Office Action Predictor
Last updated: April 16, 2026
Application No. 18/743,807

DISTILLING LANGUAGE MODELS

Non-Final OA §101§102§103
Filed
Jun 14, 2024
Examiner
KY, KEVIN
Art Unit
2671
Tech Center
2600 — Communications
Assignee
Zoom Video Communications, INC.
OA Round
1 (Non-Final)
76%
Grant Probability
Favorable
1-2
OA Rounds
2y 6m
To Grant
99%
With Interview

Examiner Intelligence

Grants 76% — above average
76%
Career Allow Rate
420 granted / 549 resolved
+14.5% vs TC avg
Strong +23% interview lift
Without
With
+22.6%
Interview Lift
resolved cases with interview
Typical timeline
2y 6m
Avg Prosecution
33 currently pending
Career history
582
Total Applications
across all art units

Statute-Specific Performance

§101
17.6%
-22.4% vs TC avg
§103
46.5%
+6.5% vs TC avg
§102
20.8%
-19.2% vs TC avg
§112
9.9%
-30.1% vs TC avg
Black line = Tech Center average estimate • Based on career data from 549 resolved cases

Office Action

§101 §102 §103
DETAILED ACTION 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 a judicial exception without significantly more. The claim(s) recite(s) limitations that fall under the grouping of abstract idea of “Certain Methods of Organizing Human Activity”, e.g. Concepts Relating To Managing Human Behavior (Step 2A, Prong One), “Mathematical Concepts” and “Mental Processes”, e.g. concepts performed in the human mind (including an observation, evaluation, judgment, opinion) (step 2A). Specifically, the claim recites selecting data based on similarity, generating a prompt including example information, inputting the prompt into a language model to generate a summary, and training another language model using the generated summary. These steps constitutes data analysis, data generating, and mathematical evaluation, which are various steps of human activity, mental processes, and mathematical concepts. Under step 2A, prong two, this judicial exception is not integrated into a practical application. The claim does not recite any specific computing implementation, does not require any particular algorithm, model or hardware configuration, and lacks technical improvement to image processing or machine learning. Furthermore, the judicial exception is not integrated into a practical application because the claims are directed to an abstract idea with additional generic computer elements (e.g. processor, memory, computer storage medium, etc.), which are generically recited computer elements that do not add a meaningful limitation to the abstract idea because they amount to simply implementing the abstract idea on a computer. Under step 2B, the claims does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because these are well-understood, routine, conventional computer functions as recognized by the court decisions listed in MPEP § 2106.05(d). The claims do not include an inventive concept that is sufficient to transform the abstract idea into a patent-eligible application. The dependent claims 2-8, 10-16 and 18-20 only add limitations that further define the abstract idea using conventional mathematical techniques, data processing steps, or generic ML implementation details. None of the dependent claims integrates the abstract idea into a practical application or adds an inventive concept. For instance, claims 2, 4, 10 and 12 uses a specific similarity metric but this merely limits the abstract idea to a particular mathematical formula. Claims 3, 5, 11, 13, 18, and 19 recite steps of data collection, labeling, and organization, which courts have consistently found abstract. These claims do not recite any new data structure, storage mechanism, or training algorithm. Claims 6, 14, and 20 uses a mathematical score to decide whether to keep or discard data, which does not add significantly more. Claims 7 and 15 recites post-processing steps or applying results of an abstract idea, which does not confer eligibility. Claims 8 and 16 recites increasing the number of examples, which does not transform the nature of the abstract idea. Claims 9-20 recite the same abstract idea as claims 1-8, but using generic computer components (e.g. processors, CRM) does not render the claims eligible. Claim Rejections - 35 USC § 102 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. Claim(s) 1, 8, 9, 16 and 17 is/are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Fabbri et al (US 20230419017). Regarding claim 1, Fabbri discloses a method comprising: selecting an entry from a validation set based on similarity to a first communication record, wherein the entry from the validation set includes a second communication record and a corresponding summary of the second communication record (¶40 uncompressed text 304 may function as the ground-truth of the training data for perturber model 308; ¶42 perturber model 308 may be trained to predict perturbed text 310 that is sufficiently similar to uncompressed text 304, and may include inserted information entities 306 and compressed text 302; ¶45 Information entities 416 may be selected from source document 408 of reference summary 402 and may be inserted into reference summary 402 to generate perturbed summary 406); generating a prompt that includes the first communication record, the second communication record, and the corresponding summary of the second communication record (¶51 compressed text 302 and reference summary 402 may be the same or different, and perturbed text 310 and perturbed summary 406 may be the same or different; ¶55 a training data set including at least an uncompressed text (e.g., 304 in FIG. 3), a compressed text (e.g., 302 in FIG. 3), and one or more information entities (e.g., 306 in FIG. 3) accompanying the compressed text is received via a communication interface (e.g., 115 in FIG. 1); ¶59 a perturbed summary (e.g., 406 in FIG. 4) is generated by the trained perturber model (e.g., 404 in FIG. 4) in response to an input of a reference summary (e.g., 402) and a source document (e.g., 408 in FIG. 4)); inputting the prompt to a first language model to obtain a generated summary of the first communication record (¶59 At step 508, a perturbed summary (e.g., 406 in FIG. 4) is generated by the trained perturber model (e.g., 404 in FIG. 4) in response to an input of a reference summary (e.g., 402) and a source document (e.g., 408 in FIG. 4); ¶60 a predicted summary (e.g., 412 in FIG. 4) is generated by an editor model); and training a second language model based on the first communication record and the generated summary (¶61 ] At step 512, the editor model is trained based on a second training objective comparing the predicted summary and the reference summary). Regarding claim 8, Fabbri discloses the method of claim 1, further comprising: selecting a second entry from the validation set based on similarity to the first communication record, wherein the second entry from the validation set includes a third communication record and a corresponding summary of the third communication record (¶40 the sentence-compression data may include one or more pairs of a compressed text 302, a corresponding uncompressed text 304, and one or more information entities 306); and wherein the prompt includes the third communication record and the corresponding summary of the third communication record (¶51 compressed text 302 and reference summary 402 may be the same or different, and perturbed text 310 and perturbed summary 406 may be the same or different; ¶55 a training data set including at least an uncompressed text (e.g., 304 in FIG. 3), a compressed text (e.g., 302 in FIG. 3), and one or more information entities (e.g., 306 in FIG. 3) accompanying the compressed text is received via a communication interface (e.g., 115 in FIG. 1); ¶59 a perturbed summary (e.g., 406 in FIG. 4) is generated by the trained perturber model (e.g., 404 in FIG. 4) in response to an input of a reference summary (e.g., 402) and a source document (e.g., 408 in FIG. 4)). Regarding claim(s) 9 and 16 (drawn to a system): The rejection/proposed combination of Fabbri, explained in the rejection of method claim(s) 1 and 8, anticipates/renders obvious the steps of the system of claim(s) 9 and 16 because these steps occur in the operation of the proposed combination as discussed above. Thus, the arguments similar to that presented above for claim(s) 1 and 8 is/are equally applicable to claim(s) 9 and 16. See Fabbri ¶18 As shown in FIG. 1, computing device 100 includes a processor 110 coupled to memory 120. Operation of computing device 100 is controlled by processor 110. Regarding claim(s) 17 (drawn to a CRM): The rejection/proposed combination of Fabbri, explained in the rejection of method claim(s) 1, anticipates/renders obvious the steps of the computer readable medium of claim(s) 17 because these steps occur in the operation of the proposed combination as discussed above. Thus, the arguments similar to that presented above for claim(s) 1 is/are equally applicable to claim(s) 17. See Fabbri ¶18 As shown in FIG. 1, computing device 100 includes a processor 110 coupled to memory 120. Operation of computing device 100 is controlled by processor 110. 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. Claim(s) 2 and 10 is/are rejected under 35 U.S.C. 103 as being unpatentable over Fabbri as applied to claim 1 and 9 above, and further in view of Azizi et al (US 20240403339). Regarding claim 2, Fabbri discloses the method of claim 1, but fail to teach where Azizi teaches wherein the similarity of the entry from the validation set to the first communication record is determined based on a cosine distance between a first embedding generated from the first communication record and a second embedding generated from the second communication record (¶41 Comparison component 215 is configured to compare two document embeddings to determine a measure of their similarity. There are several ways to compare two vectors to generate a value of their similarity, including Euclidean distance, dot product, and cosine similarity. Some embodiments of comparison component 215 are configured to compare two document embeddings using cosine similarity.). Therefore, it would have been obvious to one with ordinary skill in the art before the effective filing date of the invention to have implemented the teaching of wherein the similarity of the entry from the validation set to the first communication record is determined based on a cosine distance between a first embedding generated from the first communication record and a second embedding generated from the second communication record from Azizi into the method as disclosed by Fabbri. The motivation for doing this is to improve systems and methods for generating contextual document embeddings and recommending similar articles based on the document embeddings. Regarding claim(s) 10 (drawn to a system): The rejection/proposed combination of Fabbri and Azizi, explained in the rejection of method claim(s) 2, anticipates/renders obvious the steps of the system of claim(s) 10 because these steps occur in the operation of the proposed combination as discussed above. Thus, the arguments similar to that presented above for claim(s) 2 is/are equally applicable to claim(s) 10. See Fabbri ¶18 As shown in FIG. 1, computing device 100 includes a processor 110 coupled to memory 120. Operation of computing device 100 is controlled by processor 110. Allowable Subject Matter Claims 3-7, 11-15 and 18-20 would be allowable if rewritten to overcome the rejection(s) under 35 U.S.C. 101, set forth in this Office action and to include all of the limitations of the base claim and any intervening claims. Conclusion Any inquiry concerning this communication or earlier communications from the examiner should be directed to KEVIN KY whose telephone number is (571)272-7648. The examiner can normally be reached Monday-Friday 9-5PM. 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, Vincent Rudolph can be reached at 571-272-8243. 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. /KEVIN KY/ Primary Examiner, Art Unit 2671
Read full office action

Prosecution Timeline

Jun 14, 2024
Application Filed
Jan 05, 2026
Non-Final Rejection — §101, §102, §103
Apr 07, 2026
Response Filed

Precedent Cases

Applications granted by this same examiner with similar technology

Patent 12597158
POSE ESTIMATION
2y 5m to grant Granted Apr 07, 2026
Patent 12597291
IMAGE ANALYSIS FOR PERSONAL INTERACTION
2y 5m to grant Granted Apr 07, 2026
Patent 12586393
KNOWLEDGE-DRIVEN SCENE PRIORS FOR SEMANTIC AUDIO-VISUAL EMBODIED NAVIGATION
2y 5m to grant Granted Mar 24, 2026
Patent 12586559
METHOD AND APPARATUS FOR GENERATING SPEECH OUTPUTS IN A VEHICLE
2y 5m to grant Granted Mar 24, 2026
Patent 12579382
NATURAL LANGUAGE GENERATION USING KNOWLEDGE GRAPH INCORPORATING TEXTUAL SUMMARIES
2y 5m to grant Granted Mar 17, 2026
Study what changed to get past this examiner. Based on 5 most recent grants.

AI Strategy Recommendation

Get an AI-powered prosecution strategy using examiner precedents, rejection analysis, and claim mapping.
Powered by AI — typically takes 5-10 seconds

Prosecution Projections

1-2
Expected OA Rounds
76%
Grant Probability
99%
With Interview (+22.6%)
2y 6m
Median Time to Grant
Low
PTA Risk
Based on 549 resolved cases by this examiner. Grant probability derived from career allow rate.

Sign in for Full Analysis

Enter your email to receive a magic link. No password needed.

Free tier: 3 strategy analyses per month