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 .
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on July 8, 2026 has been entered.
Status of Claims
Claims 1, 4 - 9, 11 - 16 and 18 - 20 have been amended and are hereby entered.
Claims 1-20 are pending and have been examined.
This action is made NON-FINAL.
Response to Arguments
Applicant's arguments filed July 8, 2026 have been fully considered but they are not persuasive.
Regarding the applicant's arguments against the 101 rejection of pending claims on pages 15-21: 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. 16: The Applicant argues that the pending claims are not directed to any of the abstract idea identified because “the quoted limitations define computer-based processing of digital documents, team data, and document embeddings to detect a new type of data object-an information flow pattern-that indicates modification patterns and route machine-generated text” as disclosed in the amended limitations and “no human activity involves such processes” since the “teams and user accounts does not convert the claimed large-language-model processing and embedding-based pattern detection into a human activity”. However, the Examiner finds these arguments unpersuasive and respectfully disagrees. Because the Examiner closely examined all claim limitations individually and as a whole, and based on the MPEP 2106.04, subsection II, found that the steps fell under the identified abstract idea of a certain method of organizing human activity. Specifically, because the limitation steps are reciting the abstract idea identified when “extracting” business content items and digital documents from different teams in an organization that are used for “generating” embeddings of digital documents indicating document modifications made by a team and “generating” information flow pattern between teams to be analyzed and to detect modification patterns in the data that are further “provided” as relevant text to the users encompasses legal interactions related to handling agreements in the form of contracts as well as commercial interactions directed to managing business relations and advertisements (i.e. offering/recommending relevant document resolutions/modifications to user(s) as a service). Similarly, these same steps also falls under the abstract idea sub-group of “managing personal behavior or relationships or interactions between people” since the step for the generation of information flow patterns further involves “utiliz[ing] the plurality of information flow patterns to determine an information flow pattern” between the team and an additional team (see claim 8) to “provide text to one or more user accounts” wherein the text describe each team modifications of the relevant content items which encompasses monitoring user social activities and follow rules or instructions when further showing/providing such document modifications to all teams in the organization for them to evaluate or modify the documents. Finally, at least the “large language model” (LLM) is considered an additional element but, the functions of the LLM “processing and embedding-based pattern detection”, as alleged, are still part of the abstract idea identified.
As for the arguments related to the claims not being directed to a mental process in p. 17 from Remarks, this is unpersuasive, and upon consideration the Examiner found that the abstract idea identified and previously discussed best encompasses and is the most appropriately directed to the claimed invention and its limitation steps. However, the Examiner reminds the Applicant that more than one abstract idea can be recited in a set of limitations. In other words, “It should be noted that these groupings are not mutually exclusive, i.e., some claims recite limitations that fall within more than one grouping or sub-grouping” (see MPEP 2106.04(a)).
For Step 2A-Prong 2 and Step 2B starting in p. 18: The Applicant alleges that the claims integrate, the judicial exception identified, into a practical application because “the currently amended claims 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” and the Applicant further compares the claims to USPTO Subject Matter Eligibility Example 48, claim 2 since “the recited "information flow pattern" improves downstream computer processing in the content management system by supporting the claimed step of "providing…the text…," rather than relying on rigid account-to-account transfer chains and/or wastefully storing obsolete data among team accounts”. 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)) to achieve the recited end result of the relevant document/content item modifications. Specifically, the claims’ limitations are reciting the use of a generic computer that further is “utilizing”, as alleged by the Applicant, an LLM that is generally/broadly recited, that further “extract” team data indications of content item modifications from a team, “generate” document embeddings and/or content item data and information flow patterns to achieve the intended result of providing “text” as recommendations of relevant document modifications to user accounts associated with an additional team. Thus the alleged “technical improvement to the architecture and operation of digital content management systems” for automatic data synchronization and propagation for achieving “demonstrable gains in flexibility, accuracy, and computational efficiency of implementing devices” is still the intended result of invoking a computer used as a tool to perform the functions that recite the abstract idea identified (see MPEP 2106.05(f) and 2106.04(d)(I)). Thus, “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 claims is further narrowing the drafted abstract ideas identified and their specificity in their limitations which does not necessarily equate to eligibility. For instance, In buySAFE, Inc. v. Google, Inc. (Fed. Cir. 2014), the court stated that "abstract ideas, no matter how groundbreaking, innovative, or even brilliant, are outside what the statute means by "new and useful process, machine, manufacture, or composition of matter", and reference is made to Myriad by the court for this position. Also stated in buySAFE is "In defining the excluded categories, the Court has ruled that the exclusion applies if a claim involves a natural law or phenomenon or abstract idea, even if the particular natural law or phenomenon or abstract idea at issue is narrow. Mayo, 132 S. Ct. at 1303. The Court in Mayo rejected the contention that the very narrow scope of the natural law at issue was a reason to find patent eligibility, explaining the point with reference to both natural laws and one kind of abstract idea, namely, mathematical concepts.
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 21 – 27: 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 of providing the different types of information that the information flow pattern comprised of, including text with relevant modifications, specifically directed to the teams that the Applicant alleges not being taught. Because Applicant is focusing on each prior art teachings, rather than focusing on the actual language claimed as well as the breadth of 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 when evaluated in light of the broadest reasonable interpretation (BRI) of the claim language.
Moreover, in pp. 23 – 26 from Remarks the Applicant fails to comply with 37 CFR 1.111(b). Because these arguments fail to particularly point out how the claimed language in the claims patentably distinguishes them from the cited references or prior art of Majumdar and/or Somech. Rather, the Applicant further cites the references without providing a reason and simply amounts to general allegations by further discussing the descriptions disclosed by the references. Thus, the Examiner believe that the steps of “extracting” team data related to past modifications made by the users of the team to the digital document in document embeddings (see ¶0045, ¶0055 – 56 and ¶0058 from Majumdar; see ¶0033 and ¶0060 from Somech), having the generated document embeddings with specific corresponding “timestamps” of the document modifications (see ¶0051; Somech) and an “identifier” of the team (see ¶0042 – 43 and ¶0062 – 64; Somech), “generating” information flow patterns based on the modification patterns detected (see ¶0080 – 82; Somech), and having the different types of information that the utilized “information flow pattern” comprised of, including text with relevant modifications, specifically directed to the teams (see ¶0095 – 96, ¶0160 – 163 and ¶0175 for data display; Somech) are reasonably taught by at least the Somech reference, as shown above. 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):
… extract…to process digital documents shared within an organization account…, team data indicating a team within the organization account that modified a digital document of the digital documents;
generate, …to analyze the team data, document embeddings indicating document modifications…;
generate, …to analyze the document embeddings to detect modification patterns, an information flow pattern between the team and an additional team, the information flow pattern comprising: …
provide, based on the text describing the modification of the digital document relevant to the team and the additional team, the text to one or more user accounts associated with the additional team within the organization account.
For claim 8:
extract, …to process content items shared within an organization account of a content management system, team data indicating a team within the organization account that modified a content item of the content items;
generate…to analyze the team data, content item data comprising a data package indicating content item modifications…;
utilize the plurality of information flow patterns to determine an information flow pattern between the team and an additional team within the organization account the information flow pattern comprising: …
provide based on the text describing the modification of the first content item relevant to the team and an additional team, the text to one or more user accounts associated with the additional 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 “extracting” team data shared within an organization to “generate…document embeddings indicating document modifications made by the team to the digital document…” and “generate…an information flow pattern between the team and an additional team…”. Because “extracting” business content items and digital documents from different teams in an organization that are used for “generating” embeddings of digital documents indicating document modifications made by a team and “generating” information flow pattern between teams to be analyzed and to detect modification patterns in the data that are further “provided” as relevant text to the users encompasses legal interactions related to handling agreements in the form of contracts (i.e. “marketing documents” and project content as shown in Fig. 6, ¶0043 and ¶0106 from Applicant disclosure) as well as commercial interactions directed to managing business relations and advertisements (i.e. offering/recommending relevant document resolutions/modifications to user(s) as a service). Similarly, these same steps also falls under the abstract idea sub-group of “managing personal behavior or relationships or interactions between people” since the step for the generation of information flow patterns further involves “utiliz[ing] the plurality of information flow patterns to determine an information flow pattern” between the team and an additional team (see claim 8) to “provide text to one or more user accounts” wherein the text describe each team modifications of the relevant content items which encompasses monitoring user social activities and follow rules or instructions when further showing/providing such document modifications to all teams in the organization for them to evaluate or modify the documents with the proposed modifications.
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 that indicate document modifications made by the team, “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” text corresponding to descriptions of each team modifications of the relevant content items (from claims 1, 8 and 15) are recited as being performed by the computer that is further “using 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. Further the steps of “extract” document embeddings indicating document modifications made by a team (see claims 1 and 15) as well as “extract” team data that include indications of modification data of content items or “digital documents” 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. 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.
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. 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 and team identifiers) as well as their identification, mapping the document embeddings in an embedding space to identify similar projects and determine the information flow pattern from different versions of a digital document as well as other type of user information, generate and providing update propagating communication including project report documents and summaries from extracted data and relevant modification portions of the document located, 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 and follow rules or instructions when further showing/providing such document modifications in text to all teams in the organization for them to evaluate or modify the documents.
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.
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 discloses a “data management component (DMC) [that] can determine and extract entities of a group of entities, and relationships between entities, in documents, tables, and databases based on analysis of the entities and information relating thereto” to “desirably determine, modify, correct, and organize data elements of documents (e.g., electronic documents), tables, and databases.” (see abstract and ¶0019; Majumdar). Thus, 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 the large language model to analyze the team data, document embeddings indicating document modifications made by the team to the digital document, … (In ¶0045; Fig. 3 (308); Fig. 8 (804): teaches that “the model component 202 also can determine and create an embedding model that can embed the respective entities 116, the respective relationships 118 between respective entities 116, and the auxiliary information relating thereto, to a desired common representation, based at least in part on the results of an analysis” of this previous information mentioned, ”, in accordance to the “document embedding” definition given in ¶0035 from Applicant disclosure. Further, the “the model component 202 can continue to train and refine (e.g., improve) the embedding model over time as additional information, including feedback information (e.g., feedback information relating to decisions regarding data modifications made by a user” or by multiple users providing “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” to make the adjustments in the “embedding model” (see ¶0055 – 56 and ¶0058), which suggests modification data made by a team is received and analyzed. Examiner notes that “group of entities 116” or “entity” refers and can “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” and a “relationship 118 can be between two or more entities 116 of or associated with the electronic documents 104, tables 106, and databases 108” (see ¶0019 and ¶0042). Refer to ¶0088 – 90 for another example of “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 ¶0115 for Fig. 8 and its step 804 for details. Finally, this prior art does not teach the corresponding timestamps and an identifier of the team claimed which are addressed below by the Somech reference.)
provide, based on the text describing the modification of the digital document relevant to the team and the additional team, the text to one or more user accounts associated with the additional 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”. Further, in ¶0067 – 68 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”.)
Majumdar teaches extracting “information relating to a group of entities, and respective relationships between respective entities of the group of entities, can be extracted from electronic documents” based on the “analysis of the electronic documents, tables, and databases, and entity-related information relating to the entities” (i.e. directed to extracting document embeddings previously claimed), via the system or “DMC, employing an information extraction model” that uses AI techniques such as “machine learning and/or neural network techniques” (see ¶0030 and ¶0048; Majumdar), in accordance to the “extracting” definition given in ¶0019 and the extraction with the use of a large language model (LLM) example given in ¶0055 from Applicant disclosure. Further, Majumdar’s system can “post-process” and “embed” this information to create an “embedding model” and “can continue to train and refine (e.g., improve) the embedding model over time as additional information, including feedback information (e.g., feedback information relating to decisions regarding data modifications made by a user” or by multiple users which suggests team data is at least received/analyzed (see ¶0055 – 56 and ¶0058; Majumdar). Finally, Majumdar teach the generated text describing relevant modification to a digital document, as the “alert component 218 also can generate alert or notification messages relating to incorrect data modifications, false positives relating to data modification, and/or other anomalies relating to information stored in tables or databases, and can communicate the alert or notification messages to one or more users” as well as to generate “a particular kind of alert (e.g., a “super alert”) that can provide information (e.g., aggregated information) regarding the determinations or inferences relating to patterns and/or other information relating to the quality issue associated with the data stream” to a user (see ¶0069 – 70; Majumdar), although text with relevant modifications, specifically to the teams are not disclosed. However, Majumdar does not explicitly teach the abilities of having document embeddings extracted that specifically include the extraction of the team data related to past modifications made by the users of the team to the digital document, having the generated document embeddings with specific corresponding timestamps of the document modifications and an identifier of the team, generate information flow patterns based on the modification patterns detected and the different types of information that the information flow pattern comprised of, including text with relevant modifications, specifically directed to the teams. However, Somech which discloses “systems and methods for associating meetings with projects” wherein “projects are maintained in association with a user” and “a set of keywords that characterize the project are maintained” (see ¶0015 and ¶0033; Somech), thus teaches:
extract, using a large language model to process digital documents shared within an organization account of a content management system, team data indicating a team within the organization account that modified a digital document of the digital documents; (In ¶0033; Fig. 2 (256, 236 and 284); Fig. 5 (506): teaches that the “User activity monitor 280 analyzes the user data to identify project entities, such as meetings, extract contextual features associated with user data, and extract personal features of users, such as characteristic features of users” wherein a “project entity” refers to “a data object that system 200 associates with one or more projects” such as “files, documents, emails, events, calendar events, meetings”, etc. Finally, see ¶0060 wherein the “event logic 295 might include training data used to train a neural network that is used to evaluate user data to determine when an event has occurred” and “specify types of project features or user activity, such as specific user device interaction(s), that are associated with an event, accessing a schedule or calendar, accessing materials associated with a project entity (e.g., an agenda or presentation materials in a meeting), composing or responding to a project request communication, acknowledging a notification, navigating to a website, or launching an app” which is directed to another example of extracting team data related to modifications made by a team user to a digital document using an LLM.)
generating…corresponding timestamps of the document modifications, and an identifier of the team; (In ¶0051: teaches that the system’s “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 these “project entities” 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”. Further in ¶0042 – 43, the generation of the team identifier is taught as “User activity monitor 280” can employ “project entity identifier 282” (i.e. “identifies project entities, such as meetings, based on events detected” or “otherwise based on identifying entities associated with users (i.e., using non-event based identification)”), “entity feature identifier 285” (i.e. “contextual features of particular project entities”), and personal feature identifier 286 (i.e. “identifies personal features of users”) to use this information to identify, determine, generate, collect, and/or maintain project entities, contextual features, and/or personal features that correspond to user activity associated with one or more users” that are further stored in the users accounts or profiles (see ¶0054 and ¶0062 – 64 also). 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”.)
generate, using the large language model to analyze the document embeddings to detect modification patterns, an information flow pattern between the team and an additional team, (In ¶0081 – 82; Fig. 2 (240): teaches that “inferred event pattern information may be provided to model manager 240 and/or used to generate a project or meeting pattern based prediction regarding one or more projects inferred from the project entities”, 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. Wherein the projects inferred from the project entities are derived from the system’s “pattern inference engine 230” that is “determining project or meeting patterns based on the various information determined from user activity monitor 280” such as “Contextual information” from “the user data of one or more users” as well as “similar entity features between project entities or associated personal features” (see ¶0079 – 80) wherein the “personal features could correspond to, for example, historical projects previously identified and/or analyzed with respect to pattern inference engine 230 and/or explicitly acknowledged or otherwise specified by the user” (see ¶0082) which is directed to modification patterns detected between teams. Further, in ¶0085, the “pattern inferences logic 235 may employ machine-learning mechanisms to determine feature similarity, or other statistical measures to determine the event data belonging to a set of “example user activity” that supports the determined project or meeting pattern. In some embodiments, the project or meeting pattern(s) determined by project determiner 236 may be stored as model patterns 237 and/or provided to model manager 240, which may determine one or more inferred user model(s) 252 from the pattern(s)”.)
the information flow pattern comprising: digital content passed between the team and the additional team; modifications of the digital content passed between the team and the additional team within the organization account; and (In ¶0055 – 56: teaches that “event detector 281 may monitor user data for project-related or meeting-related features or variables corresponding to user activity, such as communications received (e.g., project requests or calendar-related communications), indications of applications launched or accessed, files accessed, modified, copied, etc., websites navigated to, online content downloaded and rendered or played, user location or change of location (e.g., user is located in or has changed locations to a conference room), or similar user activities”. Further in ¶0056, the “event detector 281 use contextual information extractor 284 to extract from the user data information about events, which may include current activity, historical activity, and/or related information such as contextual information” that is “related to one or more project entities” and this “project-related activity information” can further be “used by components of user activity monitor 280, such as event detector 281, referred to herein as project features, may include information describing app usage, online activity, searches, calls, usage duration, application data (e.g., project requests, emails, messages, posts, user profile status, notifications), or nearly any other data related to a user that is detectable via one or more user devices or computing devices, including user interactions with the user device, activity related to cloud services associated with the user (e.g., calendar or scheduling services), online account activity (e.g., email and social networks), and social network activity” (see ¶0057). Finally in ¶0058, the “the extracted event information determined by event detector 281 may be provided” to the “pattern inference engine 230”.)
text describing, from the modifications made to the digital document, a modification relevant to the team and the additional team; and (In ¶0095 - 96; Fig. 2 (230, 236 and 262): teaches text describing relevant modifications between teams as the “project determiner 236” from the “pattern inference engine 230” (see ¶0082 – 83 and Fig. 2), may “identify in-common features corresponding to one or more users (e.g., the user) interacting with a project entity”, “identify in-common features corresponding to a project entity explicitly referencing one or more project entities” (i.e. “documents attached to an email, documents attached to a meeting, invitees or participants of the meeting, applications, locations, or venues mentioned in or linked from emails or meetings, and the like”; see ¶0096) and “may identify each project entity for which it can identify or infer an explicit interaction based on detected user activity, such as an event. Examples include detecting user activity corresponding to a user opening a document, attaching a document to an email, drafting an email, working in an application, and the like. Project determiner 236 can generate one or more matrices of users and/or contacts per project entity from the identified features, which can be input to document clustering algorithms, such as NMF” (see ¶0095), in accordance to the examples given for the generation of “the description-based version of the flow pattern from document embeddings in the form of text descriptions of the modifications” in ¶0060 and ¶0094 from Applicant disclosure. Further in ¶0107, the “project determiner 236 may make a preliminary analysis of project patterns and update those determinations as additional data becomes available to system 200. This additional data may be sufficient to support inferences that projects exist and/or which project entities belong to those projects” wherein such preliminary analysis of project patterns is another example of generating text describing relevant modifications between teams. Refer to ¶0160 wherein the “pattern inference engine 230 can analyze project features corresponding to project time slots and corresponding project entities” and “History determiner 262 may further surface suggested applications based on project history, such as frequently used applications detected from user activity” and may summarize “the meetings provided by meeting analyzer 290” wherein the summary include “at least some of the keywords and/or other conversational data corresponding to the awakening event (e.g., the conversational data having a threshold similarity with the project characteristics). In addition or instead, the summary could include at least some of the meeting keywords that were mapped to the project topic(s). The summary can also include 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.” (see ¶0163). See ¶0086 wherein “project determiner 236 determine a pattern for projects or meetings (e.g., project topics) where each project has corresponding historical values of tracked project or meeting features (variables) that form patterns” and these patterns are “utilized to personalize content to users based on the projects and/or meetings” wherein this personalized content is further presented to each user (see ¶0175 and Fig. 3 (310) also).)
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 having document embeddings extracted that specifically include the extraction of the team data related to past modifications made by the users of the team to the digital document, having the generated document embeddings with specific corresponding timestamps of the document modifications and an identifier of the team, generate information flow patterns based on the modification patterns detected and the different types of information that the information flow pattern comprised of, including text with relevant modifications, specifically directed to the teams, 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 further teaches:
generate, using the large language model to analyze the team data, content item data comprising a data package… (In ¶0045; Fig. 3 (308); Fig. 8 (804): teaches that “the model component 202 also can determine and create an embedding model that can embed the respective entities 116, the respective relationships 118 between respective entities 116, and the auxiliary information relating thereto, to a desired common representation, based at least in part on the results of an analysis” of this previous information mentioned, ”, in accordance to the “content item embedding” definition given in ¶0035 from Applicant disclosure. Further, the “the model component 202 can continue to train and refine (e.g., improve) the embedding model over time as additional information, including feedback information (e.g., feedback information relating to decisions regarding data modifications made by a user” or by multiple users providing “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” to make the adjustments in the “embedding model” (see ¶0055 – 56 and ¶0058), which suggests modification data made by a team is received and analyzed. Examiner notes that “group of entities 116” or “entity” refers and can “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” and a “relationship 118 can be between two or more entities 116 of or associated with the electronic documents 104, tables 106, and databases 108” (see ¶0019 and ¶0042). Refer to ¶0088 – 90 for another example of “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. Refer to ¶0094 – 96 wherein the “AI component” can make determinations for “specific context or action” from the electronic documents which “determinations can result in the construction of new events or actions from a set of observed events and/or stored event data” that can be classified as training data or “input attributes” that can be mapped by vectors via “support vector machines”. See ¶0115 for Fig. 8 and its step 804 for details. Finally, this prior art does not teach the corresponding timestamps and an identifier of the team claimed which are addressed below by the Somech reference.)
provide based on the text describing the modification of the first content item relevant to the team and an additional team, the text to one or more user accounts associated with the additional 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”. Further, in ¶0067 – 68 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”.)
Majumdar teaches extracting “information relating to a group of entities, and respective relationships between respective entities of the group of entities, can be extracted from electronic documents” based on the “analysis of the electronic documents, tables, and databases, and entity-related information relating to the entities” (i.e. directed to extracting document embeddings previously claimed), via the system or “DMC, employing an information extraction model” that uses AI techniques such as “machine learning and/or neural network techniques” (see ¶0030 and ¶0048; Majumdar), in accordance to the “extracting” definition given in ¶0019 and the extraction with the use of a large language model (LLM) example given in ¶0055 from Applicant disclosure. Further, Majumdar’s system can “post-process” and “embed” this information to create an “embedding model” and “can continue to train and refine (e.g., improve) the embedding model over time as additional information, including feedback information (e.g., feedback information relating to decisions regarding data modifications made by a user” or by multiple users which suggests team data is at least received/analyzed (see ¶0055 – 56 and ¶0058; Majumdar). Finally, Majumdar teach the generated text describing relevant modification to a digital document, as the “alert component 218 also can generate alert or notification messages relating to incorrect data modifications, false positives relating to data modification, and/or other anomalies relating to information stored in tables or databases, and can communicate the alert or notification messages to one or more users” as well as to generate “a particular kind of alert (e.g., a “super alert”) that can provide information (e.g., aggregated information) regarding the determinations or inferences relating to patterns and/or other information relating to the quality issue associated with the data stream” to a user (see ¶0069 – 70; Majumdar), although text with relevant modifications, specifically to the teams are not disclosed. However, Majumdar does not explicitly teach the abilities of having document embeddings extracted that specifically include the extraction of the team data related to past modifications made by the users of the team to the digital document, having the generated content item data or data package indicating specific corresponding timestamps of the document modifications and an identifier of the team, generate information flow patterns based on the modification patterns detected and the different types of information that the utilized information flow pattern(s) comprised of, including text with relevant modifications, specifically directed to the teams. However, Somech further teaches:
extract, using a large language model to process content items shared within an organization account of a content management system, team data indicating a team within the organization account that modified a content item of the content items; (In ¶0033; Fig. 2 (256, 236 and 284); Fig. 5 (506): teaches that the “User activity monitor 280 analyzes the user data to identify project entities, such as meetings, extract contextual features associated with user data, and extract personal features of users, such as characteristic features of users” wherein a “project entity” refers to “a data object that system 200 associates with one or more projects” such as “files, documents, emails, events, calendar events, meetings”, etc. Finally, see ¶0060 wherein the “event logic 295 might include training data used to train a neural network that is used to evaluate user data to determine when an event has occurred” and “specify types of project features or user activity, such as specific user device interaction(s), that are associated with an event, accessing a schedule or calendar, accessing materials associated with a project entity (e.g., an agenda or presentation materials in a meeting), composing or responding to a project request communication, acknowledging a notification, navigating to a website, or launching an app” which is directed to another example of extracting team data related to modifications made by a team user to a digital document using an LLM.)
… content item data comprising a data package indicating content item modifications made by the team to the content item, corresponding timestamps of the content item modifications, and an identifier of the team; (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 these “project entities” 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”. Further in ¶0042 – 43, the generation of the team identifier is taught as “User activity monitor 280” can employ “project entity identifier 282” (i.e. “identifies project entities, such as meetings, based on events detected” or “otherwise based on identifying entities associated with users (i.e., using non-event based identification)”), “entity feature identifier 285” (i.e. “contextual features of particular project entities”), and personal feature identifier 286 (i.e. “identifies personal features of users”) to use this information to identify, determine, generate, collect, and/or maintain project entities, contextual features, and/or personal features that correspond to user activity associated with one or more users” that are further stored in the users accounts or profiles (see ¶0054 and ¶0062 – 64 also). 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”.)
generate, using the large language model to analyze the content item data to detect modification patterns, a plurality of information flow patterns corresponding to the content items; (In ¶0081 – 82; Fig. 2 (240): teaches that “inferred event pattern information may be provided to model manager 240 and/or used to generate a project or meeting pattern based prediction regarding one or more projects inferred from the project entities”, 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. Wherein the projects inferred from the project entities are derived from the system’s “pattern inference engine 230” that is “determining project or meeting patterns based on the various information determined from user activity monitor 280” such as “Contextual information” from “the user data of one or more users” as well as “similar entity features between project entities or associated personal features” (see ¶0079 – 80) wherein the “personal features could correspond to, for example, historical projects previously identified and/or analyzed with respect to pattern inference engine 230 and/or explicitly acknowledged or otherwise specified by the user” (see ¶0082) which is directed to modification patterns detected. Further, in ¶0085, the “pattern inferences logic 235 may employ machine-learning mechanisms to determine feature similarity, or other statistical measures to determine the event data belonging to a set of “example user activity” that supports the determined project or meeting pattern. In some embodiments, the project or meeting pattern(s) determined by project determiner 236 may be stored as model patterns 237 and/or provided to model manager 240, which may determine one or more inferred user model(s) 252 from the pattern(s)”.)
utilize the plurality of information flow patterns to determine an information flow pattern between the team and an additional team within the organization account the information flow pattern comprising: (In ¶0080; Fig. 2 (230): teaches that the “pattern inference engine 230” may apply “inference algorithms” to “the entity information to determine project and/or meeting patterns from the project entities identified using project entity identifier 282. For example, patterns may be determined based on similar entity features between project entities or associated personal features, which may be referred to as “in-common features” of entity-related information.” Wherein such “project entities” refers “to a data object that system 200 associates with one or more projects” such as “files, documents, emails, events”, “word processing documents” as well as “contacts, users” which is directed to the team and an additional team (see ¶0033).)
content passed between the team and an additional team; modifications of the content passed between the team and the additional team within the organization account; and (In ¶0055 – 56: teaches that “event detector 281 may monitor user data for project-related or meeting-related features or variables corresponding to user activity, such as communications received (e.g., project requests or calendar-related communications), indications of applications launched or accessed, files accessed, modified, copied, etc., websites navigated to, online content downloaded and rendered or played, user location or change of location (e.g., user is located in or has changed locations to a conference room), or similar user activities”. Further in ¶0056, the “event detector 281 use contextual information extractor 284 to extract from the user data information about events, which may include current activity, historical activity, and/or related information such as contextual information” that is “related to one or more project entities” and this “project-related activity information” can further be “used by components of user activity monitor 280, such as event detector 281, referred to herein as project features, may include information describing app usage, online activity, searches, calls, usage duration, application data (e.g., project requests, emails, messages, posts, user profile status, notifications), or nearly any other data related to a user that is detectable via one or more user devices or computing devices, including user interactions with the user device, activity related to cloud services associated with the user (e.g., calendar or scheduling services), online account activity (e.g., email and social networks), and social network activity” (see ¶0057). Finally, in ¶0058, the “the extracted event information determined by event detector 281 may be provided” to the “pattern inference engine 230”.)
text describing, from the modifications made to the content item, a modification relevant to the team and the additional team; and (In ¶0095 - 96; Fig. 2 (230, 236 and 262): teaches text describing relevant modifications between teams as the “project determiner 236” from the “pattern inference engine 230” (see ¶0082 – 83 and Fig. 2), may “identify in-common features corresponding to one or more users (e.g., the user) interacting with a project entity”, “identify in-common features corresponding to a project entity explicitly referencing one or more project entities” (i.e. “documents attached to an email, documents attached to a meeting, invitees or participants of the meeting, applications, locations, or venues mentioned in or linked from emails or meetings, and the like”; see ¶0096) and “may identify each project entity for which it can identify or infer an explicit interaction based on detected user activity, such as an event. Examples include detecting user activity corresponding to a user opening a document, attaching a document to an email, drafting an email, working in an application, and the like. Project determiner 236 can generate one or more matrices of users and/or contacts per project entity from the identified features, which can be input to document clustering algorithms, such as NMF” (see ¶0095), in accordance to the examples given for the generation of “the description-based version of the flow pattern from document embeddings in the form of text descriptions of the modifications” in ¶0060 and ¶0094 from Applicant disclosure. Further in ¶0107, the “project determiner 236 may make a preliminary analysis of project patterns and update those determinations as additional data becomes available to system 200. This additional data may be sufficient to support inferences that projects exist and/or which project entities belong to those projects” wherein such preliminary analysis of project patterns is another example of generating text describing relevant modifications between teams. Refer to ¶0160 wherein the “pattern inference engine 230 can analyze project features corresponding to project time slots and corresponding project entities” and “History determiner 262 may further surface suggested applications based on project history, such as frequently used applications detected from user activity” and may summarize “the meetings provided by meeting analyzer 290” wherein the summary include “at least some of the keywords and/or other conversational data corresponding to the awakening event (e.g., the conversational data having a threshold similarity with the project characteristics). In addition or instead, the summary could include at least some of the meeting keywords that were mapped to the project topic(s). The summary can also include 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.” (see ¶0163). See ¶0086 wherein “project determiner 236 determine a pattern for projects or meetings (e.g., project topics) where each project has corresponding historical values of tracked project or meeting features (variables) that form patterns” and these patterns are “utilized to personalize content to users based on the projects and/or meetings” wherein this personalized content is further presented to each user (see ¶0175 and Fig. 3 (310) also).)
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 having document embeddings extracted that specifically include the extraction of the team data related to past modifications made by the users of the team to the digital document, having the generated content item data or data package indicating specific corresponding timestamps of the document modifications and an identifier of the team, generate information flow patterns based on the modification patterns detected and the different types of information that the utilized information flow pattern(s) comprised of, including text with relevant modifications, specifically directed to the teams, 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 text] by extracting data relevant to the modification from the [digital document] content item and relevant to the additional team; and provide the update propagation communication to a user account associated with the [digital document] 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 additional 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 additional 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 :
The combination of Majumdar and Somech, as shown in the rejection above, discloses the limitations of claim 1.
Majumdar further teaches:
wherein providing the text further comprises: identifying, based on the information flow pattern, a plurality of versions of the digital document; determining, based on the information flow pattern, a newest version from the plurality of versions of the digital document; (In ¶0044 – 45 ¶0056 – 57 ¶0080 ¶0086 ¶0112 ¶0161 ¶0163; Fig. 2 (214) and Fig. 3 (318): teaches that the “DMC” system “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 (i.e. storing “previous version of the new entity, for reference, in a version control repository in a data store” as well as “information relating to the modification of the new entity” and/or “information relating to the updated embedding model in the version control repository”; see ¶0027) to further “enable the DMC 102 or user 122 to access the previous versions of such changes, if and as desired, for example, for review or evaluation, or to facilitate determining a data modification to make with regard to a current instance of an entity” which is directed to determining the newest version of the digital document.)
Majumdar 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” at step 804 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” (i.e. directed to generating information flow patterns), (see ¶0115 – 116; Majumdar). Further, 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 ¶0023 and ¶0069; Majumdar) that not necessarily or explicitly disclose reflecting the last modification to the users. Although Majumdar system can update its database to reflect “new data, such as the data modification made to the information of or associated with the entity 116 (e.g., new entity) and/or other information relating to the data modification” (see ¶0119; Majumdar). But Majumdar does not explicitly teach the abilities of locating a portion relevant to the additional team within the newest version of the document, extracting data relevant to the portion being modified, generating and providing an updated propagation communication to the additional team and its associated user accounts based on relevant portion modifications. However, Somech further teaches:
locating, within the newest version of the digital document, a portion relevant to the additional team; (In ¶0087; Fig. 2 (236): teaches “the project features are based on project entities previously associated with the project by project determiner 236. In one instance, a tracked variable is associated with a time slot corresponding to an observed instance of the project. Optionally, the tracked variable could further be based on entity features of one or more entities within the time slot. It will be appreciated that, conceptually, many different types of historical values for tracked project features (variables) may be used” to “determine patterns for projects based on any number of features” which are directed to locating relevant portions of a digital document for an additional team. Further, see ¶0098 for an example of locating relevant portions in a recent digital document that is relevant to a team wherein “Project determiner 236 may also identify in-common place-related features for the clustering algorithms. For example, each project entity may be associated with one or more venues, locations, and/or GPS coordinates. The similarity between place-related features may be based on location proximity of the place-related features. This may be based on the notion that at least some project entities are more likely to belong to the same project when they are locationally similar. For instance, people may work on projects in particular locations. Thus, documents associated with the office are more likely to be related to the same project.”)
extracting data relevant to the modification from the portion relevant to the additional team; (In ¶0088 – 89; Fig. 2 (236): teaches that the “project determiner 236 comprises a multistage classifier in which a project entity preclassifier classifies project entities by project topic” and “a project entity reclassifier reclassifies the project entities using the project topics by the grouped time slots” that “may extract project keywords from project entities and group project entities into project topics corresponding to the keywords” wherein the “keywords can be extracted from emails (e.g., subject line, message body, attachments, and the like), documents accessed during or in association with meetings, meeting invites or other planning or scheduling messages, and the like“ which is directed to data relevant to the modification from the portion relevant to the additional team. Further, “it is noted that any suitable keyword extraction techniques may be employed”. See ¶0031 and ¶0033 for general details of “using project characteristics” (i.e. “project keywords, which when detected in a conversation indicate the conversation corresponds to a meeting that may be related to a project”), “formed by patterns extracted from project data, and determined from project models”.)
generating, using the large language model, an updated propagation communication comprising the data relevant to the modification from the portion relevant to the additional team; (In ¶0086; Fig. 3 (310): teaches ¶0086 wherein “project determiner 236 determine a pattern for projects or meetings (e.g., project topics) where each project has corresponding historical values of tracked project or meeting features (variables) that form patterns” and these patterns are “utilized to personalize content to users based on the projects and/or meetings” wherein this personalized content is further presented to each user (see ¶0175 and Fig. 3 (310) also).)
and providing the updated propagation communication to the one or more user accounts associated with the additional team (In ¶0175 – 177; Fig. 3 (310): teaches “interface manager 260 can personalize content to the user based on the assignment of the meeting to the project. The personalized content can be presented on a user device using presentation component 220.” Wherein examples of “personalized content”, such as “any of the various content provided by presentation component 220 (e.g., information populating a user interface) which in addition to what has been described above, can include values for fields in a template, such as a content card, an email, a push notification, a text message, or other message or visualization, which may be provided to the user. This could include suggestions, recommendations, and the like that are generated based on the user data” (see ¶0177), in accordance to the example given for the “update propagation communication” (i.e. notification of “changes to a digital content”) in ¶0044 from Applicant disclosure. Further in ¶0169, “information from project or meeting models may be displayed” and a “user may be able to access aspects of and view his or her project or meeting models” to “see patterns and trends that may be reflected in their models” as well as these models can be the “models of other user profiles and/or aggregated models” that can be “generated for display”. See ¶0170, for another example wherein “history determiner 262 may identify a new or recent participant to join a project”, and upon “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. This could include automatically sharing the data with the user and/or providing one or more links to the information.”)
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 locating a portion relevant to the additional team within the newest version of the document, extracting data relevant to the portion being modified, generating and providing an updated propagation communication to the additional team and its associated user accounts based on relevant portion 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 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 additional 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 additional 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 content item, identify a related content item corresponding to the modification of the content item and the additional 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 and the additional team. 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 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 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 additional 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 an additional 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 text; 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 team and the one or more user accounts associated with the additional 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 an additional 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 digital 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 digital document relevant to the additional team to extract data relevant to the modification from the digital 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 digital document.)
determine contact information for the one or more user accounts associated with the additional 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 additional 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 text and the contact information for the one or more user accounts associated with the additional 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 digital document, identify a related document corresponding to the modification of the digital document and the additional 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 and the additional team. 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 digital document with the related portion of the related document to determine that the modification of the digital 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 additional 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)”.)
provide a notification to the one or more user accounts associated with the additional 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 20:
The combination of Majumdar and Somech, as shown in the rejection above, discloses the limitations of claim 15.
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). Further, Majumdar teach the generated text describing relevant modification to a digital document, as the “alert component 218 also can generate alert or notification messages relating to incorrect data modifications, false positives relating to data modification, and/or other anomalies relating to information stored in tables or databases, and can communicate the alert or notification messages to one or more users” as well as to generate “a particular kind of alert (e.g., a “super alert”) that can provide information (e.g., aggregated information) regarding the determinations or inferences relating to patterns and/or other information relating to the quality issue associated with the data stream” to a user (see ¶0069 – 70; Majumdar), although text with relevant modifications, specifically to the teams are not disclosed. But, Majumdar does not explicitly teach the abilities of generating a project report document from the digital document including text relevant to the additional team to provide its digital access to the additional team and their user accounts. However, Somech teaches:
further comprising instructions that, when executed by the at least one processor, cause the system to: generating a project report document comprising information from the digital document relevant to the additional team and including the text; 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.” See ¶0160 wherein the pattern inference engine 230 can analyze project features corresponding to project time slots and corresponding project entities” and “History determiner 262 may further surface suggested applications based on project history, such as frequently used applications detected from user activity”.)
providing digital access to the project report document to one or more user accounts associated with the team and the one or more user accounts associated with the additional 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 from the digital document including text relevant to the additional team to provide its digital access to the additional 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).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Reddypalli (U.S. Pub No. 20240160415 A1) is pertinent because it “relates to intelligently and dynamically updating membership of one or more group objects based at least in part on a change to an attribute value with respect to an entity associated with the one or more group objects” and “further include employing the dependency mapping file to identify the one or more second group objects based at least in part on detection of a change to the attribute value with respect to an entity associated with the first group object and the one or more second group objects.”
Sharma (U.S. Patent No. 10832187 B2) is pertinent because it is directed to a system that “receive project information regarding a development project. The development project may be associated with one or more agile methodologies. The one or more instructions, when executed by the one or more processors, may cause the one or more processors to generate an agile development user interface that provides information regarding the development project. The information may include collaboration information associated with facilitating message exchange between a plurality of users of the agile development user interface, metrics information regarding one or more metrics associated with the development project, or alert information regarding one or more updates to the development project.”
Choudhary (CA Pub No. 20210224306 A1) is pertinent because it “relate to Artificial Intelligence Tools for recommending editors for structured text documents.”
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.”
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Ivonnemary Rivera Gonzalez whose telephone number is (571)272-6158. The examiner can normally be reached Mon - Fri 9:00AM - 5:30PM.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Jessica Lemieux can be reached at (571) 270-3445. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/IVONNEMARY RIVERA GONZALEZ/Examiner, Art Unit 3626 /DENNIS W RUHL/Primary Examiner, Art Unit 3626
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