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 .
DETAILED ACTION
1. This Office Action is in response to the application filed on 04/30/2025.
Claims 1-20 are pending.
Priority
2. This application is a Continuation of 18/101,286 (Patent US 12,314,328), which was filed on 01/25/2023, was acknowledged and considered.
Double Patenting
3. The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the "right to exclude" ranted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory obviousness-type double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Omum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); and In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on a nonstatutory double patenting ground provided the conflicting application or patent either is shown to be commonly owned with this application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement.
Effective January 1, 1994, a registered attorney or agent of record may sign a terminal disclaimer. A terminal disclaimer signed by the assignee must fully comply with 37 CFR 3.73(b).
4. Claims 1-20 are rejected on the ground of nonstatutory obviousness-type double patenting as being unpatentable over claims 1-16 of U.S. Patent No. 12,314,328. Although the conflicting claims are not identical, they are not patentably distinct from each other.
Instant application 19194434
Patent US 12,314,328
Claim 1:
A computer-implemented method, comprising:
receiving, using at least one processor, a text for generation of an electronic document;
querying, using the at least one processor, using one or more parameters associated with
the text, a storage location storing one or more candidate text portion suggestions;
applying, using the at least one processor, a machine-learned model to the one or more
candidate text portion suggestions and the text to generate at least one document portion for the electronic document; and
generating, using the at least one processor, using the machine-learned model, the
electronic document using the at least one document portion.
Claim 1:
A method comprising:
identifying a first set of agreement documents associated with a received set of clause terms; identifying a first set of text portions within the first set of agreement documents that include a threshold number of the received set of clause terms;
identifying a second set of text portions within a second set of agreement documents associated with a subject using a machine learned language model;
presenting each of a plurality of the second set of text portions within a corresponding portion of an interface;
determining a measure of similarity between the first set of text portions and each of the text portions in the second set of text portions; and
for each text portion in the second set of text portions, presenting the measure of similarity to a user within the corresponding interface portion; identifying the agreement document from which the text portion originates;
determining characteristics of the agreement document from which the text portion originates; and in response to receiving a selection from a user, presenting the determined characteristics to the user.
Claim 8:
A non-transitory computer-readable storage medium storing executable instructions that, when executed by at least one processor, cause the at least one processor to:
receive a text for generation of an electronic document;
query using one or more parameters associated with the text, a storage location storing one or more candidate text portion suggestions;
apply a machine-learned model to the one or more candidate text portion suggestions and
the text to generate at least one document portion for the electronic document; and
generate, using the machine-learned model, the electronic document using the at least one
document portion.
Claim 9:
A non-transitory computer-readable storage medium storing executable instructions that, when executed by a hardware processor, cause the hardware processor to:
identify a first set of agreement documents associated with a received set of clause terms;
identify a first set of text portions within the first set of agreement documents that include a threshold number of the received set of clause terms;
identify a second set of text portions within a second set of agreement documents associated with a subject using a machine learned language model;
present each of a plurality of the second set of text portions within a corresponding portion of an interface;
determine a measure of similarity between the first set of text portions and each of the text portions in the second set of text portions; and
for each text portion in the second set of text portions, present the measure of similarity to a user within the corresponding interface portion;
identify the agreement document from which the text portion originates; determine characteristics of the agreement document from which the text portion originates; and
in response to receiving a selection from a user, present the determined characteristics to the user.
Claim 15:
A system, comprising:
at least one processor; and
a non-transitory computer-readable storage medium storing executable instructions that,
when executed, cause the at least one processor to:
receive a text for generation of an electronic document;
query using one or more parameters associated with the text, a storage location
storing one or more candidate text portion suggestions;
apply a machine-learned model to the one or more candidate text portion
suggestions and the text to generate at least one document portion for the electronic
document; and
generate, using the machine-learned model, the electronic document using the at
least one document portion.
Claim 16:
A document management system, comprising:
a hardware processor; and
a non-transitory computer-readable storage medium storing executable instructions that, when executed, cause the hardware processor to: identify a first set of agreement documents associated with a received set of clause terms;
identify a first set of text portions within the first set of agreement documents that include a threshold number of the received set of clause terms;
identify a second set of text portions within a second set of agreement documents associated with a subject using a machine learned language model;
present each of a plurality of the second set of text portions within a corresponding portion of an interface; determine a measure of similarity between the first set of text portions and each of the text portions in the second set of text portions; and
for each text portion in the second set of text portions, present the measure of similarity to a user within the corresponding interface portion; identify the agreement document from which the text portion originates;
determine characteristics of the agreement document from which the text portion originates; and
in response to receiving a selection from a user, present the determined characteristics to the user.
Examiner’s Note
5. Sumner et al, US 12,169,697, [Sumner: Column 5, lines 33-48 (“generating expert suggestions that elaborate on the text data. To generate an expert suggestion, the expert model 116 may receive a text data. The text data may be text input by a user, such as text entered on a keyboard via I/O interface 110. The expert model 116 may use supervised machine learning techniques to train a neural network to receive an input and generate an output, where the input may include the text data and the output may be one or more sentences based on the text data. An example neural network includes, but is not limited to, OpenAI® GPT-3 and BERT. In some embodiments, the input to the expert model 116 may be the text data, thus priming the expert model 116 for predicting one or more sentences based on the text data. The predictions output from the expert model may be the expert suggestion”, i.e., receiving textual input and generating output using a machine learning model)] [Sumner: Column 5, lines 49-63 (“Extracting keywords may utilize supervised methods that train a machine learning model based on labeled training sets and uses the trained model to determine whether a word is a keyword, wherein the machine learning model is a decision tree, a Bayes classifier, a support vector machine, a convolutional neural network, and the like. Extracting keywords may also or instead utilize unsupervised methods that rely on linguistic-based, topic-based, statistics-based, and/or graph-based features of the text data such as text-frequency inverse-document-frequency (TF-IDF), KP-miner, TextRank, Latent Dirichlet Allocation (LDA), and the like”, i.e., applying a machine learning model to candidate text portion suggestion)].
Tiku et al, US 20240070390, [Tiku: Paragraph 25 (“the language model may be an attention-based language model deployed on the XR device. The language model may be a transformer-based machine learning model for natural language processing, such as Bidirectional Encoder Representations from Transformers (BERT).”)].
Tran, US 20230351102, [Tran: Paragraph 9 (“The system may include biasing neural network weights with the milestone overview text when generating a context-sensitive component text suggestion. The combining further may include combining a title and a background text with the one or more seed landmark texts and providing the combined title, background, and seed landmark texts to a learning machine to synthesize artificial-intelligence-generated text. The system The system may include extracting one or more references from a figure and annotating the one or more references with text; and forming one or more artificial-intelligence-generated reference text suggestions. The system The system may include performing grammar analysis and suggesting grammar correction and editing the document for conciseness.”)] [Tran: Paragraphs 27 and 38 (“The annotation can be typed in or can be optically recognized using a learning machine, computer vision (OpenCV), or other suitable machine recognition techniques. The user can type in brief descriptions of the drawings on the top of space 4, and a few sentences in the detailed description section 10. With that seed information, the artificial intelligence software starts suggesting one or more text paragraphs for the user to adopt or edit/revise and then add to the detailed description. Next, the system goes through each annotation in space 6 and machine-generated additional text suggestions for the user to apply to the detailed description”, i.e., applying a machine learning model to candidate text portion suggestions)] [Tran: Paragraph 43 (“The resulting machine operation feature vector is provided to the learning machine.”, i.e., applying a machine learning model to candidate text portion suggestions)] [Tran: Paragraph 9 (“The system The system may include biasing neural network weights with the milestone overview text when generating a context-sensitive component text suggestion. The combining further may include combining a title and a background text with the one or more seed landmark texts and providing the combined title, background, and seed landmark texts to a learning machine to synthesize artificial-intelligence-generated text.”, i.e., applying a machine learning model to text portion suggestions and text to generate document portions)] [Tran: Paragraph 66 (“The transformer / learning machine receives from the user designations on how objects in an image and can move and change during the tweening process. To aid the transformer, the user can manually render or adjust transitional frames by hand or software may be used to automatically render transitional frames using interpolation of graphic parameters. The instant video inbetweening applies the learning machines to the inbetweening workflow where keyframes are generated by a skilled artisan, and then inbetween movements are specified for rendering software.”, i.e., “rendering software” is ‘the electronic document’ and other inputs are ‘document portion’ and ‘text’)] [Tran: Paragraph 96 (“generated using the image transformers or learning machines to generate computer code in accordance with the pseudo-code”, i.e., “computer code” is ‘the electronic document’)].
Satterfield et al, US 20200192921, [Satterfield: Paragraphs 4 and 50 (“Receiving, from a client device via a computer network, a request for suggested text to insert at a position in the electronic input document and contextual information about the input document” AND “andidate texts in response to requests for suggested text received from client devices 110. The text identification module 314 receives a request for suggested text for an input document from a client device 110 and associated contextual information”)] [Satterfield: Paragraph 4 (“The method evaluates a set of candidate texts generated from a set of source documents using the set of annotations to generate confidence scores for the candidate texts, where a confidence score for a candidate text indicates suitability of the candidate text for use as the suggested text”)] [Satterfield: Paragraph 46 (“generates a meaning vector for a text unit by applying a machine-learned embedding model to the sets of words that make up the text unit. The embedding model is configured to receive a set of words, and output a word embedding vector that characterizes the meaning of the set of words”)].
Claim Rejections - 35 USC § 102
6. In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
7. The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
8. Claims 1-20 are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Tran (US 20230351102).
Claim 1:
Tran suggests a computer-implemented method, comprising: receiving, using at least one processor, a text for generation of an electronic document [Tran: Paragraph 4 and Abstract (“The AI architecture herein can be used for communication, for example, to generate long text or video using the neural network architectures. In one aspect for AI content generation, computerized systems and methods are disclosed to generate a document from one or more first and second text prompts”)]. Tran suggests querying, using the at least one processor, using one or more parameters associated with the text, a storage location storing one or more candidate text portion suggestions [Tran: Paragraphs 104-108 (“FIG. 2G shows an exemplary process to generate targeted responses/proposals for the user. The process includes: [0105] Select deep neural network architecture (for example, retrieval, generative, and retrieve/refine, transformer-based, BERT-based, GPT-based, among others) for a learning machine [0106] Train the learning machine with data that is logically grouped or clustered to provide context and accuracy (for example, by technology field or by industry/specialization; by computer code such as ASIC code, database code, html code, neural network code; by type of writing, by type of novel or movie them such as Mysteries, Romance, Thriller, Science Fiction, Fantasy, Historical Fiction, among others) [0107] Gather customization information from user by interacting with the user and request DNN to generate context sensitive text suggestion [0108] Determine the trained group or cluster best matching the customization information and apply the customization information to bias the learning machine to generate context-sensitive responses that are realistic in terms of accuracy and depth”)]. Tran suggests applying, using the at least one processor, a machine-learned model to the one or more candidate text portion suggestions and the text to generate at least one document portion for the electronic document [Tran: Paragraph 43 (“The resulting machine operation feature vector is provided to the learning machine.”, i.e., applying a machine learning model to candidate text portion suggestions)] [Tran: Paragraph 9 (“The system The system may include biasing neural network weights with the milestone overview text when generating a context-sensitive component text suggestion. The combining further may include combining a title and a background text with the one or more seed landmark texts and providing the combined title, background, and seed landmark texts to a learning machine to synthesize artificial-intelligence-generated text.”, i.e., applying a machine learning model to text portion suggestions and text to generate document portions)]. Tran suggests generating, using the at least one processor, using the machine-learned model, the electronic document using the at least one document portion [Tran: Paragraph 66 (“The transformer / learning machine receives from the user designations on how objects in an image and can move and change during the tweening process. To aid the transformer, the user can manually render or adjust transitional frames by hand or software may be used to automatically render transitional frames using interpolation of graphic parameters. The instant video inbetweening applies the learning machines to the inbetweening workflow where keyframes are generated by a skilled artisan, and then inbetween movements are specified for rendering software.”, i.e., “rendering software” is ‘the electronic document’ and other inputs are ‘document portion’ and ‘text’)] [Tran: Paragraph 96 (“generated using the image transformers or learning machines to generate computer code in accordance with the pseudo-code”, i.e., “computer code” is ‘the electronic document’)] [Tran: Paragraphs 147-151 (“generates context-sensitive text by: [0148] using a first learning machine to map text matching each topic to a corresponding vector; [0149] building a search index for the search topics and in response to a search topic returning a responsive first vector; [0150] training a second learning machine to generate text from a training corpus; [0151] at run time, using the first learning machine to look up the search index and select responsive documents to provide to the second learning machine generate responsive context-sensitive text. The second learning machine can be a learning machine architecture (LMA) trained a corpus on a specific domain (such as engineering, medical, chemical, patent), wherein the architecture can be GPT, or a suitable network,”)].
Claim 2:
Tran suggests wherein the one or more candidate text portion suggestions are configured to be ranked using the machine-learned model based on a likelihood that each candidate text portion suggestion in the one or more candidate text portion suggestions will be selected for generation of the at least one document portion [Tran: Paragraph 4 and Abstract (“generating one or more context-sensitive text suggestions using a transformer with an encoder on the text prompts and a decoder that produces a text expansion to provide the context-sensitive text suggestions based on the one or more first and second text prompts by applying generative artificial intelligence with token biased weights for zero-shot”)].
Claim 3:
Tran suggests wherein at least one candidate text portion suggestion in the one or more candidate text portion suggestions is selected for generation of the at least one document portion based on a rank associated with the at least one candidate text portion suggestion [Tran: Paragraph 4 and Abstract (“generating one or more context-sensitive text suggestions using a transformer with an encoder on the text prompts and a decoder that produces a text expansion to provide the context-sensitive text suggestions based on the one or more first and second text prompts by applying generative artificial intelligence with token biased weights for zero-shot”)].
Claim 4:
Tran suggests wherein the one or more parameters include at least one of: one or more characteristics of the electronic document, one or more words in the electronic document, one or more text portions in the electronic document, a type of the electronic document, one or more clause terms in the electronic document, and any combination thereof [Tran: Paragraph 4 and Abstract (“generating one or more context-sensitive text suggestions using a transformer with an encoder on the text prompts and a decoder that produces a text expansion to provide the context-sensitive text suggestions based on the one or more first and second text prompts by applying generative artificial intelligence with token biased weights for zero-shot”)].
Claim 5:
Tran suggests wherein the one or more characteristics of the electronic document include at least one of: a context of the electronic document, an author of the electronic document, an entity associated with the electronic document, an industry associated with the electronic document, or any combinations thereof [Tran: Paragraph 9 (“The system may include detecting plagiarism in the document by matching the document text to text crawled from the Internet. The system may include generating a part list by detecting noun phrases (NPs) in the document and corresponding numbers for the NPs. The system may include generating a list of claimed elements. The system may include generating a list of unclaimed elements.”)].
Claim 6:
Tran suggests wherein the machine-learned model has been trained using one or more historical candidate text portion suggestions selected for inclusion into one or more historical electronic documents [Tran: Paragraph 135 (“the system performs training on the corpus with a vocabulary of around 52000 words. It then gets a subset of documents (either from a search on terms that is close to the target text or from prior history of text generated by the user, for example.”)].
Claim 7:
Tran suggests wherein the machine-learned model is retrained based on at least one candidate text portion suggestion in the one or more candidate text portion suggestions selected for generation of the at least one document portion for the electronic document [Tran: Paragraph 135 (“the system performs training on the corpus with a vocabulary of around 52000 words. It then gets a subset of documents (either from a search on terms that is close to the target text or from prior history of text generated by the user, for example.”)] [Tran: Paragraph 4 and Abstract (“generating one or more context-sensitive text suggestions using a transformer with an encoder on the text prompts and a decoder that produces a text expansion to provide the context-sensitive text suggestions based on the one or more first and second text prompts by applying generative artificial intelligence with token biased weights for zero-shot”)].
Claim 8:
Claim 8 is essentially the same as claim 1 except that it sets forth the claimed invention as a program product rather than a method and rejected under the same reasons as applied above.
Claim 9:
Claim 9 is essentially the same as claim 2 except that it sets forth the claimed invention as a program product rather than a method and rejected under the same reasons as applied above.
Claim 10:
Claim 10 is essentially the same as claim 3 except that it sets forth the claimed invention as a program product rather than a method and rejected under the same reasons as applied above.
Claim 11:
Claim 11 is essentially the same as claim 4 except that it sets forth the claimed invention as a program product rather than a method and rejected under the same reasons as applied above.
Claim 12:
Claim 12 is essentially the same as claim 5 except that it sets forth the claimed invention as a program product rather than a method and rejected under the same reasons as applied above.
Claim 13:
Claim 13 is essentially the same as claim 6 except that it sets forth the claimed invention as a program product rather than a method and rejected under the same reasons as applied above.
Claim 14:
Claim 14 is essentially the same as claim 7 except that it sets forth the claimed invention as a program product rather than a method and rejected under the same reasons as applied above.
Claim 15:
Claim 15 is essentially the same as claim 1 except that it sets forth the claimed invention as a system rather than a method and rejected under the same reasons as applied above.
Claim 16:
Claim 16 is essentially the same as claim 2 and claim 3 except that it sets forth the claimed invention as a system rather than a method and rejected under the same reasons as applied above.
Claim 17:
Claim 17 is essentially the same as claim 4 except that it sets forth the claimed invention as a system rather than a method and rejected under the same reasons as applied above.
Claim 18:
Claim 18 is essentially the same as claim 5 except that it sets forth the claimed invention as a system rather than a method and rejected under the same reasons as applied above.
Claim 19:
Claim 19 is essentially the same as claim 6 except that it sets forth the claimed invention as a system rather than a method and rejected under the same reasons as applied above.
Claim 20:
Claim 20 is essentially the same as claim 7 except that it sets forth the claimed invention as a system rather than a method and rejected under the same reasons as applied above.
9. Any inquiry concerning this communication or earlier communications from the examiner should be directed to [Hung D. Le], whose telephone number is [571-270-1404]. The examiner can normally be communicated on [Monday to Friday: 9:00 A.M. to 5:00 P.M.].
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Apu Mofiz can be reached on [571-272-4080]. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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Hung Le
03/31/2026
/HUNG D LE/Primary Examiner, Art Unit 2161
Prosecution Insights