DETAILED ACTION
This action is in response to the filing of 7-27-2023. Claims 1-10 are pending and have been considered below:
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 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.
Claims 1-3, 5-8 and 10 is/are rejected under 35 U.S.C. 103 as being unpatentable over Appel et al. (“Appel” 20230419162 A1) in view of Anthony et al. (“Anthony” 20250005224 A1) and Polleri et al. (“Polleri” 20210081819 A1).
Claim 1: Appel discloses a system of assisting in building machine learning model without programming, comprising
an artificial intelligence platform, configured to receive a query message through an application programming interface, provide the query message (Paragraphs 24 and 39; provide a query for task determination);
comprising: an operation interface, configured to provide a machine learning model building operation area (Appel: Figure 3 and Paragraphs 8 and 20; model composer);
a non-transitory computer readable storage medium, configured to store computer readable instructions (Appel: Figure 3 and Paragraphs 42 and 50);
and a hardware processor, electrically connected to the non-transitory computer readable storage medium and the operation interface, and configured to execute the computer readable instructions to make machine learning model building host perform(Appel: Figure 3 and Paragraphs 41; processor):
Appel may not explicitly disclose input to a large language model to generate an answer message, and transmit the answer message through the application programming interface; and a machine learning model building host;
Anthony is provided because it discloses a prompt(input) into a large language model (Paragraph 6, the LLM provides a response to the API (Paragraph 44-45), and further the response is provided to a machine learning algorithm (Paragraph 45-46) in order to provide model assistance.
Therefore it would have been obvious to one having ordinary skill in the art before the effective filling date of the claimed invention to apply a known technique to a known device ready for improvement and incorporate Large language models for inputs in Appel. One would have been motivated to provide a large language model because it provides a useful and extensive knowledge base for implementing a response/buildout.
Appel also may not explicitly disclose each feature for providing an operation and a description of each of step processes of building a machine learning model on the machine learning model building operation area; providing an interactive question-and-answer area corresponding to one of the step processes being executed, through an operation interface, wherein the Interactive question-and-answer area comprises a query input part and a question and answer display part; receiving the query message on the query input part, transmitting the query message through an application programming interface, and displaying the query message on the question and answer display part; and receiving the answer message through the application programming interface, and displaying the answer message on the question and answer display part (Appel: Paragraph 25; provides query the user and response capability for a step of the process and Figure 2:222; Paragraph 34; question/answer for results step).
Anthony previously incorporated further discloses a content gate that provides interface area for question and answer functionality (Paragraph 43) and further provides step through prompts for configuring a model (Figure 3 and Paragraphs 54-64).
One would have been motivated to provide an interface with question and answer functionality because this will provide a well-known system for implementing feedback allowing an improved user experience.
Polleri is also provided because it discloses a functionality where question and answers are provided (abstract) for a model composition engine, and allows different parameters to be set up for construction (Paragraph 46), further the user can provide multiple inputs (question/answer/query) at steps within the process which assist in implementation of the model (Paragraph 105-106).
Therefore it would have been obvious to one having ordinary skill in the art before the effective filling date of the claimed invention to apply a known technique to a known device ready for improvement and incorporate the functionality of receiving inputs (i.e. chatbot interface feedback) during a process of building a model in order to provide responses in Appel. One would have been motivated to provide the additional response mechanisms because an intuitive interface can assist users in constructing a machine learning application through a series of queries (Polleri: Paragraph 5).
Claim 2: Appel, Anthony and Polleri disclose a system of assisting in building machine learning model without programming according to claim 1, wherein the machine learning model building host performs: pre-building a host language model (Polleri: Paragraphs 78-79); and providing the answer message to the host language model to perform a word parsing, to parse at least one label suggestion content or at least one feature word (Polleri: Figure 2 and Paragraphs 80-82 and Anthony: Paragraphs 43-45 and 53-64; provide model that receives the input and provides response specific to task (feature)).
Claim 3: Appel, Anthony and Polleri disclose a system of assisting in building machine learning model without programming according to claim 2, wherein the machine learning model building host performs: when the answer message is displayed on the question and answer display part, highlighting the at least one label suggestion content parsed from the answer message (Anthony: Paragraph 52; provide color coded highlight of response(label)).
Claim 5: Appel, Anthony and Polleri disclose a system of assisting in building machine learning model without programming according to claim 1, wherein the step processes of the machine learning model comprise a data receiving step (Appel: Figure 2:202), a data scrubbing step (Appel: Paragraph 20; correction), a data feature processing step (Appel: Figure 2:204/206), a machine learning model training step, and a machine learning model evaluation step (Appel: Figure 2:218 and Paragraph 33; test results), and a machine learning model deployment and inference simulation step (Appel: Figure 2:220-226 and Paragraphs 33-38).
Claim 6 is similar in scope to claim 1 and therefore rejected under the same rationale (Appel: Paragraph 5; computer implemented method).
Claim 7 is similar in scope to claim 2 and therefore rejected under the same rationale.
Claim 8 is similar in scope to claim 3 and therefore rejected under the same rationale.
Claim 10 is similar in scope to claim 5 and therefore rejected under the same rationale.
Claims 4 and 9 is/are rejected under 35 U.S.C. 103 as being unpatentable over Appel et al. (“Appel” 20230419162 A1), Anthony et al. (“Anthony” 20250005224 A1) and Polleri et al. (“Polleri” 20210081819 A1) in further view of Zaydman et al. (“Zaydman” 20160026439 A1).
Claim 4: Appel, Anthony and Polleri disclose a system of assisting in building machine learning model without programming according to claim 2, wherein the machine learning model building host performs (Appel: Figure 3 and Paragraphs 8 and 20; model composer): however may not explicitly disclose each feature building a hyperlink between the at least one feature word parsed from the answer message to an operation component displayed on the machine learning model building operation area and corresponding to the step process being executed; when the answer message is displayed on the question and answer display part, displaying the at least one feature word parsed from the answer message, through the hyperlink; and when one of the at least one feature word is clicked, executing the one of the step processes corresponding to the operation component (Anthony: Paragraphs 43-45 and 53-64; provide model that receives the input and provides response specific to task (feature)).
Zaydman is provided because it discloses an equivalent functionality of a question/answer area with a widget line (Paragraph 18). Within the widget a focus area is determined (Paragraphs 21-23) and further the widget area allows for selection (hyperlink) in order to perform operations pertaining to the determined area/step, for execution (or completion)(Paragraphs 24-25 and 27). This functionality could be incorporated with the model composition and query features of the modified Appel.
Therefore it would have been obvious to one having ordinary skill in the art before the effective filling date of the claimed invention to apply a known technique to a known device ready for improvement and incorporate the linking functionality for model composition steps found in the modified Appel. One would have been motivated to provide the functionality because the features provide enhanced help and user convenience (Zaydman: Paragraph 4).
Claim 9 is similar in scope to claim 4 and therefore rejected under the same rationale.
Conclusion
The prior art made of record and not relied upon is considered pertinent to Applicant’s disclosure:
6879971 B1 KEELER
Applicant is required under 37 C.F.R. § 1.111(c) to consider these references fully when responding to this action.
It is noted that any citation to specific pages, columns, lines, or figures in the prior art references and any interpretation of the references should not be considered to be limiting in any way. A reference is relevant for all it contains and may be relied upon for all that it would have reasonably suggested to one having ordinary skill in the art. In re Heck, 699 F.2d 1331, 1332-33, 216 U.S.P.Q. 1038, 1039 (Fed. Cir. 1983) (quoting In re Lemelson, 397 F.2d 1006, 1009, 158 U.S.P.Q. 275, 277 (C.C.P.A. 1968)).
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Any inquiry concerning this communication or earlier communications from the examiner should be directed to SHERROD KEATON whose telephone number is 571-270-1697. The examiner can normally be reached 9:30am to 5:00pm.
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/SHERROD L KEATON/Primary Examiner, Art Unit 2148
3-10-2026
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