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 .
Continued Examination Under 37 CFR 1.114
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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 01/16/2026 has been entered. Claims 1-4, 7-13 and 16-19 are pending. Claims 1, 10 and 19 are currently amended. Claims 5-6, 14-15 and 20 have been cancelled without prejudice.
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.
The text of those sections of Title 35, U.S. Code not included in this action can be found in a prior Office action.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 1-4, 7-13 and 16-19 are rejected under 35 U.S.C. 103 as being unpatentable over Wilson et al. (Wilson), US Patent Application Publication No. US 2023/0052372 A1, in view of Sabapathy et al. (Sabapathy), US Patent Application Publication No. US 2023/0316098 A1, further in view of Huang et al. (Huang), US Patent 11,470,279, and further in view of Ibraheem, US Patent Application Publication No. US 2022/0012289 A1.
As to independent claim 1, Wilson discloses a computer-implemented method for predicting document metadata using machine learning, the computer-implemented method comprising:
receiving, by a document management system, a document representing an interaction between a plurality of entities, wherein a set of metadata attributes describe the interaction between the plurality of entities (paragraphs [0005]- [0007]: displaying an electronic document, displaying a list of suggested labels that may be applicable to categories of text within the electronic document, receiving different inputs from a user to interact with the electronic document, displaying suggested selections of text that may correspond to the suggested labels comprising repeating the receiving steps for the first user input and the second user input for one or more additional selections of text and assigned labels, wherein the selected text comprises a word, a phrase, a sentence, a paragraph, a section, or a table, wherein the list of suggested labels (metadata attributes) comprises a list of text categories that includes name, date, address, signature, or any combination thereof);
extracting from the document, a set of tokens and one or more sequences of tokens, wherein tokens of a sequence of tokens occur adjacent to each other in the document (paragraph [0013]: extract text corresponding to one or more labels for the type of electronic document; paragraph [0010]: the selected text comprises a word (token), a phrase, a sentence, a paragraph (tokens/sequence of tokens);
providing the set of tokens as input to one or more machine learning models (paragraph [0010]:repeating the displaying and receiving steps for one or more additional electronic documents and storing one or more additional annotated electronic documents, and using the stored annotated electronic documents as training data to train a machine learning model to automatically predict and extract selections of text corresponding to one or more labels from non-annotated electronic documents);
executing the one or more machine learning models to predict metadata attributes describing one or more tokens and one or more sequences of tokens of the document, the metadata attributes describing the interaction between the plurality of entities (paragraph [0013]: providing a plurality of machine learning models, each is selected based on a type of electronic document and is trained to extract text corresponding to one or more labels for that type of electronic document); and
annotating each of one or more tokens and one or more sequences of tokens of the document with a metadata attribute predicted using the one or more machine learning models (paragraph [0014]: the plurality of machine learning models are continuously trained as additional annotated documents; paragraph [0054]: review AI-predicted annotations that match a list of user-selected terms or labels).
Wilson, however, does not disclose wherein each of the one or more machine learning models is trained to predict scores indicating a likelihood that a token or a sequence of tokens of an input document represents a metadata attribute describing the interaction between the plurality of entities.
In the same field of endeavor, Sabapathy discloses a plurality of coordinate vectors are generated and positioned in relation to the plurality of value data token, wherein a coordinate vector may be generated and span between respective spatial coordinate sets for the label data token and a value data token, and the coordinate vectors are then provided to a vector classification machine learning model configured to output a classification and/or a pairing score for each coordinate vector that represents a predicted likelihood that a coordinate vector is indicative of a label-value pair or a predicted likelihood that the value data token to which a coordinate vector extends likely describes the entity identified by the label data token (paragraph [0105]).
It would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to modify the system of Wilson to include each of the one or more machine learning models is trained to predict scores indicating a likelihood that a token or a sequence of tokens of an input document represents a metadata attribute describing the interaction between the plurality of entities, as taught by Sabapathy for the purpose of finding the likelihood matching label data token.
As pointed out above that Sabapathy discloses a plurality of coordinate vectors are generated and positioned in relation to the plurality of value data token, wherein a coordinate vector may be generated and span between respective spatial coordinate sets for the label data token and a value data token, and the coordinate vectors are then provided to a vector classification machine learning model configured to output a classification and/or a pairing score for each coordinate vector that represents a predicted likelihood that a coordinate vector is indicative of a label-value pair or a predicted likelihood that the value data token to which a coordinate vector extends likely describes the entity identified by the label data token (paragraph [0105]). Sabapathy, however does not disclose “wherein the one or more machine learning models receives information identifying an input token and outputs a set of scores, each score indicating a likelihood that the input token represents a particular metadata attribute for a given type of interaction”.
In the same field of endeavor, Huang discloses a method includes obtaining a transcript of a conference, inputting strings from the transcript to a machine learning model to obtain respective scores for the strings; selecting a string for highlighting from the transcript based on respective scores of strings (col. 1, lines 29-39). Huang further discloses extracting a text summary from the transcript using a natural language processing technique, wherein the text summary may be a subset of the set of strings in the transcript that are considered most significant, and wherein the text summary provides a condensed version of the transcript of the conference, which is a telephone call between two or more participants (entities) (Figure 5, col. 16, line 11 – col. 17, line 14). Huang further discloses the text summary may be a set of pointers or identifiers for strings of the transcript that have been identified as most relevant, for example, respective scores may be determined for strings of the transcript that reflect relevance of the strings, and theses scores may be used to rank and select strings of the transcript for inclusion in the text summary, wherein a machine learning model is trained and used to determine respective scores for strings of the transcript (col. 16, line 58 – col. 17, line 17). Huang further discloses determining respective scores for strings of a transcript based on content of the strings (col. 16, line 58 – col. 17, line 17).
It would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to modify the system of Wilson and Sabapathy, to include “wherein the one or more machine learning models receives information identifying an input token and outputs a set of scores, each score indicating a likelihood that the input token represents a particular metadata attribute for a given type of interaction”, as taught by Huang. Huang discloses respective scores may be determined for strings of the transcript that reflect relevance of the strings, and theses scores may be used to rank and select strings of the transcript for inclusion in the text summary (Huang, col. 16, line 58 – col. 17, line 17).
Wilson, Sabapathy, and Huang, however, does not disclose wherein a machine learning model predicts a type of interaction between the plurality of entities.
Ibraheem discloses systems and methods that can provide complete, accurate, and real time predictions to the user on demand, using a self-automated map and/or an intelligence matrix to automatically generate a query response for a user, wherein the query response can include a prediction related to an entity (e.g., person, object, location, etc.), and the prediction can include a possibility of interaction between two or more entities and/or the type of interaction between two or more entities (paragraph [0011]). Ibraheem further discloses the intelligence matrix generator 216 can automatically generate an intelligence matrix that can predict a possible of interaction between tow or more entities and/or the type of interaction between two or more entities (paragraph [0028]). Ibraheem further discloses the intelligence matrix generator 216 is located in the host device 108, which can be any suitable processing device such as processor configured to run and/or execute a set of instructions or code and may include machine learning processors, and deep learning processors (Figure 2 and paragraph [0034]). Ibraheem further discloses the machine learning mode being conjured to generate the matrix (Claim 16).
It would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention to incorporate a machine learning model predicts a type of interaction between the plurality of entities, as taught by Ibraheem for the purpose of providing complete, accurate, and real time predictions to a user on demand (Ibraheem, paragraph [0011]).
As to dependent claim 2, Wilson discloses identifying a set of tokens of the document such that a same metadata attribute is predicted for each token from the set of tokens and each token from the set of tokens is adjacent to at least one other token from the set of tokens; and annotating the document such that the set of tokens is indicated as representing the metadata attribute (paragraphs [0007], [0010]).
As to dependent claim 3, Wilson discloses wherein a machine learning model predicts a date associated with the interaction between the plurality of entities (paragraph [0007]).
As to dependent claim 4, Wilson discloses wherein a machine learning model predicts a role of each entity from the plurality of entities (paragraphs [0075], [0081]).
As to dependent claim 7, Wilson discloses wherein a machine learning model further receives as input, information describing one or more user interactions with the document (paragraphs [0006]-[0007]).
As to dependent claim 8, Wilson discloses wherein a machine learning model further receives as input, information describing a relation of the document with one or more other documents of the document management system (paragraph [0013]).
As to dependent claim 9, Wilson and Sabapathy disclose wherein a machine learning model receives as input, a sequence tokens of the document, wherein the sequence of tokens represents one or more sentences of the document, wherein the machine learning model outputs a set of scores, each score indicating a likelihood that the sequence of tokens represents a particular metadata attribute (Wilson, paragraph [0054]; Sabapathy, paragraph [0105]).
Claims 10-13 and 16-18 are medium claims that contain similar limitations of claims 1-4, 7-9, respectively. Therefore, claims 10-13 and 16-18 are rejected under the same rationale.
Claim 19 is system claims that contain similar limitations of claim 1. Therefore, claim 19 is rejected under the same rationale.
Response to Arguments
Applicant’s arguments and amendments filed on 06/30/2025 have been fully considered but they are not deemed fully persuasive. Applicant’s arguments with respect to claims 1-5, 7-14, 16-18 and 19 have been considered but are moot in view of the new ground(s) of rejection as explained here below, necessitated by Applicant’s substantial amendment (i.e., a machine learning model predicts a type of interaction between the plurality of entities) to the claims which significantly affected the scope thereof. Please see the rejection above with newly cited prior art Ibraheem).
Conclusion
Any inquiry concerning this communication should be directed to CHAU T NGUYEN at telephone number (571)272-4092. The examiner can normally be reached on M-F from 8am to 5pm (PT).
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Cesar Paula, can be reached at telephone number 5712724128. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/CHAU T NGUYEN/Primary Examiner, Art Unit 2145