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
Claims 1-6, 8-13, and 15-20 are pending. Claims 1, 8 and 15 are independent.
This Application was published as US 20240037345.
Apparent priority is 28 July 2022.
Applicant’s amendments and arguments are considered but are either unpersuasive or moot in view of the new grounds of rejection that, if presented, were necessitated by the amendments to the Claims.
This action is Final.
Response to Amendment
Applicant’s amendments to the claims have overcome each and every 112(a) rejection previously set forth in the Non-Final Office Action mailed 1 April 2026.
Response to Arguments
35 USC 103
Applicant’s arguments with respect to claim(s) 1-6, 8-13, and 15-20 have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
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.
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.
Claim(s) 1-6, 8-13, and 15-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Brown et al. ("Language Models Are Few-Shot Learners") in view of Pfitzmann et al. (US 20230132061 A1) and Lester et al. (US 20240378196 A1).
Regarding claim 1 Brown discloses:
A method comprising: receiving data at a source system comprising a source-specific configuration, ("For each task we generate a dataset of 2,000 random instances of the task and evaluate all models on those instances."[pg. 22, paragraph 2]) wherein the data has a plurality of rows and an initial format (Figure G.21 shows the context data is in multiple rows. Brown also discloses use of massive datasets which could typically be arranged in rows. Brown also discloses data (example transformations) in rows in Fig. G.3.)
receiving, from a user at the source system, a description of a task associated with the data; ("task description"[pg. 7, Figure 2.1, Few-shot section])
receiving, from the user at the source system, a plurality of example transformations; ("examples"[pg. 7, Figure 2.1, Few-shot section])
receiving, from the user at the source system, input and output labels. (Figure G.18 shows input labels “Q:” and output labels “A:”.)
combining, via at least one processor of the source system, the task description together with the plurality of example transformations and input and output labels, resulting in a prompt; (Lines 1-5 are entered to the model at the same time and can be considered a single prompt.[pg. 7, Figure 2.1, Few-shot section])
wherein the combining further comprises performing a string aggregation of the task description together with the plurality of example transformations and input and output labels; (Fig. G.33 shows a task description (“Instructions”) with input and output labels (“Question:” and “Answer:”). These are aggregated in a string format, with different portions separated by equal signs (“=”) which would be valid in a string prompt. Fig. G.1 shows a string aggregation of example transformations with input and output labels. )
(It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to include the task description with the example transformations and labels in order to improve model performance. (“Model performance improves with the addition of a natural language task description, and with the number of examples in the model’s context, K.” Brown, Pg. 5 para 2))
executing, via the at least one processor, natural language processing (NLP) on the prompt, resulting in parsed text of the prompt; (“…our BPE encoding operates on significant fractions of a word (on average 0:7 words per token), so from the LM’s perspective succeeding at these tasks involves not just manipulating BPE tokens but understanding and pulling apart their substructure…”pg. 24, para 5 – BPE encoding includes parsing all input to the model under the BRI.)
training a machine learning model using the prompt (“As shown in Figure 2.1, for a typical dataset an example has a context and a desired completion (for example an English sentence and the French translation), and few-shot works by giving K examples of context and completion, and then one final example of context, with the model expected to provide the completion. … As indicated by the name, few-shot learning as described here for language models is related to few-shot learning as used in other contexts in ML [HYC01, VBL+16] – both involve learning based on a broad distribution of tasks (in this case implicit in the pre-training data) and then rapidly adapting to a new task.” Pg. 6, para 7 – providing examples for the machine learning model to learn would be considered training.
wherein the training comprises changing one or more parameters of the machine learning model based at least in part on the task description and the input and output labels included in the prompt; (not explicitly disclosed)
executing, via the at least one processor, the machine learning model, wherein the prompt is an input to the machine learning model, and wherein output of the machine learning model comprises computer-executable instructions for executing the task; ("the model is given a few demonstrations of the task at inference time as conditioning". [pg. 6, paragraph 7] The examples are input to the model to train it in the few shot setting. The trained model contains computer-executable instructions which can process further data)
executing, via the at least one processor, the task to transform a row of the plurality of rows from the initial format to a transformed format using the computer-executable instructions; ("For each task we generate a dataset of 2,000 random instances of the task and evaluate all models on those instances."[pg. 22, paragraph 2])
presenting, by a user interface, the transformed format of the row; (Fig. 3.17 shows outputs in bold.)
validating, by the user, the transformed format of the row; (Figure G.26 shows an example of using the method to format text input in a Symbol Insertion task to produce a clean output. (“Context -> Please unscramble the letters into a word, and write that word: r e!c.i p r o.c a/l = Target Completion -> reciprocal”.) Pg. 8, Section 2.2 clearly indicates that part of the data is held out to validate the result. For the Symbol Insertion task, this would be a validation of the format. Additionally, Table H.1 shows the scores, including for “Symbol Insertion.” See also Figures 3.1 and 4.1)
responsive to the validating, automatically applying the computer-executable instructions to each of the plurality of rows; and (Fig. 3.1 shows performance based on validation. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to automatically apply the best performing model.)
loading the each of the plurality of rows, after the validation, to a knowledge graph via a connector interface. (not explicitly disclosed by Brown)
Brown does not disclose sending the output, after the validation, to a knowledge graph, or changing parameters of the model during the (few-shot) training based on the task description and input and output labels.
Pfitzmann discloses: loading the each of the plurality of rows, after the validation, to a knowledge graph via a connector interface. (“Knowledge graphs are well-known data structures for representing information derived from a large corpus of documents. A knowledge graph essentially comprises nodes, which represent particular entities about which associated information is stored, interconnected by edges which represent defined relations between entities.” [0003]; See Abstract, which discloses that information is extracted from a corpus of documents and used to generate a knowledge graph. An interface to extract the information is implicitly disclosed.)
Brown and Pfitzmann are considered analogous art to the claimed invention because they disclose methods of extracting information from text. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Brown with a knowledge graph as disclosed by Pfitzmann. Doing so would have been beneficial to produce a searchable representation of the extracted information. (Pfitzmann, Abstract). Additionally, this combination falls under combining prior art elements according to known methods to yield predictable results or use of known technique to improve similar devices (methods, or products) in the same way. See MPEP 2141, KSR, 550 U.S. at 418, 82 USPQ2d at 1396.
Pfitzmann does not changing parameters of the model during the (few-shot) training based on the task description and input and output labels.
Lester discloses: wherein the training comprises changing one or more parameters of the machine learning model based at least in part on the task description and the input and output labels included in the prompt; (“[0104] The training example can include one or more examples and/or one or more task descriptions. The training process can involve supervised training or unsupervised training. Therefore, the training example can be a supervised example or an unsupervised example. In some implementations, the training example can include an example and a label, in which the label is a respective label for the example. Additionally and/or alternatively, the training example can be a fine-tuning example or a pre-training example.” – see also Fig. 4 which shows the meta-prompt is updated; see also [0091] and [0094])
Brown and Pfitzmann are considered analogous art to the claimed invention because they disclose methods of extracting information. Brown and Lester are considered analogous art to the claimed invention because they disclose methods of prompting machine learning models. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the combination with prompt tuning to include a meta-prompt in the model, and update its parameters based on a prompt gradient based on a task description and one or more examples as taught by Lester. Doing so would have been beneficial to obtain larger datasets (Lester [0040]), leverage a large model without issues of deployment (Lester [0041]), and to segregate data (Lester [0041]).
Regarding claim 2 in addition to the limitations of claim 1 Brown discloses: The method of claim 1, wherein the plurality of example transformations comprise: an input for a transformation; and an output for the transformation. ("sea otter => loutre de mer"[pg. 7, Figure 2.1, Few-shot section])
Regarding claim 3 in addition to the limitations of claim 1 Brown discloses: The method of claim 1, wherein the plurality of example transformations number three. (There are three examples (sea otter, peppermint, and plush girafe)[pg. 7, Figure 2.1, Few-shot section])
Regarding claim 4 in addition to the limitations of claim 1 Brown discloses: The method of claim 1, wherein the description of the task is prose. (“For some tasks (see Appendix G) we also use a natural language prompt in addition to (or for K = 0, instead of) demonstrations.”[pg. 10 paragraph 2])
Regarding claim 5 in addition to the limitations of claim 4 Brown discloses: The method of claim 4, further comprising: executing, via the at least one processor, natural language processing (NLP) on the description of the task, the plurality of example transformations and input and output labels, resulting in parsed text, wherein the prompt further comprises the parsed text. ("larger models are able to make increasingly effective use of in-context information, including both task examples and natural language task descriptions." [pg. 24, paragraph 4] The model uses the natural language task description to more effectively perform the task. It would be obvious to one of ordinary skill in the art that in order to use the natural language description, the model must use NLP and use the processed text as an input. Additionally the BPE encoding mapped in claim 1 reads on parsing all input text.)
Regarding claim 6 in addition to the limitations of claim 1 Brown discloses: The method of claim 1, further comprising: receiving, at the source system, feedback regarding accuracy of the execution of the task on the data using the computer-executable instruction; and retraining, via the at least one processor, the machine learning model using the feedback. ("we run evaluation on the clean-only examples and report the relative percent change between the clean score and the original score." "retrain the model on a corrected version of the training dataset." [pg. 44 paragraph 5, paragraph 4] Brown describes the evaluation of the model, specifically with regard to the training data. In this case, the authors did not retrain the model due to cost restraints, but a method wherein the model is retrained is disclosed.)
Claim 8 is a system claim with limitations corresponding to the limitations of Claim 1 and is rejected under similar rationale. Additionally, “at least one processor” and “a non-transitory computer-readable storage medium having instruction stored” of the Claim are taught by Brown (Brown discloses training a GPT-3 model which requires a processor and instructions stored in memory).
Claim 9 is a system claim with limitations corresponding to the limitations of Claim 2 and is rejected under similar rationale.
Claim 10 is a system claim with limitations corresponding to the limitations of Claim 3 and is rejected under similar rationale.
Claim 11 is a system claim with limitations corresponding to the limitations of Claim 4 and is rejected under similar rationale.
Claim 12 is a system claim with limitations corresponding to the limitations of Claim 5 and is rejected under similar rationale.
Claim 13 is a system claim with limitations corresponding to the limitations of Claim 6 and is rejected under similar rationale.
Claim 15 is a computer-readable storage medium claim with limitations corresponding to the limitations of Claim 1 and is rejected under similar rationale.
Claim 16 is a computer-readable storage medium claim with limitations corresponding to the limitations of Claim 2 and is rejected under similar rationale.
Claim 17 is a computer-readable storage medium claim with limitations corresponding to the limitations of Claim 3 and is rejected under similar rationale.
Claim 18 is a computer-readable storage medium claim with limitations corresponding to the limitations of Claim 4 and is rejected under similar rationale.
Claim 19 is a computer-readable storage medium claim with limitations corresponding to the limitations of Claim 5 and is rejected under similar rationale.
Claim 20 is a computer-readable storage medium claim with limitations corresponding to the limitations of Claim 6 and is rejected under similar rationale.
Conclusion
Applicant's amendment necessitated the new ground(s) 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 JON C MEIS whose telephone number is (703)756-1566. The examiner can normally be reached Monday - Thursday, 8:30 am - 5:30 pm EST.
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, Hai Phan can be reached on 571-272-6338. 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.
/JON CHRISTOPHER MEIS/Examiner, Art Unit 2654
/HAI PHAN/Supervisory Patent Examiner, Art Unit 2654
Prosecution Insights