DETAILED ACTION
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 .
Response to Arguments
Applicant’s arguments with respect to claims 1 - 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 § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 1, 3, 18, and 19 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claim 1 recites the limitation "SQL" in lines 8, 10, 14. There is insufficient antecedent basis for this limitation in the claim.
Claim 1 recites the limitation "API" in lines 9, 10, 14. There is insufficient antecedent basis for this limitation in the claim.
Claim 3 recites the limitation "SQL" in lines 1, 6. There is insufficient antecedent basis for this limitation in the claim.
Claim 3 recites the limitation "API" in lines 2, 6. There is insufficient antecedent basis for this limitation in the claim.
Claim 18 recites the limitation "SQL" in lines 9, 11, 15. There is insufficient antecedent basis for this limitation in the claim.
Claim 18 recites the limitation "API" in lines 10, 11, 15. There is insufficient antecedent basis for this limitation in the claim.
Claim 19 recites the limitation "SQL" in lines 9, 11, 15. There is insufficient antecedent basis for this limitation in the claim.
Claim 19 recites the limitation "API" in lines 10, 11, 15. There is insufficient antecedent basis for this limitation in the claim.
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention.
Claims 1 – 20 are rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the enablement requirement. The claims contains subject matter which was not described in the specification in such a way as to enable one skilled in the art to which it pertains, or with which it is most nearly connected, to make and/or use the invention. The newly added limitations, such as “prosodic features, Structured query language, structured metadata, etc” are not described in the specification.
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.
Claims 1 – 12, 15 – 20 are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Mishra (US PAP 2023/0368773).
As per claims 1, 18, 19, Mishra teaches a method for sustaining one or more semi- or fully-autonomous conversations between at least one human and at least one artificial intelligence agent, comprising:
(a) receiving first speech from a human; (b) converting the first speech to first text using a speech-to-text language model (“A virtual agent may be an automated service configured to communicate with client device 104 and/or the user thereof. The automated service may include one or more audio-to-text converters (e.g., configured to convert voice communications into corresponding alphanumeric text, etc.)” paragraph 30);
(c) analyzing prosodic features of the speech and encoding the features into structured metadata (“neural network, multi-layer perceptron, mem-resistive network, etc.), classifiers, large language modules, and/or the like, that may be trained by multiple sets of features structured into feature vectors according to a particular dimension (e.g., such as time, frequency, etc.).”; paragraphs 23, 73);
(d) generating, using a first generative language model, output containing SQL
commands and API calls based on the first text and prosodic metadata (“the queries may be automatically executed. For instance, a query may be defined using structured query language (SQL) schema to automatically obtain information from a database. One or more APIs (of interfaces 332) may be used to translate queries of domain-specific queries 328 into native code of a corresponding information source to automatically obtain requested information.”; paragraphs 23, 73);
(e) extracting and executing the SQL commands and API calls on a first database, the database being a production database or a copy of a production database (“For instance, a query may be defined using structured query language (SQL) schema to automatically obtain information from a database. One or more APIs (of interfaces 332) may be used to translate queries of domain-specific queries 328 into native code of a corresponding information source to automatically obtain requested information. The executable code may obtain specific information (e.g., such as store hours, names, etc.) or may be configured to obtain any information associated with client device 336 or the user thereof (e.g., such as using a web crawler, database crawler, etc.).”; paragraphs 23, 73, 76);
(f) generating, using a second generative language model, dialogue text responsive to (“the one or more machine-learning models may include speech-to-text models, natural language understanding (NLU) machine-learning models, large language models and/or generative machine-learning models (such as generative adversarial networks, etc.) configured to generate communications from one or more information sources and based on an input communication, text-to-speech, etc.”; paragraphs 21 - 23,41, 113):
(i) the conversation history with the human (“generate a personal virtual agent for a particular user device and/or the user thereof based on historical interactions between the user and the domain”; paragraph 20),
(ii) results from the SQL and API command execution (paragraphs 23, 73), and
(iii) the prosodic metadata (paragraph 32);
(g) converting the dialogue text to second speech using a text-to-speech language model (“the one or more machine-learning models may include speech-to-text models, natural language understanding (NLU) machine-learning models, large language models and/or generative machine-learning models (such as generative adversarial networks, etc.) configured to generate communications from one or more information sources and based on an input communication, text-to-speech”; paragraphs 23, 73, 113); and
(h) transmitting the second speech to the human (paragraphs 66, 70 – 73).
As per claim 2, Mishra further discloses analyzing prosodic features
comprises:
(a) analyzing pitch, tempo, and pauses in the speech (paragraphs 32, 95);
(b) encoding these features into a structured metadata format comprising: (i) timestamped prosodic events, (ii) feature vectors aligned with words, (iii) emotion classification data (paragraphs 45 – 47, 95).
As per claim 3, Mishra further discloses generating output containing SQL
commands and API calls comprises:
(a) Converting the input text, including any conversation history, and prosodic features into numerical vectors(paragraphs 32, 95);
(b) applying attention mechanisms over these vectors; (c) generating output consisting of in-line SQL commands and API calls; (d) further validating these commands for validity (paragraphs 23, 73).
As per claim 4, Mishra further discloses executing database operations
includes at least one of modifying one or more records in the first database, cancelling or starting a new order for the human, and retrieval, addition, or modification of customer data (“the first device or the user thereof may communicate connect a customer service network of a home improvement domain. The computing device may connect the first device to the personal virtual agent trained for the first device for the home improvement domain. The personal virtual agent may communicate with the first device in place of a human or virtual agent of the home improvement domain to access any service or operation of the home improve domain (e.g., obtain product information, obtain account information, pay balances, return products or services, etc.).”; paragraphs 50, 73, 115).
As per claim 5, Mishra further discloses allowing the generative language model to propose actions responsive to the first text, wherein said actions are: (a) stored in the first database, (b) validated against predefined safety rules, (c) logged in the diff log for review, and (d) diffs are written to the production database after human approval (paragraphs 73, 111).
As per claim 6, Mishra further discloses the generative language model is
configured to maintain an ongoing memory of previous conversations with that customer or any subset of customers, utilizing: human- and/or machine-readable data structures, a language model finetuning process, a language model training process, a language model prefix tuning process, one or more language model prompts, or a combination thereof (paragraphs 45, 60 – 63).
As per claim 7, Mishra further discloses repeating steps (a)-(e) until a predetermined condition is met, wherein the predetermined condition includes at least one of: (a) completion of a transaction, (b) resolution of a customer inquiry, or
(c) explicit termination by the human(“the first device or the user thereof may communicate connect a customer service network of a home improvement domain. The computing device may connect the first device to the personal virtual agent trained for the first device for the home improvement domain. The personal virtual agent may communicate with the first device in place of a human or virtual agent of the home improvement domain to access any service or operation of the home improve domain (e.g., obtain product information, obtain account information, pay balances, return products or services, etc.).”; paragraphs 50, 73, 115).
As per claim 8, Mishra further discloses after generating the dialogue text, allowing an agent to send a message to the human, wherein: (a) the message is optionally reviewed before transmission, (b) can be modified or recalled if needed (“a query may be transmitted to client device 336 over one or more communication channels (e.g., email, telephone, text messaging, instant messaging, a proprietary communication protocol, etc.). Upon being received by client device 336, the user thereof may manually provide the information that may satisfy the query and transmit the information to automated service generator 304 via interfaces 332 using the same or a different communication channel.”; paragraphs 74, 79, 83).
As per claims 9, 20, Mishra further discloses text, via text message or a web-based text chat, is received instead of speech in step (a); text, via text message or a web-based text chat, is transmitted instead of speech in step (e); and steps (b) and (d) are optionally bypassed (paragraphs 74, 79, 83).
As per claim 10, Mishra further discloses maintaining a separate production database and a cache database, the cache database being a deep or shallow copy of the production database (paragraphs 116, 117).
As per claim 11, Mishra further discloses the generative language model is configured to: (i) incorporate company policies through fine-tuning, retrieval-augmented generation, prompt engineering, or a combination thereof that determine: persona,
credit issuance thresholds, refund authorization levels, escalation criteria, conversation style preferences including formality level and empathy expression, or a combination thereof; (ii) dynamically adjust these parameters based on: customer satisfaction metrics, business outcomes, compliance requirements (“if the user previously connected to a domain to discuss a faulty object, the personal virtual may generate communications asking about the object, the resolution of the previous connection, whether the current intent of the user is related to the previous connection, and/or any other detail or communication provided during the previous connection to ensure that the user's intent was resolved satisfactorily.”; paragraphs 85, 115, 126).
As per claim 12, Mishra further discloses allowing the artificial intelligence
agent to generate or execute or reference functions that directly interact with the cache database (paragraphs 116, 117).
As per claim 15, Mishra further discloses delaying after generating second text and before transmitting the second speech (paragraphs 88 – 97).
As per claim 16, Mishra further discloses allowing a human agent to prevent
further interaction between the human and the artificial intelligence agent (paragraphs 88-97, 115, 126).
As per claim 17, Mishra further discloses preventing further interaction includes
allowing the human agent to communicate with the human via a text message (paragraphs 74, 79, 83, 88-97, 115, 126).
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.
Claims 13, 14 are rejected under 35 U.S.C. 103 as being unpatentable over Mishra (US PAP 2023/0368773) in view of Desai et al. (US patent 11,706,345).
As per claim 13, Li et al. do not specifically teach a human or machine reviewing one or more diffs in batches, where each diff defines one or more changes in the cache database from the production database.
Desai et al. teach that the delayed process timing is not met during a “batch window.” The delayed process or batch window timing may be any appropriate time period, such as one day, one hour, one minute, etc. In some embodiments, meeting 604 the batch timing may include the passage of a particular amount of time since the end of the previous delayed process period, or may be met by a certain real-world time (e.g., every 4 clock hours; or at 6 am, 9 am, noon, 3 pm, 6 pm, and midnight, etc.)[col.28, line 65 -col.29, line 7].
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to review one or more diffs in batches as taught by Desai in Mishra, because that would help improve the operation of a contact center service offered by a provider network (col.2, lines 6, 7).
As per claim 14, Mishra in view of Desai further disclose allowing the changes to be written to the production database [Desai; col.28, line 65 -col.29, line 7].
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 LEONARD SAINT-CYR whose telephone number is (571)272-4247. The examiner can normally be reached Monday- Friday.
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/LEONARD SAINT-CYR/Primary Examiner, Art Unit 2658