Detailed Office Action
1. This communication is being filed in response to the initial submission having a mailing date of (05/24/2024), in which a three (3) month Shortened Statutory Period for Response has been set.
Notice of Pre-AIA or AIA Status
2. The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Acknowledgements
3. Upon new entry, claims (1 -30) appear pending for examination, of which (1, 11, 21) being the three (3) parallel running independent claims on record.
Information Disclosure Statement
4. The Information Disclosure Statement (IDS)s that was/were submitted on 11/14/2025, is/are in compliance with the provisions of 37 CFR 1.97, being considered by the Examiner.
Drawings
5. The submitted Drawings on date (05/24/2024) has been accepted and considered under the 37 CFR 1.121 (d).
Claim interpretation
6. For the sole purpose of examination, and under the broadest reasonable interpretation (BRI), consistent with the instant specification and the common knowledge of one of ordinary skill in the art, the below list of terms/limitations will be considered to read as:
_ Term “chatbot” is defined, and will be read as – an application designed to simulate human conversation through text/speech or voice interactions.
_ Term “hallucination” is defined, and will be read as – falsehoods happen whenever the generated content—often subtly—deviates from reality or context.
6.1. Further, the undersigned notes that when Applicant describes the features and/or components of the claim-invention, they’re usually followed by a set of passive verbs/terms such as “evaluating/receiving/comparing… and the like it”, indicating that a function is performed without requiring of any additional functional structure and/or method as a limitation on the claim itself. It is clear that such claim construction does not further limit the claims, and does not require a separate reason for rejection; (see also MPEP 2111.04 for more information). The clause may be given some weight to the extent it provides "meaning and purpose” to the claimed invention, but not when “it simply expresses the intended result” of the invention. While a cumulative rejection of such language is provided below for purposes of compact prosecution, Examiner undersigned suggests amend such claim language to recite clear limitations corresponding to the subject matter of the claim.
6.2. As a matter of claim interpretation in general terms, the Office gives the claims their broadest reasonable interpretation (BRI) consistent with the specification and the common knowledge. See “In re Morris,” 127 F.3d 1048, 1054 (Fed. Cir. 1997); and see also "In re Am. Acad. Of Sci. Tech Ctr.”, 367 F.3d 1359, 1369 (Fed. Cir. 2004); …and while the Office interprets the presented claims broadly but reasonably in light of the specification, we nonetheless must not import limitations from the specification into the claims. See also “Phillips v. A WH Corp.”, 415 F.3d 1303, 1323 (Fed. Cir. 2005).
Claim rejection section
35 USC 103
7. 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.
7.1. The factual inquiries set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ
459 (1966), that are applied 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 non-obviousness.
7.2. Claims (1 -30) is/are rejected under 35 U.S.C. 103, as being unpatentable over Weizenbaum; et al. (ELIZA – a computer program for the study of natural language communication between man and machine; 1966; hereafter “Weizenbaum”) in view of Amine; et al (US 12,487,818 B2; hereafter “Amine”).
Claim 1. Weizenbaum discloses the invention substantially as claimed - A computer-implemented method, executed on a computing device, comprising: (a system able to make certain natural language conversation between humans and computer possible, as shown in Figs. (1 -2); by analyzing input content on the basis of decomposition rules which are triggered by key words appearing in the input text, and generate responses accordantly, given assembly rules, employing data processing of the same, via evaluation/ranking and comparing data content, [pages 36; 39]; and able to detect falsehoods (i.e. hallucination, contradictions, and the like it) in at least [page 43]).
Given the teachings of Weizenbaum; et al. as a whole, and under the obvious assumption and purpose of his papers, it is noted that some of the functional steps and/or components as listed (i.e. no schematic/functional architecture disclosed), are missed or not fully described in the papers.
For the purpose of additional clarification, and in the same field of endeavor, Amine similarly discloses – (e.g. an interactive chatbot functional architecture (Figs. 2. 4) and methodology (Figs. 3, 5) of the same, employing an improved data content retrieving technique [3: 04]; and a trained machine learning AI model, able to evaluate, process, and compare/ranking/scoring chatbot content/models, [4: 08; 4: 36; 5: 40], avoiding hallucinations/falsehoods [3: 05; 6: 07]; able to generate response (208, 402) that may include text, summaries, code, dialogs, other extra contents, etc, in interactive software and API documentation chatbot system of Figs. (2, 4); [Amine; 5: 40; 5: 60].)
Amine specifically discloses - providing evaluation content to a target chatbot, wherein the evaluation content includes a plurality of inquiries and a plurality of anticipated responses; (e.g. see method steps of Figs. (3, 5); [Amine]);
processing the plurality of inquiries on the target chatbot; (e.g. see method steps of Figs. (3, 5); [Amine]);
receiving a plurality of generated responses from the target chatbot in response to the plurality of inquiries; (e.g. see method steps of Figs. (3, 5); [Amine]);
and comparing the plurality of generated responses received from the target chatbot to the plurality of anticipated responses included within the evaluation content; (e.g. see method steps of Figs. (3, 5); including evaluate, process, and compare/ranking/scoring chatbot content/models, [Amine; 4: 08; 4: 36; 5: 40] and produce output return in form of text, code and/or both; [Amine 5: 18]).
Therefore, it would have been obvious to one skilled in the art before the effective filing date of the invention, to modify the implementation of Weizenbaum, with the codec architecture of Amine, in order to provide (e.g. an improved/interactive based chatbot & deep learning technique, for software and APIs; [Amine; 2: 65].)
Claim 2. Weizenbaum/Amine discloses - The computer-implemented method of claim 1 wherein the evaluation content includes one or more of: a CSV (Comma Separated File) file; a JSON (JavaScript Object Notation) file; an XML (eXtensible Markup Language) file; a TSV (Tab-Separated Values) file; a PSV (Pipe-Separated Values) file; and a SSV (Space-Separated Values) file; (e.g. see plurality of input data (i.e. file/text formats) types (408); [Amine; 5: 53]; the same motivation applies herein.)
Examiner’s note in taken, regarding the common use of cited above “file/text formats”, way before the invention was made/filed.
Claim 3. Weizenbaum/Amine discloses - The computer-implemented method of claim 1 wherein the target chatbot includes one or more of: a rule-based chatbot; an AI-based chatbot; a hybrid chatbot; a conversational chatbot; a contextual chatbot; a voice-activated chatbot; a service / action-based chatbot; a social media chatbot; a messaging platform chatbot; and an enterprise chatbot. (The same rationale and motivation apply as given to Claim 1 above. In addition, see conversational and rule-based chatbot [Weizenbaum], and analogous conversational and AI based model in [Amine].)
Claim 4. Weizenbaum/Amine discloses -The computer-implemented method of claim 1 wherein comparing the plurality of generated responses received from the target chatbot to the plurality of anticipated responses included within the evaluation content includes: (the same rationale and motivation apply as given to Claim 1 above);
determining the accuracy of the target chatbot by comparing the plurality of generated responses received from the target chatbot to the plurality of anticipated responses included within the evaluation content; (e.g. see data comparison via event ranking technique; [Weizenbaum; page 39]; and see model training, able to compare/ranking/scoring chatbot content/models, [Amine; 4: 08 -36; 5: 40]; the same motivation applies herein.)
Claim 5. Weizenbaum/Amine discloses - The computer-implemented method of claim 4 further comprising: revising one or more algorithms/models associated with the target chatbot based, at least in part, upon a determined accuracy of the target chatbot; (e.g. see model training, able to compare/ranking/scoring chatbot content/models, [Amine; 4: 08 -36; 5: 40]; the same motivation applies herein.)
Claim 6. Weizenbaum/Amine discloses - The computer-implemented method of claim 1 wherein comparing the plurality of generated responses received from the target chatbot to the plurality of anticipated responses included within the evaluation content includes: (the same rationale and motivation apply as given to Claim 1 above);
determining if the target chatbot is hallucinating by comparing the plurality of generated responses received from the target chatbot to the plurality of anticipated responses included within the evaluation content; (e.g. see detecting and avoiding hallucinations/falsehoods entries [3: 05; 6: 07]; the same motivation applies herein.)
Claim 7. Weizenbaum/Amine discloses - The computer-implemented method of claim 6 further comprising: revising one or more algorithms / models associated with the target chatbot based, at least in part, upon an hallucination status of the target chatbot; (e.g. see detecting and avoiding hallucinations/falsehoods entries [Amine 3: 05; 6: 07]; the same motivation applies herein.)
Claim 8. Weizenbaum/Amine discloses - The computer-implemented method of claim 1 further comprising: generating a report concerning the quality and/or accuracy of the generated responses received from the target chatbot; (e.g. see how Amine resolves the falsehood issues employing deep learning models; [Amine; 3: 07; 5: 09]; the same motivation applies herein.)
Claim 9. Weizenbaum/Amine discloses - The computer-implemented method of claim 1 further comprising: identifying a chatbot to be evaluated for accuracy, thus defining the target chatbot; (e.g. see trained machine learning AI model, able to evaluate, process, and compare/ranking/scoring chatbot content/models, [4: 08; 4: 36; 5: 40]; the same motivation applies herein.)
Claim 10. Weizenbaum/Amine discloses - The computer-implemented method of claim 1 further comprising: defining the evaluation content; (e.g. see evaluation via comparing/ranking/scoring chatbot content/models, [Amine; 4: 08; 4: 36; 5: 40] and produce output return in form of text, code and/or both; [Amine 5: 18]; the same motivation applies herein.)
Claim 11. Weizenbaum/Amine discloses - A computer program product residing on a computer readable medium having a plurality of instructions stored thereon which, when executed by a processor, cause the processor to perform operations comprising: providing evaluation content to a target chatbot, wherein the evaluation content includes a plurality of inquiries and a plurality of anticipated responses; processing the plurality of inquiries on the target chatbot; receiving a plurality of generated responses from the target chatbot in response to the plurality of inquiries; and comparing the plurality of generated responses received from the target chatbot to the plurality of anticipated responses included within the evaluation content. (Current lists all the same elements as recite in Claim 1 above, but in “CRM form” instead, and is/are therefore on the same premise.)
Claim 12. Weizenbaum/Amine discloses - The computer program product of claim 11 wherein the evaluation content includes one or more of: a CSV (Comma Separated File) file; a JSON (JavaScript Object Notation) file; an XML (eXtensible Markup Language) file; a TSV (Tab-Separated Values) file; a PSV (Pipe-Separated Values) file; and a SSV (Space-Separated Values) file. (The same rationale and motivation apply as given to Claim 2 above.)
Claim 13. Weizenbaum/Amine discloses - The computer program product of claim 11 wherein the target chatbot includes one or more of: a rule-based chatbot; an AI-based chatbot; a hybrid chatbot; a conversational chatbot; a contextual chatbot; a voice-activated chatbot; a service / action-based chatbot; a social media chatbot; a messaging platform chatbot; and an enterprise chatbot. (The same rationale and motivation apply as given to Claim 3 above.)
Claim 14. Weizenbaum/Amine discloses - The computer program product of claim 11 wherein comparing the plurality of generated responses received from the target chatbot to the plurality of anticipated responses included within the evaluation content includes: determining the accuracy of the target chatbot by comparing the plurality of generated responses received from the target chatbot to the plurality of anticipated responses included within the evaluation content. (The same rationale and motivation apply as given to Claim 5 above.)
Claim 15. Weizenbaum/Amine discloses - The computer program product of claim 14 further comprising: revising one or more algorithms / models associated with the target chatbot based, at least in part, upon a determined accuracy of the target chatbot. (The same rationale and motivation apply as given to Claim 5 above.)
Claim 16. Weizenbaum/Amine discloses - The computer program product of claim 11 wherein comparing the plurality of generated responses received from the target chatbot to the plurality of anticipated responses included within the evaluation content includes: determining if the target chatbot is hallucinating by comparing the plurality of generated responses received from the target chatbot to the plurality of anticipated responses included within the evaluation content. (The same rationale and motivation apply as given to Claim 6 above.)
Claim 17. Weizenbaum/Amine discloses - The computer program product of claim 16 further comprising: revising one or more algorithms / models associated with the target chatbot based, at least in part, upon an hallucination status of the target chatbot. (The same rationale and motivation apply as given to Claim 7 above.)
Claim 18. Weizenbaum/Amine discloses - The computer program product of claim 11 further comprising: generating a report concerning the quality and/or accuracy of the generated responses received from the target chatbot. (The same rationale and motivation apply as given to Claim 8 above.)
Claim 19. Weizenbaum/Amine discloses - The computer program product of claim 11 further comprising: identifying a chatbot to be evaluated for accuracy, thus defining the target chatbot. (The same rationale and motivation apply as given to Claim 9 above.)
Claim 20. Weizenbaum/Amine discloses - The computer program product of claim 11 further comprising: defining the evaluation content. (The same rationale and motivation apply as given to Claim 10 above.)
Claim 21. Weizenbaum/Amine discloses - A computing system including a processor and memory configured to perform operations comprising: providing evaluation content to a target chatbot, wherein the evaluation content includes a plurality of inquiries and a plurality of anticipated responses; processing the plurality of inquiries on the target chatbot; receiving a plurality of generated responses from the target chatbot in response to the plurality of inquiries; and comparing the plurality of generated responses received from the target chatbot to the plurality of anticipated responses included within the evaluation content. (Current lists all the same elements as recite in Claim 1 above, but in “system form” instead, and is/are therefore on the same premise.)
Claim 22. Weizenbaum/Amine discloses - The computing system of claim 21 wherein the evaluation content includes one or more of: a CSV (Comma Separated File) file; a JSON (JavaScript Object Notation) file; an XML (eXtensible Markup Language) file; a TSV (Tab-Separated Values) file; a PSV (Pipe-Separated Values) file; and a SSV (Space-Separated Values) file. (The same rationale and motivation apply as given to Claim 2 above.)
Claim 23. Weizenbaum/Amine discloses - The computing system of claim 21 wherein the target chatbot includes one or more of: a rule-based chatbot; an AI-based chatbot; a hybrid chatbot; a conversational chatbot; a contextual chatbot; a voice-activated chatbot; a service / action-based chatbot; a social media chatbot; a messaging platform chatbot; and an enterprise chatbot. (The same rationale and motivation apply as given to Claim 3 above.)
Claim 24. Weizenbaum/Amine discloses - The computing system of claim 21 wherein comparing the plurality of generated responses received from the target chatbot to the plurality of anticipated responses included within the evaluation content includes: determining the accuracy of the target chatbot by comparing the plurality of generated responses received from the target chatbot to the plurality of anticipated responses included within the evaluation content. (The same rationale and motivation apply as given to Claim 4 above)
Claim 25. Weizenbaum/Amine discloses - The computing system of claim 24 further comprising: revising one or more algorithms / models associated with the target chatbot based, at least in part, upon a determined accuracy of the target chatbot. (The same rationale and motivation apply as given to Claim 5 above.)
Claim 26. Weizenbaum/Amine discloses - The computing system of claim 21 wherein comparing the plurality of generated responses received from the target chatbot to the plurality of anticipated responses included within the evaluation content includes: determining if the target chatbot is hallucinating by comparing the plurality of generated responses received from the target chatbot to the plurality of anticipated responses included within the evaluation content. (The same rationale and motivation apply as given to Claim 6 above.)
Claim 27. Weizenbaum/Amine discloses - The computing system of claim 26 further comprising: revising one or more algorithms / models associated with the target chatbot based, at least in part, upon an hallucination status of the target chatbot. (The same rationale and motivation apply as given to Claim 7 above.)
Claim 28. Weizenbaum/Amine discloses - The computing system of claim 21 further comprising: generating a report concerning the quality and/or accuracy of the generated responses received from the target chatbot. (The same rationale and motivation apply as given to Claim 8 above.)
Claim 29. Weizenbaum/Amine discloses - The computing system of claim 21 further comprising: identifying a chatbot to be evaluated for accuracy, thus defining the target chatbot. (The same rationale and motivation apply as given to Claim 9 above.)
Claim 30. Weizenbaum/Amine discloses - The computing system of claim 21 further comprising: defining the evaluation content. (The same rationale and motivation apply as given to Claim 10 above.)
Prior Art Citations
8. The following List of prior art, made of record and not relied upon, is/are considered
pertinent to applicant's disclosure:
8.1. Patent documentation
US 11,431,660 B1 G06F3/04847; G06F40/279; G06F40/30; Leeds; et al.
US 12,487,818 B2 G06F16/3329; G06F8/73 Amine; et al.
US 12,554,626 B2 G06F11/3688; G06F11/3692; Pryzant; et al.
8.2. Non-Patent documentation:
_ ELIZA – a computer program for the study of natural language communication between man and machine; Weizenbaum -1966.
_ Chatbots greetings to humans-computer communication; Pereira – 2016.
_ Recipes for building an open-domain chatbot; Roller - April-2020
_ ELIZA reanimated; Lane – 2025.
CONCLUSIONS
9. Any inquiry concerning this communication or earlier communications from the examiner should be directed to LUIS PEREZ-FUENTES (luis.perez-fuentes@uspto.gov) whose telephone number is (571) 270 -1168. The examiner can normally be reached on Monday-Friday 8am-5pm. If attempts to reach the examiner by telephone are unsuccessful, the examiner's supervisor, WILLIAM VAUGHN can be reached on (571) 272-3922. The fax phone number for the organization where this application or proceeding is assigned is (571) 272 -3922. Information regarding the status of an application may be obtained from the Patent Application Information Retrieval (PAIR) system. Status information for published applications may be obtained from either Private PAIR or Public PAIR. Status information for unpublished applications is available through Private PAIR only. For more information about the PAIR system, http://pair-direct.uspto.gov. Should you have questions on access to the Private PAIR system, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative or access to the automated system, please call (800) 786 -9199 (USA OR CANADA) or (571) 272 -1000.
/LUIS PEREZ-FUENTES/
Primary Examiner, Art Unit 2481.
Prosecution Insights