Detailed Action
The office action is in response to the communication dated on 05/04/2026.
Claims 1, 2, 11, and 20 are currently amended.
Claims 1-20 are pending.
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 .
Response to Amendment
Examiner has fully considered Applicant’s amendments to the Claims in the arguments filed on 05/04/2026. Claims 1-20 remain pending in the application.
Drawing
The correction to the drawings, submitted on May 04, 2026, is entered as it is found acceptable for the examination purpose.
Specification
The correction the specification, submitted on May 04, 2026, is entered as it is found acceptable for the examination purpose.
Response to Arguments
Applicant’s arguments filed 05/04/2026, with respect to the objections to the specification, drawings, and claim 2 have been fully considered and are found persuasive. Therefore, the previous claim objection is now withdrawn. Additionally, with respect to the prior-art rejection of independent claims 1, 11, and 20 and the corresponding dependent claims under 35 USC 103 have been fully considered and are found persuasive. Therefore, the previous ground of rejection under 35 USC 103 has been withdrawn. However, upon further consideration, a new ground of rejection is made in view of newly applied references by Karlberg (US 20210288971 A1) and Sundradhas (US 20260057090 A1). Specifically, the combination of Karlberg and Sundradhas teaches the newly added limitation “wherein the one or more ideas represent one or more semantic concepts conveyed by content of the particular document”.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1, 3, 4, 7, 8, 10, 11, 13, 14, 17, 18, and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Karlberg (US 20210288971 A1) in view of Sundradhas (US 20260057090 A1).
Regarding Claim 1:
Karlberg teaches a method, comprising:
… wherein the one or more ideas represent one or more semantic concepts conveyed by content of the particular document (Karlberg – Paragraph [0030]: The term “concept” as described herein may comprise a topic, category, or knowledge representation, which may include or may be associated with one or more computer resources, instances, sub-categories, or sub-topics that describe the concept. A “computer resource” or “resource” as described herein refers to any data source, set of computer instructions (e.g., an app or web application), and/or any suitable data set that is accessible by a computing device. For example, a first resource can be a document … In an example of how concepts and resources are related, a user may issue a query that includes a request to obtain all information for the concept of “John Doe.” There may be multiple resources or instances that need to be accessed to execute this query for John Doe such as documents, inter/intranet sites, user profile services, chat services, email threads, location services, and the like; Paragraph [0058]: the relationship type is a “related document” identifier, which indicates that there is a related document to a concept (or other resource) for a given resource. The related document reason indicator may specifically describe why a resource is related to a given concept (or other resources for the same concept). For example, reasons can be that the document contains a particular paragraph, sentence, phrase, acronym, and/or the like that matches one or more terms of a concept (or has the same semantic meaning as the concept));
determining, by the device, a measure of similarity between the one or more ideas from the particular document and those of each of a body of existing documents, to identify a set of one or more similar documents (Karlberg – Paragraph [0049]: Operating environment 100 can be utilized to implement one or more of the components of the systems 200, 300, and/or 400 as described with respect to FIG. 2, FIG. 3, and FIG. 4 respectively; Paragraph [0055]: The concept-resource mapper 216 is generally responsible for associating or mapping each concept (e.g., of a predefined set of concepts) to one or more resources or vice versa. For example, some embodiments leverage manual annotations (e.g., user requests), link analysis, statistical, and/or machine level models to make such association. For example, word embedding vector models such as WORD2VEC can be used to map a set of resources that are within a threshold distance of each other to their nearest categorical or semantic term in vector space, which would represent a concept; Paragraph [0058]: The related document reason indicator may specifically describe why a resource is related to a given concept (or other resources for the same concept). For example, reasons can be that the document contains a particular paragraph, sentence, phrase, acronym, and/or the like that matches one or more terms of a concept (or has the same semantic meaning as the concept));
generating, by the device, an access control list for the particular document, based on one or more access control lists associated with the set of one or more similar documents (Karlberg – Paragraph [0136]: the concept is associated with a plurality of resources related to the concept. For example, the concept-resource mapper 216 can map a concept to a plurality of resources, as described with respect to FIG. 2 … each resource of the plurality of resources is also associated with a respective ACL needed to access the resource. For example, the concept-resource mapper 316 as described with respect to FIG. 3 can determine that each resource of the plurality of resources requires user access credentials to include one or more access control levels needed to access the corresponding resource. For instance, a first resource may require a first ACL, which consists of access levels A, B, and C to access its contents, whereas a second resource may require a second ACL, which consists of access levels C, D, and E to access its contents; Paragraph [0138]: generate an aggregated ACL data structure mapping such that the generating of the aggregated ACL data structure mapping aggregates a plurality of different ACLs associated with different computer resources of a first concept into a single record; Paragraph [0049]: Operating environment 100 can be utilized to implement one or more of the components of the systems 200, 300, and/or 400 as described with respect to FIG. 2, FIG. 3, and FIG. 4 respectively); and
restricting, by the device, access to the particular document according to the access control list for the particular document (Karlberg - Paragraph [0032]: when a user requests access for a concept, some embodiments efficiently return only resources that the user has access to by comparing the user's access credentials (also referred to herein as “user claims” and “user claim set”) with the one or more access control level identifiers. Paragraph [0138]: generate an aggregated ACL data structure mapping such that the generating of the aggregated ACL data structure mapping aggregates a plurality of different ACLs associated with different computer resources of a first concept into a single record).
Karlberg does not teach extracting, by a device and using an embedding model, one or more ideas from a particular document, [wherein the one or more ideas represent one or more semantic concepts conveyed by content of the particular document].
However, Sundradhas teaches extracting, by a device and using an embedding model, one or more ideas from a particular document, wherein the one or more ideas represent one or more semantic concepts conveyed by content of the particular document (Sundradhas – Paragraph [0076]: the AI model 262, the AI model 264, the AI model 266 can include various engines/classifiers and/or can be implemented on a neural network. The features of the engines/classifiers can be implemented by configuring and arranging the computer system 202 to execute machine learning algorithms. In general, machine learning algorithms, in effect, extract features from received data (e.g., the complete message formed of segmented messages) in order to “classify” the received data; Paragraph [0041]: The AI model 262 is a machine learning model trained on training data 263 to predict, label, and/or classify segments in documents … The training data 263 include feature vectors (i.e., features) utilized to train the AI model 262 … Example features utilized to train the AI model 262 may include keyword frequency, header detection, font styles, semantic analysis, named entity recognition (NER), and metadata such as section titles and document metadata. These features enable the AI model 262 to discern and categorize different sections within a document effectively; Paragraph [0046]: The AI model 264 is a machine learning model trained on training data 265 to generate tags for input segments in which the tags uniquely number and name segments in documents … The training data 265 include feature vectors (i.e., features) utilized to train the AI model 264 … Example features utilized to train the AI model 264 may include text patterns, contextual keywords, structural elements (e.g., headers, footers), font styles and sizes, section breaks, and semantic context. These features enable the AI model 264 to accurately tag and uniquely identify different segments within a document);
It would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to modify Karlberg, further incorporating Sundradhas to arrive at the claimed invention. One would be motivated to incorporate Sundradhas’s teachings into Karlberg’s invention to provide granular level document security with role-based access using an artificial intelligence (AI) workflow to protect sensitive data from unauthorized personnel; see Sundradhas Paragraphs [0001-0002].
Regarding Claim 3:
The combination of Karlberg and Sundradhas teaches the method of claim 1.
Sundradhas further teaches wherein the device extracts the one or more ideas from the particular document by using the embedding model … (Sundradhas – Paragraph [0076]: the AI model 262, the AI model 264, the AI model 266 can include various engines/classifiers and/or can be implemented on a neural network. The features of the engines/classifiers can be implemented by configuring and arranging the computer system 202 to execute machine learning algorithms. In general, machine learning algorithms, in effect, extract features from received data (e.g., the complete message formed of segmented messages) in order to “classify” the received data; Paragraph [0046]: The AI model 264 is a machine learning model trained on training data 265 to generate tags for input segments in which the tags uniquely number and name segments in documents … The training data 265 include feature vectors (i.e., features) utilized to train the AI model 264 … Example features utilized to train the AI model 264 may include text patterns, contextual keywords, structural elements (e.g., headers, footers), font styles and sizes, section breaks, and semantic context. These features enable the AI model 264 to accurately tag and uniquely identify different segments within a document).
Karlberg further teaches … using the embedding model to generate vector embeddings that represent the one or more ideas present in the particular document (Karlberg – Paragraph [0055]: The concept-resource mapper 216 is generally responsible for associating or mapping each concept (e.g., of a predefined set of concepts) to one or more resources or vice versa. For example, some embodiments leverage manual annotations (e.g., user requests), link analysis, statistical, and/or machine level models to make such association. For example, word embedding vector models such as WORD2VEC can be used to map a set of resources that are within a threshold distance of each other to their nearest categorical or semantic term in vector space, which would represent a concept).
The motivation to combine the arts is the same as that of Claim 1.
Regarding Claim 4:
The combination of Karlberg and Sundradhas teaches the method of claim 1.
Karlberg further teaches wherein the device generates the access control list for the particular document by aggregating the one or more access control lists associated with the set of one or more similar documents (Karlberg – Paragraph [0136]: the concept is associated with a plurality of resources related to the concept. For example, the concept-resource mapper 216 can map a concept to a plurality of resources, as described with respect to FIG. 2 … each resource of the plurality of resources is also associated with a respective ACL needed to access the resource. For example, the concept-resource mapper 316 as described with respect to FIG. 3 can determine that each resource of the plurality of resources requires user access credentials to include one or more access control levels needed to access the corresponding resource. For instance, a first resource may require a first ACL, which consists of access levels A, B, and C to access its contents, whereas a second resource may require a second ACL, which consists of access levels C, D, and E to access its contents; Paragraph [0138]: generate an aggregated ACL data structure mapping such that the generating of the aggregated ACL data structure mapping aggregates a plurality of different ACLs associated with different computer resources of a first concept into a single record).
The motivation to combine the arts is the same as that of Claim 1.
Regarding Claim 7:
The combination of Karlberg and Sundradhas teaches the method of claim 1.
Karlberg further teaches wherein the access control list restricts access to the particular document to at least one of: a set of one or more authorized users, a set of one or more authorized groups, or a set of one or more authorized locations (Karlberg – Paragraph [0032]: An ACL indicates one or more particular access control levels needed to access a corresponding resource. Each access control level can specify an individual (e.g., a name of a person), user (e.g., a particular service or end user), or security groups whose members have access to the resources, required labels, information barriers, and the like).
The motivation to combine the arts is the same as that of Claim 1.
Regarding Claim 8:
The combination of Karlberg and Sundradhas teaches the method of claim 1.
Karlberg further teaches wherein the particular document is a file (Karlberg – Paragraph [0005]: Particular aspects of this disclosure relate to computerized systems for using improved data structures and functionality to efficiently render different multiple access-controlled resources (e.g., files or applications) that are part of a concept (e.g., a topic or knowledge representation of information)).
The motivation to combine the arts is the same as that of Claim 1.
Regarding Claim 10:
The combination of Karlberg and Sundradhas teaches the method of claim 1.
Karlberg further teaches wherein the particular document is an email (Karlberg – Paragraph [0030]: There may be multiple resources or instances that need to be accessed to execute this query for John Doe such as documents, inter/intranet sites, user profile services, chat services, email threads, location services, and the like).
The motivation to combine the arts is the same as that of Claim 1.
Regarding Claim 11:
Claim 11 is an apparatus that recites features that are similar to those of the method claim 1.
Therefore, claim 11 is rejected with the same rationale and motivation as applied against claim 1 above.
In addition, Karlberg teaches an apparatus, comprising: one or more network interfaces; a processor coupled to the one or more network interfaces and configured to execute one or more processes; and a memory configured to store a process that is executable by the processor, the process when executed configured to: (Karlberg – Paragraph [Claim 1]: A computerized system comprising: one or more processors; and computer storage memory having computer-executable instructions stored thereon which, when executed by the one or more processors, implement a method comprising; Paragraph [0044]: Each of the components shown in FIG. 1 may be implemented via any type of computing device, such as computing device 1900 described in connection to FIG. 19, for example. These components may communicate with each other via network 110, which may include, without limitation, one or more local area networks (LANs) and/or wide area networks (WANs); Paragraph [0067]: Turning now to FIG. 3, example system 300 includes network 110, which is described in connection to FIG. 1, … These components may be embodied as a set of compiled, interpreted, or executable computer instructions or functions, program modules, computer software services, or an arrangement of processes carried out on one or more computer systems, such as computing device 1900 described in connection to FIG. 19, for example. In some embodiments, the system 300 is a part of or included in the system 200 of FIG. 2).
Regarding Claim 13:
Rejection of claim 11 is incorporated. In addition, claim 13 recites features that similar to those
of claim 3. Therefore, claim 13 is rejected with the same rationale and motivation as applied against
claim 3 above.
Regarding Claim 14:
Rejection of claim 11 is incorporated. In addition, claim 14 recites features that similar to those
of claim 4. Therefore, claim 14 is rejected with the same rationale and motivation as applied against
claim 4 above.
Regarding Claim 17:
Rejection of claim 11 is incorporated. In addition, claim 17 recites features that similar to those
of claim 7. Therefore, claim 17 is rejected with the same rationale and motivation as applied against
claim 7 above.
Regarding Claim 18:
Rejection of claim 11 is incorporated. In addition, claim 18 recites features that similar to those
of claim 8. Therefore, claim 18 is rejected with the same rationale and motivation as applied against
claim 8 above.
Regarding Claim 20:
Claim 20 is a tangible, non-transitory, computer-readable medium that recites features that are similar to those of the method claim 1. Therefore, claim 20 is rejected with the same rationale and motivation as applied against claim 1 above. In addition, Karlberg teaches a tangible, non-transitory, computer-readable medium storing program instructions that cause a device to execute a process comprising: (Karlberg – Paragraph [0114]: the system (that includes at least one computing device having at least one processor and at least one computer readable storage medium), and/or the computer storage media as described herein may perform or be caused to perform the processes or any other functionality described herein; Paragraph [0170]: Computer storage media includes, but is not limited to, RAM, ROM, EEPROM, flash memory or other memory technology, CD-ROM, digital versatile disks (DVDs) or other optical disk storage, magnetic cassettes, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other medium which can be used to store the desired information).
Claims 2 and 12 are rejected under 35 U.S.C. 103 as being unpatentable over Karlberg (US 20210288971 A1) in view of Sundradhas (US 20260057090 A1), and further in view of Galitsky (US 11100144 B2).
Regarding Claim 2:
The combination of Karlberg and Sundradhas teaches the method of claim 1.
The combination of Karlberg and Sundradhas does not teach wherein said restricting access to the particular document comprises: preventing, by the device, the particular document from being transmitted across a computer network.
However, Galitsky teaches wherein said restricting access to the particular document comprises: preventing, by the device, the particular document from being transmitted across a computer network (Galitsky - [Claim 7, lines 2-4]: document is classified as private, [and] preventing a transmission of the document over a data network; [Column 31, lines 45-51]: FIG. 19 depicts a simplified diagram of a distributed system 1900…[that] includes one or more client computing devices 1902, 1904, 1906, and 1908, which are configured to execute and operate a client application such as a web browser, proprietary client (e.g., Oracle Forms), or the like over one or more network(s) 1910).
It would have been obvious, before the effective filing date of the claimed invention, to a person having ordinary skill in the art to modify the combination of Karlberg and Sundradhas, further incorporating Galitsky to arrive at the conclusion of the claimed method. One would be motivated to incorporate Galitsky’s teaching into the combination of Karlberg and Sundradhas’s method to provide another layer of security by preventing data breaches when a system is handling sensitive information; see Galitsky [Col. 1, Lines 26-33].
Regarding Claim 12:
Rejection of claim 11 is incorporated. In addition, claim 12 recites features that similar to those
of claim 2. Therefore, claim 12 is rejected with the same rationale and motivation as applied against
claim 2 above.
Claims 5, 6, 15, and 16 are rejected under 35 U.S.C. 103 as being unpatentable over Karlberg (US 20210288971 A1) in view of Sundradhas (US 20260057090 A1), and further in view of Lu (US 20250139580 A1).
Regarding Claim 5:
The combination of Karlberg and Sundradhas teaches the method of claim 1.
The combination of Karlberg and Sundradhas does not teach wherein the particular document comprises an input prompt for a large language model (LLM).
However, Lu teaches wherein the particular document comprises an input prompt for a large language model (LLM) (Lu - [Claim 1, lines 1-12]: computer-implemented method for saving
prompts on a page of a workspace, the method comprising: receiving a first input that
instantiates a prompt block configured to initiate a generative process to create in-block
content, wherein the prompt block is embedded on a first page of a workspace configured to
include multiple pages, wherein the first input includes a string of text, and wherein the string of
text includes instructions that cause a generative artificial intelligence (AI) system to create the
in-block content to be displayed on the page; Paragraph [0002, lines 6-8]: workspaces can
include compilations of electronic documents that can be organized within the workspace; and
Paragraph [0020, lines 5-8]: the content can be generated by an artificial intelligence (AI) system
(e.g., a generative AI system including a large language model (LLM))).
It would have been obvious, before the effective filing date of the claimed invention, to
a person having ordinary skill in the art to modify the combination of Karlberg and Sundradhas, further incorporating Lu to arrive at the conclusion of the claimed method. One would be motivated to incorporate Lu’s teaching into the combination of Karlberg and Sundradhas’s method to provide an easier and efficient manner for a generative AI system to analyze contents from similar documents while securing it through access control polices; see Lu Paragraph [0020-0021].
Regarding Claim 6:
The combination of Karlberg and Sundradhas teaches the method of claim 1.
The combination of Karlberg and Sundradhas does not teach wherein the particular document comprises an answer generated by a large language model (LLM).
However, Lu teaches wherein the particular document comprises an answer generated by a large language model (LLM) (Lu - [Claim 1, lines 8-12]: the first input includes a string of text, and wherein the string of text includes instructions that cause a generative artificial intelligence
(AI) system to create the in-block content to be displayed on the page; and Paragraph [0002,
lines 6-8]: workspaces can include compilations of electronic documents that can be organized
within the workspace; and Paragraph [0020, lines 5-8]: the content can be generated by an
artificial intelligence (AI) system (e.g., a generative AI system including a large language model
(LLM))).
It would have been obvious, before the effective filing date of the claimed invention, to a person
having ordinary skill in the art to modify the combination of Karlberg and Sundradhas, further incorporating Lu to arrive at the conclusion of the claimed method. One would be motivated to incorporate Lu’s teaching into the combination of Karlberg and Sundradhas’s method in order to generate content based on document contents and display the generated content within the same document; Lu Paragraph [0021, Lines 1-15]. This addition allows the document to serve as an interface with an LLM while integrating Sundradhas’s document extraction method and Karlberg’s access control methods, enabling a secure interaction with an LLM that processes sensitive, extracted information.
Regarding Claim 15:
Rejection of claim 11 is incorporated. In addition, claim 15 recites features that similar to those
of claim 5. Therefore, claim 15 is rejected with the same rationale and motivation as applied against
claim 5 above.
Regarding Claim 16:
Rejection of claim 11 is incorporated. In addition, claim 16 recites features that similar to those
of claim 6. Therefore, claim 16 is rejected with the same rationale and motivation as applied against
claim 6 above.
Claims 9 and 19 are rejected under 35 U.S.C. 103 as being unpatentable over Karlberg (US 20210288971 A1) in view of Sundradhas (US 20260057090 A1), and further in view of Gardner (US 20250061291 A1).
Regarding Claim 9:
The combination of Karlberg and Sundradhas teaches the method of claim 1.
The combination of Karlberg and Sundradhas does not teach wherein the embedding model comprises a large language model (LLM).
However, Gardner teaches wherein the embedding model comprises a large language model (LLM) (Gardner - [claim 1, lines 1 -18]: system comprising: …one or more computer
processors to perform operations, the operations comprising: receiving a content corpus to be
summarized; generating a first summarized content by applying a large language model (LLM) or
an appropriate processing model to the content corpus to create an initial summary at a first predefined abstraction level; recursively generating subsequent summarized content by
applying the large language model or the appropriate processing model to previously
summarized content at progressively greater abstraction levels, wherein each subsequent
summary is derived from last summarized content without reprocessing the content corpus; and
Paragraph [1223, lines 1-6]: vector space indexing system may also enhance the recursive
zooming capabilities of the summarization process. As summaries are generated at different
abstraction levels, their vector representations may be stored in the index, creating a
hierarchical structure of semantic embeddings).
It would have been obvious, before the effective filing date of the claimed invention, to a person
having ordinary skill in the art to modify combination of Karlberg and Sundradhas, further incorporating Gardner to arrive at the conclusion of the claimed apparatus. One would be motivated to incorporate Gardner’s teaching to have an LLM that enables more control over depth and brevity by controlling abstraction depth and content representation in the embedding model of Karlberg and Sundradhas’s apparatus; see Gardner Paragraph [0014-0016]. This addition allows the embedding model to better categorize extracted information in Sundradhas’s teachings, while also being secured with Karlberg’s access control teachings that restricts access to the sensitive, extracted information.
Regarding Claim 19:
Rejection of claim 11 is incorporated. In addition, claim 19 recites features that similar to those
of claim 9. Therefore, claim 19 is rejected with the same rationale and motivation as applied against
claim 9 above.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant’s disclosure.
Choi (US 20250231980 A1) teaches a device that uses a natural language processor to identify text in documents similar to a user’s topic query.
Dang (US 9449182 B1) teaches a document management system generating access privileges for documents based on users in an organization.
Kishan (US 20250103741 A1) teaches a system creating an ACL based on contents in a newly created document.
Narayanaswamy (US 12229291 B1) teaches a management system generating an ACL file that is applied to files in a directory tree.
Daskon (US 20250097230 A1) teaches a system that determines access control for documents in different geographic regions.
Vangala (US 20220245267 A1) teaches a system that extracts topics from a corpus of documents and associates users with each category based on user access privileges.
Applicant's amendment necessitated the new grounds of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to NATHANIEL C SKIRVIN whose telephone number is (571)272-9798. The examiner can normally be reached Monday-Friday 8-5.
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, Yin Chin Shaw can be reached at (571) 272-8878. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/NATHANIEL CHRISTIAN SKIRVIN/Examiner, Art Unit 2498
/YIN CHEN SHAW/Supervisory Patent Examiner, Art Unit 2498
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