DETAILED ACTION
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
This is in response to the correspondence filed on 11/22/23. Claims 1-20 are still pending and have been considered below.
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 2, 3, 5, 6, 8, 12 and 13 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.
Claims 2, 5 and 8 recite the limitation "the stored known harmful input phrases" throughout the clams. There is insufficient antecedent basis for this limitation in the claims. Examiner notes that the preceding claim language does not appear to establish any first instance of “stored known harmful input phrases”.
Claims 2, 3, 6, 12 and 13 recite the limitation "the input phrase" throughout the clams. There is insufficient antecedent basis for this limitation in the claims. Examiner notes that the preceding claim language appears to establish separate and distinct instances of various types of “input phrase(s)” (ie. a received input phrase, a known harmful input phrase, etc.).
Claim 8 recites the limitation "the first set of similarity values" and "the second set of similarity values" throughout the claim. There is insufficient antecedent basis for this limitation in the claim. Examiner notes that the preceding claim language appears to establish at least two separate and distinct instances of a first and second set of similarity values.
Claim Rejections - 35 USC § 102
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 (i.e., changing from AIA to pre-AIA ) 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 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, 3, 4, 9-11, 13, 15-18 and 20 is/are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Hayes (2025/0045531).
Claim 1: Hayes discloses a computer-implemented method comprising:
receiving a request for an input phrase to be responded to by a generative artificial intelligence (AI) model(user prompts/input data that the server receive from users) [pages 3-4, paragraphs 0034-0035];
comparing to the received input phrase to a known harmful input phrase and a plurality of paraphrases associated with the known harmful input phrase in a multi-stage process to determine that the received input phrase is to be provided to the generative AI model(pre-processing LLM receives user prompt and determines if jailbreaking/malicious prompts, out of scope questions, harmful language is present using generative AI framework that is separated into at least three different processing stages) [page 5, paragraphs 0042-0043];
providing the received input phrase to the generative AI model(each updated output from the previous stage is provided as input to the next stage of the AI framework) [page 5, paragraphs 0042 & 0046-0047]; and
generating a response by at least in part on an output of the generative AI model(generating an answer to user prompt) [page 5, paragraph 0048].
Claim 3: Hayes discloses the computer-implemented method of claim 1, wherein the input phrase is in a format of one of text, audio, or video [page 4, paragraphs 0035 & 0040 | page 6, paragraph 0055].
Claim 4: Hayes discloses a computer-implemented method comprising:
receiving a request for an input phrase to be responded to by a generative artificial intelligence (AI) model [pages 3-4, paragraphs 0034-0035];
comparing to the received input phrase to at least one known harmful input phrase to determine that the received input phrase is to be provided to the generative AI model [page 5, paragraphs 0042-0043];
providing the received input phrase to the generative AI model [page 5, paragraphs 0042 & 0046-0047]; and
generating a response by at least in part on an output of the generative AI model [page 5, paragraph 0048].
Claim 9: Hayes discloses the computer-implemented method of claim 4, further comprising: receiving one or more known harmful input phrases; generating one or more paraphrases for each known harmful input phrase using one or more paraphrase generator models(process training data to determine next-word probabilities that it may use to later process future input data); and storing the known harmful input phrases and the generated one or more paraphrases for each known harmful input phrases(guardrails, predetermined rules/restrictions and probabilities of entire/partial phrases stored in associated database) [page 4, paragraphs 0035 & 0037-0038 | page 5, paragraph 0043].
Claim 10: Hayes discloses the computer-implemented method of claim 9, further comprising: storing a denial response for one or more of the received harmful input phrases [page 6, paragraph 0052].
Claim 11: Hayes discloses the computer-implemented method of claim 4, further comprising: verifying the output of the generative AI model is not a hallucination [page 5, paragraph 0048].
Claim 13: Hayes discloses the computer-implemented method of claim 4, wherein the input phrase is in a format of one of text, audio, or video [page 4, paragraphs 0035 & 0040 | page 6, paragraph 0055].
Claim 15: Hayes discloses the computer-implemented method of claim 4, further comprising: translating the received input phrase to a different language and using the translation as the received input phrase [pages 3-4, paragraph 0034].
Claim 16: Hayes discloses a system comprising:
a first one or more computing devices to implement a storage service in a multi-tenant provider network [page 2, paragraph 0012 | figure 1]; and
a second one or more computing devices to implement a generative artificial intelligence (AI) service in the multi-tenant provider network [page 2, paragraph 0012 | figure 1], the generative AI service including instructions that upon execution cause the generative AI service to:
receive a request for an input phrase to be responded to by a generative AI model [pages 3-4, paragraphs 0034-0035];
compare to the received input phrase to at least one known harmful input phrase to be stored by the storage service to determine that the received input phrase is to be provided to the generative AI model [page 5, paragraphs 0042-0043];
provide the received input phrase to the generative AI model [page 5, paragraphs 0042 & 0046-0047]; and
generate a response by at least in part on an output of the generative AI model [page 5, paragraph 0048].
Claim 17: Hayes discloses the system of claim 16, wherein the generative AI service is further to compare to the received input phrase to a plurality of paraphrases associated with at least one known harmful input phrase [page 5, paragraphs 0042-0043].
Claim 18: Hayes discloses the system of claim 16, wherein the generative AI service is further to verify the output of the generative AI model is not a hallucination [page 5, paragraph 0048].
Claim 20: Hayes discloses the system of claim 16, wherein the input phrase is in a format of one of text, audio, or video [page 4, paragraphs 0035 & 0040 | page 6, paragraph 0055].
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) 5, 14 and 19 is/are rejected under 35 U.S.C. 103 as being unpatentable over Hayes (2025/0045531) in view of Lafon et al. (2025/0068741).
Claim 5: Hayes discloses the computer-implemented method of claim 4, but does not explicitly disclose wherein comparing to the received input phrase to at least one known harmful input phrase to determine that the received input phrase is to be provided to the generative AI model comprises: generating a first set of similarity values between the received input phrase and at least a subset of the stored known harmful input phrases; and determining that the first set of similarity values is below a first similarity threshold.
However, Lafon et al. discloses a similar invention [page 1, paragraph 0013] and further discloses wherein comparing to the received input phrase to at least one known harmful input phrase to determine that the received input phrase is to be provided to the generative AI model comprises: generating a first set of similarity values between the received input phrase and at least a subset of the stored known harmful input phrases(input risk scores) [page 3, paragraph 0022]; and determining that the first set of similarity values is below a first similarity threshold(risk thresholds) [page 3, paragraph 0025].
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to further modify the disclosure of Hayes with the additional features of Lafon et al., in order to maximize the detection of malicious content while minimizing the number of false positives, as suggested by Lafon et al. [page 3, paragraph 0025].
Claim 14: Hayes discloses the computer-implemented method of claim 4, but does not explicitly disclose wherein the generative AI model is one of a Transformer-based model, a generative adversarial network, or an auto-regressive convolutional neural networks (AR-CNN).
However, Lafon et al. discloses a similar invention [page 1, paragraph 0013] and further discloses wherein the generative AI model is one of a Transformer-based model, a generative adversarial network, or an auto-regressive convolutional neural networks (AR-CNN) [page 3, paragraph 0024].
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to further modify the disclosure of Hayes with the additional features of Lafon et al., in order to leverage the power of pre-trained language models, as suggested by Lafon et al. [page 3, paragraph 0024].
Claim 19: Hayes discloses the system of claim 16, but does not explicitly disclose wherein the generative AI model is one of a Transformer-based model, a generative adversarial network, or an auto-regressive convolutional neural networks (AR-CNN).
However, Lafon et al. discloses a similar invention [page 1, paragraph 0013] and further discloses wherein the generative AI model is one of a Transformer-based model, a generative adversarial network, or an auto-regressive convolutional neural networks (AR-CNN) [page 3, paragraph 0024].
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to further modify the disclosure of Hayes with the additional features of Lafon et al., in order to leverage the power of pre-trained language models, as suggested by Lafon et al. [page 3, paragraph 0024].
Allowable Subject Matter
Claims 2, 6-8 and 12 are objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Feng et al. (2025/0080556).
Any inquiry concerning this communication or earlier communications from the examiner should be directed to EDWARD ZEE whose telephone number is (571)270-1686. The examiner can normally be reached Monday-Friday 9AM-5PM EST.
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/EDWARD ZEE/Primary Examiner, Art Unit 2435