches the automatic modifications interpreted as the DMC system being able to automatically “determine and implement desired data modifications to correct, disambiguate, and/or expand information regarding the entities associated with electronic documents, databases, or tables” (see ¶0033 and ¶0053; Majumdar) and can recommend actions by notifying “data modification decisions with regard to entities” in order for the users to be able to review such decisions (see ¶0069; Majumdar). However, Majumdar does not explicitly teach the abilities of generating a project report document comprising a summary of the automatic modifications to provide its digital access to the first team and their user accounts that are further associated with a second team. Thus, Somech further teaches:
further comprising instructions that, when executed by the at least one processor, cause the computer system to: in response to modifying the related content item, generate a project report document comprising a summary of automatic modifications and the recommended action; and (In ¶0163; Fig. 2 (262): teaches “history determiner 262 summarizes the meetings provided by meeting analyzer 290” wherein the summary includes “meeting keywords that were mapped to the project topic(s)” and “one or more lists of entities and one or more indications of how those entities were associated with the meeting”. For example, “lists of documents accessed during the meeting, lists of participants detected from an analysis of the conversation, etc.”)
provide digital access to the project report document to one or more user accounts associated with the first team and the one or more user accounts associated with the second team. (In ¶0169 – 170; Fig. 2 (260 and 220): teaches that the “information from project or meeting models may be displayed or made accessible via interface manager 260 and presentation component 220” for the user to be “able to access aspects of and view his or her project or meeting models”. Further, “history determiner 262 may identify a new or recent participant to join a project”. For example, “the participant may be detected in a meeting (e.g., conversation)” and “based on history determiner 262 determining the participant does not correspond to a characteristic project participant, interface manager 260 may provide access to one or more documents or files, summaries, or other information associated with the project”.)
It would have been obvious to one of ordinary skill in the art before the earliest effective filing date of the claimed invention to modify Majumdar to provide the abilities of generating a project report document comprising a summary of the automatic modifications to provide its digital access to the first team and their user accounts that are further associated with a second team, as taught by Somech in order to “reliably capture and track a significant portion of project-related information” in order to be “properly leveraged by computing systems” and provide “computationally efficient access to this tagged data for various applications, including personalizing content to users.”(¶0002 and ¶0006; Somech).
Regarding claim 16:
The combination of Majumdar and Somech, as shown in the rejection above, discloses the limitations of claim 15.
Majumdar further teaches:
further comprising instructions that, when executed by the at least one processor, cause the system to generate the information flow pattern in response to identifying a creation of the first document. (In ¶0116; Fig. 8 (806 – 812): teaches “at 806, with regard to a new (e.g., subsequent) entity associated with an electronic document (e.g., a new entity of or associated with a table, database, or freeform information of the electronic document) that is received subsequent to the group of electronic documents, a relationship between the new entity and one or more entities of the group of entities can be predicted based at least in part on the embedding model” and further “candidate modifications associated with the new entity can be determined” (e.g. at step 808), ranked (e.g. at step 810) and outputted (e.g. at step 812; see ¶0119) directed to generating the information flow pattern, in accordance to the generation of the “information flow pattern” example given in ¶0085 from Applicant disclosure. Moreover in ¶0050, “Based at least in part on the respective relationships 118 between respective entities 116, including new relationships between entities, as predicted or determined by the model component 202 using the embedding model, the data modification component 208 can determine candidate (e.g., suggested, recommended, or proposed) data modifications for entities 116, such as a new entity, that can be evaluated to determine which (if any) candidate data modification of the candidate data modifications is to be used to modify information of an entity to correct the information of the entity”.)
Regarding claim 18:
Majumdar, as shown in the rejection above, discloses the limitations of claim 17.
Majumdar further teaches:
further comprising instructions that, when executed by the at least one processor, cause the system to: in response to receiving the information flow pattern, generate an update propagation communication by determining a portion of the first document relevant to the second team to extract data relevant to the modification from the first document; provide the update propagation communication to a user account associated with the first document; (In ¶0069: teaches “the DMC 102 can comprise an alert component 218 that can generate alert or notification messages relating to evaluations of candidate data modifications and/or data modification decisions with regard to entities 116 made by the decision component 212, and can communication such alert or notification messages to a user(s), such as user 122 (e.g., via communication device 120), to inform or notify the user(s) of the evaluations of candidate data modifications and/or data modification decisions with regard to entities 116 made by the decision component 212, so that the user(s) can review such evaluations or decisions, if and as desired”. Moreover, the “DMC 102 can comprise a version control component 214 that can store version information relating to the modifications to the information of or associated with the entities (e.g., data elements) in the data store 216 (e.g., in a version control system, repository, or database of the data store 216)” for users to further review version changes from portions of the first document relevant to the second team. See ¶0042 for more details of “group of entities 116” which “comprise data elements of the documents 104, tables 106, and databases 108, wherein the data elements can comprise, for example, a table, a database, a column of a table or database, a row of a table or database, an item of data (e.g., a data value of data), metadata of or associated with a document or dataset (e.g., table or database), or other type of entity” directed to portion of the first document.)
determine contact information for the one or more user accounts associated with the second team; (In ¶0093: teaches that the “AI component 204 can employ artificial intelligence techniques and algorithms, and/or machine learning techniques and algorithms, to facilitate determining or inferring users (e.g., social contacts) associated with a recipient user that are to be selected to invite to participate in a pool associated with the recipient user in connection with an event, determining or inferring merchants associated with a recipient user that are to be selected to invite to participate in a pool associated with the recipient user in connection with an event, determining or inferring a gift item (e.g., gift for a good or service associated with a merchant, or a gift in the form of an offer or discount for a good or service provided by a merchant) that can be selected, purchased, or recommended with regard to a pool associated with the recipient user in connection with an event, and/or automating one or more functions or features of the disclosed subject matter”.)
detect a communication format corresponding to the second team; (In ¶0094 – 95: teaches that “the AI component 204 can examine the entirety or a subset of the data” to determine and/or identify “a specific context or action” (e.g. directed detecting communication format) that can employ techniques for “composing higher-level events from a set of events and/or data” and result “in the construction of new events or actions from a set of observed events and/or stored event data, whether or not the events are correlated in close temporal proximity, and whether the events and data come from one or several event and data sources”, in accordance to the “communication format” examples given in ¶0042 and ¶0104 from Applicant disclosure.)
and generate the update propagation communication in the communication format and comprising the recommended action and the contact information for the one or more user accounts associated with the second team. (In ¶0069: teaches “the DMC 102 can comprise an alert component 218 that can generate alert or notification messages relating to evaluations of candidate data modifications and/or data modification decisions with regard to entities 116 made by the decision component 212, and can communication such alert or notification messages to a user(s), such as user 122 (e.g., via communication device 120), to inform or notify the user(s) of the evaluations of candidate data modifications and/or data modification decisions with regard to entities 116 made by the decision component 212, so that the user(s) can review such evaluations or decisions, if and as desired”, in accordance to the “update propagation communication” definition given in ¶0044 from Applicant disclosure.)
Regarding claim 19:
The combination of Majumdar and Somech, as shown in the rejection above, discloses the limitations of claim 15.
Majumdar further teaches:
further comprising instructions that, when executed by the at least one processor, cause the system to: in response to identifying the modification of the first document, identify a related document corresponding to the modification of the first document and the second team; (In ¶0117; Fig. 8 (808): teaches that the “DMC can determine the candidate data modifications (e.g., potential, recommended, or suggested data modifications) associated with the new entity based at least in part on the determined relationship between the new entity and the one or more entities” directed to identifying a related document. Refer to ¶0053 wherein “the decision component 212 can evaluate the data modification information regarding the candidate data modifications to determine (e.g., automatically determine) which of the candidate data modifications (if any) can be the desired (e.g., correct, accurate, suitable, or optimal) candidate data modification to be selected to use to modify information associated with the entity 116”)
identify a related portion of the related document; (In ¶0047: teaches “in addition to the relationship information (e.g., connectivity information) regarding the relationships 118 between respective entities 116, the model component 202 and/or AI component 204, in connection with creating and utilizing the embedding model, can receive and analyze information relating to particular domains (e.g., domain-specific information) associated with respective portions of the information received from data sources”.)
compare the modification of the first document with the related portion of the related document to determine that the modification of the first document is newer than a current state of the related portion of the related document; (In ¶0048 – 49: teaches that the “model component 202 and/or the AI component 204, employing the information extraction model, can analyze the new data, including analyzing the new data in relation to the previous data (e.g., the electronic documents 104, tables 106, databases 108, data dictionaries 110, metadata 112, and/or external information 114)” which is directed to comparing the modification with related portions to determine document modifications being newer than the current state of the related portion of the related document, in accordance to the examples given in ¶0101 and ¶0120 from Applicant disclosure. Thus, “based at least in part on the results of such analysis, the information extraction model of the model component 202 can extract information regarding new entities from the new data, extract information regarding respective new relationships between respective new entities from the new data, and/or extract information regarding respective new relationships between respective new entities and respective entities 116 of the group of entities from the new data and the previous data in the desired structured format”. Refer to ¶0060 wherein the “DMC 102 can comprise a version control component 214 that can store version information relating to the modifications to the information of or associated with the entities (e.g., data elements) in the data store 216 (e.g., in a version control system, repository, or database of the data store 216)” for users to further review version changes.)
modify the related portion of the related document corresponding to the second team to reflect the modification of the first document; and (In ¶0054: teaches that “in response to selection of the desired candidate data modification, the data modification component 208 can modify the information of or associated with the entity 116 (e.g., new entity) under consideration based at least in part on the candidate data modification” and “decision component 212 also can communicate information relating to the selection of the candidate data modification and/or other feedback information relating thereto to the embedding model of the model component 202 (as indicated at reference numeral 316 of the data management process 300)”.)
provide a notification to the one or more user accounts associated with the second team. (In ¶0055: teaches “the DMC 102 (e.g., the model component 202, data modification component 208, decision component 212, or other component of the DMC 102) can communicate the information relating to the group of candidate data modifications associated with the entity 116 as an output to the communication device 120 (as indicated at reference numeral 312 of the data management process 300)” and/or “the DMC 102 or the communication device 120 can communicate the information relating to the group of candidate data modifications associated with the entity 116 to desired communication devices associated with desired users to have such users evaluate the information relating to the group of candidate data modifications associated with the entity 116 and provide their selection of a desired candidate data modification or other feedback information regarding the group of candidate data modifications associated with the entity 116 to the DMC 102 and/or the communication device 120 associated with the user 122.”)
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Yogerst (WO Pub No. 2024130180 A2) is pertinent because it is about “methods and systems are described herein for providing historical and contextual data that enables labelers of machine learning input data to aid in labeling decisions, including model evaluation data and label modification data.”
Choudhary (U.S. Pub No. 20210224306 A1) is pertinent because it is “directed to systems, apparatuses, and methods for providing a more effective customer service support system.”
Guggilla (U.S. Pub No. 20200073882 A1) is pertinent because “Artificial intelligence based corpus enrichment for knowledge population and query response apparatuses, methods for artificial intelligence based corpus enrichment for knowledge population and query response, and non-transitory computer readable media having stored thereon machine readable instructions to provide artificial intelligence based corpus enrichment for knowledge population and query response are disclosed herein.”
Ashlock (U.S. Pub No. 20220245201 A1) is pertinent because it “relates generally to the execution of documents, and more specifically to modifications of a document package during the execution of documents in a document management platform.”
Bernardin (U.S. Pub No. 20230351291 A1) is pertinent because it is “a computer-implemented method is provided for managing workflows.”
Funk (U.S. Patent No. 11853700 B1) is pertinent because the “present invention provide methods, apparatuses, systems, computing devices, computing entities, and/or the like for performing efficient and effective natural language processing with greater semantic intelligence by utilizing entity scoring machine learning models.”
THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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/IVONNEMARY RIVERA GONZALEZ/Examiner, Art Unit 3626
/NATHAN C UBER/Supervisory Patent Examiner, Art Unit 3626
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