DETAILED ACTION
This action is responsive to the following communications: Original Application filed on March 29, 2024. All references to this application refer to the U.S. Patent Application Publication No. 2024/0330661 A1.
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claims 1-20 are pending in this case. Claims 1, 11, and 20 are the independent claims. Claims 1-20 are rejected.
Priority
Applicants claim the benefit of U.S. Provisional Patent Application No. 63/493,693, filed on March 31, 2023.
Claim Interpretation - 35 USC § 101
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
With regard to claim 1,
Step 2A, Prong 1
This part of the eligibility analysis evaluates whether the claim recites a judicial exception. As explained in MPEP 2106.04, subsection II, a claim “recites” a judicial exception when the judicial exception is “set forth” or “described” in the claim.
Claim 1 recites:
A computer-implemented method for correcting a response generated by a first machine learning model, the method comprising:
receiving the response from the first machine learning model, wherein the response is generated by the first machine learning model based on a request and a context;
determining a plurality of portions of the context that are similar to one or more portions of the response;
for each portion of the context included in the plurality of portions of the context, determining whether the portion of the context supports at least one portion of the response; and
performing one or more operations to generate a corrected response based on the response and whether each portion of the context included in the plurality of portions of the context supports at least one portion of the response.
The broadest reasonable interpretation of the bolded limitations above are directed to a mental process able to be performed in the human mind or by a human using pen and paper. A human can determine similarities between content (observation/judgement), determine whether portions of the context support at least one portion of the response (observation/judgement), and perform operations to generate a corrected response based on the determinations (displaying results of the data collection and analysis). A human can perform these steps mentally or with pen and paper.
Step 2A, Prong 1 (Yes).
Step 2A, Prong 2
This part of the eligibility analysis evaluates whether the claim as a whole integrates the recited judicial exception into a practical application of the exception or whether the claim is “directed to” the judicial exception. This evaluation is performed by (1) identifying whether there are any additional elements recited in the claim beyond the judicial exception, and (2) evaluating those additional elements individually and in combination to determine whether the claim as a whole integrates the exception into a practical application. See MPEP 2106.04(d).
The additional element in this claim is the “first machine learning model.” This element is recited at a high level of generality and thus is a generic computer component performing computer functions. Thus these are mere instructions to apply the exception using a generic computer component. See MPEP 2106.05(f).
Even when viewed in combination the additional element does not integrate the recited judicial exception into a practical application.
Step 2A, Prong 2 (Yes).
Step 2B
This part of the eligibility analysis evaluates whether the claim as a whole amounts to significantly more than the recited exception i.e., whether any additional element, or combination of additional elements, adds an inventive concept to the claim. See MPEP 2106.05.
As explained with respect to Step 2A, the only additional element the “first machine learning model” which at best is mere instructions to apply the abstract ideas and cannot provide an inventive concept, even when considered in combination. See MPEP 2106.05(f). Additionally, the claim further recites “receiving the response from the first machine learning model, wherein the response is generated by the first machine learning model based on a request and a context” which constitutes receiving or transmitting data, as well as storing and retrieving information in memory, which courts have recognized as well-understood, routine, and conventional computer functions. See MPEP 2016.05(d)(II).
Step 2B (Yes).
Claim 1 is ineligible.
With respect to independent claims 11 and 20,
These claims are similar in scope to Claim 1 and are rejected under a similar rationale. The non-transitory computer-readable media (of claim 11) and memories storing instructions and processors coupled to the memories (of claim 20) are also generic computing components.
Claims 11 and 20 are ineligible.
Dependent Claims:
Claims 2-4 and 12-14: These claims recite further abstract ideas (mental processes). Additionally, the claims recite a second machine learning model which performs a plurality of determinations. As the repeated determinations are themselves mental processes, as described above, the only additional element is the second machine learning model.
With respect to Step 2A, Prong 2, this element is recited at a high level of generality and thus is a generic computer component performing computer functions. Thus these are mere instructions to apply the exception using a generic computer component. See MPEP 2106.05(f).
With respect to Step 2B, the “second machine learning model” at best is mere instructions to apply the abstract ideas and cannot provide an inventive concept, even when considered in combination. See MPEP 2106.05(f).
Thus claims 2-4 and 12-14 are ineligible.
Claims 5-9 and 15-17: These claims recite further abstract ideas (mental processes: performing entailment and making determinations; performing coreference resolution; performing semantic similarity; ensuring particular lengths of context and response; determining context based on the request;) and as explained above these do not provide a practical application or inventive concept and thus are ineligible.
Claim 10: This claims defines the first machine learning model to be a large language model (LLM).
With respect to Step 2B, this is a field of use or technological environment limitation in which to apply the abstract idea. Accordingly, this fails to integrate the abstract idea into a practical application. See MPEP 2106.05(h).
Thus claim 10 is ineligible.
Claim 18: This claims recites a further abstract idea (“computing a score”) and also recites “appending the score to the response.”
With respect to Step 2A, Prong 2 the additional element (“appending the score to the response”) is displaying the results of data analysis and collection. See MPEP 2106.04(III).
With respect to Step 2B, “appending the score to the response” is nothing more than insignificant extra-solution activity. See MPEP 2106.05(g).
Thus the claims is ineligible.
Claim 19: This claim further recites “searching a database…to determine the context.”
With respect to Step 2A, Prong 2 this is mere data gathering recited at a high level of generality and thus is insignificant extra-solution activity. See MPEP 2106.04(a)(2)(III).
With respect to Step 2B, searching a database has been found by the courts to be a form of data gathering that is well-understood, routine and conventional activity. See MPEP 2106.05(g).
Thus the claim is ineligible.
To expedite a complete examination of the instant application, the claims rejected above under 35 U.S.C. 101, as relating to judicial exceptions without significantly more, are further rejected as set forth below in anticipation of amendments to these claims to place them within the four statutory categories of invention.
Examiner’s Note
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.
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.
This application currently names joint inventors. In considering patentability of the claims the Examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicants are advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the Examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 1, 2, 4, 6-12, 14, and 16-20 are rejected under 35 U.S.C. 103 as being unpatentable over U.S. Patent Application Publication No. 2024/0289395 A1, filed by Zhou et al., on December 4, 20231, and published on August 29, 2024 (hereinafter Zhou), in view of U.S. Patent No. 9,7520,981 B1, issued to Boguraev on August 1, 2017, and filed on February 25, 2016 (hereinafter Boguraev).
With respect to independent claim 1, Zhou discloses a computer-implemented method for correcting a response generated by a first machine learning model, the method comprising:
Receiving the response from the first machine learning model, wherein the response is generated by the first machine learning model based on a request and a context; Zhou discloses receiving a response from an ML model generated based on a request and a context of the request (see Zhou, Figs. 1-3; see also Zhou, paragraphs 0017 [response generator takes two inputs, including prompts and contexts, as well as determined relevant resources (evidence) in order to generate a response], 0031-0034 [describing Fig. 2 of the factually-grounded generative system that receives a prompt and prompt context used to generate the response, including encoding the prompt and context into vectors to better determine similarity using different measures (distance, semantic similarity, etc.)], and 0037 [supporting evidence must meet threshold relevancy scores]).
Determining a plurality of portions of the context that are similar to one or more portions of the response; Zhou discloses determining portions of the context that are similar to the response (see Zhou, paragraphs 0034 and 0037, described supra).
Although Zhou discloses determining portions of evidence that support the response (see Zhou, paragraphs 0038-0041 [describing how the supporting evidence is gathered, compared to the encoded prompt and used to generate the response as well as provide the support for the response]), Zhou fails to expressly disclose for each portion of the context included in the plurality of portions of the context, determining whether the portion of the context supports at least one portion of the response.
However, Boguraev teaches performing deep analysis on the candidate answer by comparing the answers to the prompt and the context of the prompt (see Boguraev, col. 6, lines 14-38 [describing the general process of the system: receive an input question, analyze the input to extract major elements of the input, use the extracted elements to generate and execute queries to generate candidate answers, then perform deep analysis of the candidate answers to obtain scores that indicate how well the candidate responds to the input], col. 12, lines 9-34 [describing stage 350 of Fig. 3, in which the deep analysis and comparison of the language of the input question and each candidate answer is performed and calculating a score that indicates the measure of relevance of the portions of each answer as well as a score that indicates the correctness of each answer], col. 13, lines 43-54 [describing stage 370, which ranks and compares the answers using generated and collected evidence to generate a final answer and confidence score that is returned to the user], and col. 17, line 54 – col. 18, line 15 [describing the process of Fig. 8, for using multi-instance ML models to answer questions]).
Accordingly, it would have been obvious to one of ordinary skill in the art, having the teachings of Zhou and Boguraev before him before the effective filing date of the claimed invention, to modify the method of Zhou to incorporate comparing portions of the context to the generated response(s) as taught by Boguraev. One would have been motivated to make such a combination because this bypasses the feature merging issues other limitations of conventional question-answer systems, as taught by Boguraev (see Boguraev, col. 3, line 55 – col. 4, line 6 [“The illustrative embodiments provide a mechanism for passage scoring that does not require feature merging, thus bypassing the question of determining an optimal merging policy for each passage scoring feature, and potentially better suited to answering “yes-no” questions, where justifying passages are difficult to come up with for training. The mechanism of the illustrative embodiments is compatible with the overall passage scoring framework outlined above and, therefore, reuses many existing algorithms and components. The illustrative embodiments are applicable to different question answering frameworks for different question types, including questions whose answers are longer passages rather than the one or more conventionally assumed (and expected) short expressions. Separately, the illustrative embodiments rely less on the redundancy of the answer being mentioned in different text fragments or on the assumption that the answer will be embedded in a text fragment. This makes for an interesting and useful extension of the conventional QA system pipeline.”].
Zhou, as modified by Boguraev, further teaches performing one or more operations to generate a corrected response based on the response and whether each portion of the context included in the plurality of portions of the context supports at least one portion of the response.
Zhou further teaches a re-writing refiner that augments, corrects, or replaces portions of the generated response based on an analysis of the response in view of the prompt and the context (see Zhou, paragraphs 0067-0068 [model is trained to learn how to rewrite responses and provide indications of the system’s confidence in the answer as presented in the process of Fig. 4, which can include providing corroborating resources (e.g., evidence)]).
With respect to dependent claim 2, Zhou, as modified by Boguraev, teaches the computer-implemented method of claim 1, as described above.
Boguraev further teaches the method wherein, for each portion of the context included in the plurality of portions of the context, determining whether the portion of the context supports at least one portion of the response comprises:
Prompting a second machine learning model a plurality of times to generate a plurality of determinations of whether the portion of the context supports the at least one portion of the response; Boguraev further teaches prompting a second ML model multiple times to generate a plurality of determinations of whether a portion of the context supports a portion of the response (see Boguraev, col. 6, lines 14-38, col. 12, lines 9-34, col. 13, lines 43-54, and col. 17, line 54 – col. 18, line 15, described supra, claim 1).
Determining whether the portion of the context supports the at least one portion of the response based on the plurality of determinations; Boguraev further teaches determining the confidence and correctness based on the plurality of determinations (see Boguraev, col. 6, lines 14-38, col. 12, lines 9-34, col. 13, lines 43-54, and col. 17, line 54 – col. 18, line 15, described supra, claim 1).
With respect to dependent claim 4, Zhou, as modified by Boguraev, teaches the computer-implemented method of claim 2, as described above.
Boguraev further teaches the method wherein the first machine learning model is the second machine learning model.
Boguraev further teaches that the model can be represented using a single model or multiple models (see Boguraev, col. 4, lines 44-63 [describing and defining the engine, including that functionality attributed to a single engine can be performed by multiple engines a single engine]).
With respect to dependent claim 6, Zhou, as modified by Boguraev, teaches the computer-implemented method of claim 1, as described above.
Zhou and Boguraev further teach the method, further comprising performing one or more coreference resolution operations on the one or more portions of the response.
Zhou further teaches using named entity resolution between the prompt, context, and response (see Zhou, paragraph 0052 [describing how entities within the prompt or context are identified and used to generate responses]; see also, Zhou, paragraph 0034, described supra, claim 1).
Additionally or alternatively, Boguraev further teaches entity resolution between question, context and response (see Boguraev, col. 10, line 54 – col. 11, line 8 [describing an example of extracting entities from questions for use in generating queries for generating candidate responses]).
With respect to dependent claim 7, Zhou, as modified by Boguraev, teaches the computer-implemented method of claim 1, as described above.
Boguraev further teaches the method wherein the plurality of portions of the context are determined to be similar to the one or more portions of the response based on semantic similarity.
Boguraev further teaches using semantic similarity (see Boguraev, col. 5, line 65 – col. 6, line 13 [describing the use of semantic similarity for use in providing answers in QA systems]).
With respect to dependent claim 8, Zhou, as modified by Boguraev, teaches the computer-implemented method of claim 1, as described above.
Zhou and Boguraev further teach the method wherein each portion of context included in the plurality of portions of the context is a sentence, and each portion of the response included in the one or more portions of the response is a sentence.
Zhou further teaches that context and responses are comprised of complete sentences (see Zhou, paragraphs 0040-0041 [describing how the lengths of content are used to determine best results (e.g., two shorter sentences might be better than one long sentence), or how content portions can be concatenated into sentences]; see also, Zhou, paragraph 0067, described supra, claim 1).
Additionally or alternatively, Boguraev further teaches that context and responses are comprised of sentences (see Boguraev, col. 11, lines 15-49 [describing different prompts and contexts that are comprised of sentences, including the construction of a response sentence using replacements] and col. 12, line 56 – col. 13, line 5 [describing the use of sentences in context and response generation]).
With respect to dependent claim 9, Zhou, as modified by Boguraev, teaches the computer-implemented method of claim 1, as described above.
Zhou and Boguraev further teach the method wherein each portion of context included in the plurality of portions of the context includes text of a predefined length, and the one or more portions of the response includes an entirety of the response.
Zhou further teaches that context and responses are comprised of content that exceeds a predetermined minimum length to form a complete response (see Zhou, paragraphs 0040-0041, described supra, claim 8; see also, Zhou, paragraph 0067, described supra, claim 1).
Additionally or alternatively, Boguraev further teaches context that exceeds a predetermined minimum length to form a complete response (see Boguraev, col. 11, lines 15-49 and col. 12, line 56 – col. 13, line 5, described supra, claim 8).
With respect to dependent claim 10, Zhou, as modified by Boguraev, teaches the computer-implemented method of claim 1, as described above.
Zhou further teaches the method wherein the first machine learning model comprises a large language model (LLM).
Zhou further teaches the first ML model comprises an LLM (see Zhou, Figs. 1-3; see also, Zhou, paragraphs 0020 [describing Fig. 1 as including one or more factually-grounded LLMs] and 0030 [system includes one or more factually-grounded LLMs]).
Independent claim 11, and its respective dependent claims 12, 14, and 16, recite one or more non-transitory computer-readable media storing instructions that, when executed by at least one processor, cause the at least one processor to perform the steps of the method recited in independent claim 1, and its respective dependent claims 2, 4, and 6. Accordingly, independent claim 11, and its respective dependent claims 12, 14, and 16, are rejected under the same rationales used to reject independent claim 1, and its respective dependent claims 2, 4, and 6, which are incorporated herein.
Independent claim 20 recites a system, comprising: one or more memories storing instructions; and one or more processors that are coupled to the one or more memories and, when executing the instructions, are configured to perform the method recited in independent claim 1. Accordingly, independent claim 20 is rejected under the same rationales used to reject independent claim 1, which are incorporated herein.
With respect to dependent claim 17, Zhou, as modified by Boguraev, teaches the non-transitory computer-readable media of claim 11, as described above.
Zhou further teaches the media wherein the instructions, when executed by the at least one processor, further cause the at least one processor to perform the steps of:
Determining the context based on the request; Zhou further teaches determining the context based on the input prompt (see Zhou, paragraphs 0031-0034, described supra, claim 1).
Prompting the first machine learning model to generate the response based on the request and the context; Zhou further teaches the ML model generating the response based on the request and the context (see Zhou, paragraphs 0031-0034 and 0037, described supra, claim 1).
With respect to dependent claim 18, Zhou, as modified by Boguraev, teaches the non-transitory computer-readable media of claim 11, as described above.
Boguraev further teaches the media wherein performing one or more operations to generate the corrected response comprises:
Computing a score based on whether each portion of the context included in the plurality of portions of the context supports at least one portion of the response; Boguraev further teaches computing a score based on whether each portion of the context supports at least one portion of the response (see Boguraev, col. 6, lines 14-38, col. 12, lines 9-34, and col. 13, lines 43-54, described supra, claim 1).
Appending the score to the response; Boguraev further teaches providing the confidence score with the response (see Boguraev, col. 13, lines 43-54 [describing stage 370, in which the supporting evidence is collected, and a final answer is selected, and provided with the confidence score and the supporting evidence to the user]; see also, Boguraev, col. 12, lines 9-34 and col. 17, line 54 – col. 18, line 15, described supra, claim 1).
With respect to dependent claim 19, Zhou, as modified by Boguraev, teaches the non-transitory computer-readable media of claim 11, as described above.
Boguraev further teaches the media wherein the instructions, when executed by the at least one processor, further cause the at least one processor to perform the steps of:
Searching a database based on the request to determine the context, wherein the context includes at least one portion of one or more documents stored in the database; Boguraev further teaches searching databases to determine the context based on the request (see Boguraev, col. 6, lines 14-38, col. 12, lines 9-34, col. 13, lines 43-54, and col. 17, line 54 – col. 18, line 15, described supra, claim 1).
Prompting the first machine learning model to generate the response based on the request and the context; Boguraev further teaches the ML model generating the response based on the request and the context (see Boguraev, col. 6, lines 14-38, col. 12, lines 9-34, col. 13, lines 43-54, and col. 17, line 54 – col. 18, line 15, described supra, claim 1).
Claims 3 and 13 are rejected under 35 U.S.C. 103 as being unpatentable over Zhou, in view of Boguraev, further in view of U.S. Patent Application Publication No. 2022/0374608 A1, filed by Shazeer et al., on May 20, 2022, and published on November 24, 2022 (hereinafter Shazeer).
With respect to dependent claim 3, Zhou, as modified by Boguraev, teaches the computer-implemented method of claim 2, as described above.
Zhou and Boguraev fail to further teach the method wherein performing one or more operations to generate the corrected response comprises: For each portion of the context included in the plurality of portions of the context that does not support at least one portion of the response, prompting the second machine learning model to generate an intermediate corrected response based on the portion of the context.
However, Shazeer teaches creating a plurality of intermediate corrected responses using the portions of the prompt that do not support the response (see Shazeer, paragraph 0022 [the context is processed to generate a plurality of intermediate strings which are then used to generate the final output response], 0032 [at inference time, the model generates intermediate analysis given the inputs using the previously generated output as a portion of the input], 0034 [the model uses the context, intermediate analysis and response triple (tuple) to generate the intermediate and final responses], 0046 [the generation of the intermediate strings can be performed over a number of iterations to generate a plurality of intermediate strings], and 0051 [the model shifts to an error-correction or fact-checking mode in order to generate output that best matches the input and the context]).
Accordingly, it would have been obvious to one of ordinary skill in the art, having the teachings of Zhou, Boguraev, and Shazeer before him before the effective filing date of the claimed invention, to modify the method of Zhou, as modified by Boguraev, to incorporate generating a plurality of intermediate corrected responses as taught by Shazeer. One would have been motivated to make such a combination because this improves the interpretability of the prompt and improves accuracy of the answer, as taught by Shazeer (see Shazeer, paragraph 0023 [“Thus, aspects of the present disclosure improve the knowledge, grounding, and interpretability of a machine-learned language model by teaching the model to generate textual analysis before (e.g., in service of) generating output text responsive to a contextual text input (e.g., generating a response to a question or prior dialog). The generation of such intermediate textual analysis can improve the interpretability of the model output. In particular, the intermediate textual analysis can be reviewed or inspected to interpret or understand how the model generated the output in response to the contextual input. This may also facilitate assessment of the reliability and/or suitability of the output in serving a particular task.”]).
Zhou, as modified by Boguraev and Shazeer, further teach prompting the second machine learning model to generate the corrected response based on the intermediate corrected response generated for each portion of the context included in the plurality of portions of the context that does not support at least one portion of the response.
Shazeer further teaches generating the corrected response based on the intermediate corrected responses (see Shazeer, paragraphs
Dependent claims 13 recites one or more non-transitory computer-readable media storing instructions that, when executed by at least one processor, cause the at least one processor to perform the steps of the method recited in dependent claim 3. Accordingly, dependent claim 13 is rejected under the same rationales used to reject dependent claim 3, which are incorporated herein.
Claims 5 and 15 are rejected under 35 U.S.C. 103 as being unpatentable over Zhou, in view of Boguraev, further in view of U.S. Patent Application Publication No. 2024/0273345 A1, filed by Bharadwaj et al., on February 13, 2023, and published on August 15, 2024 (hereinafter Bharadwaj).
With respect to dependent claim 5, Zhou, as modified by Boguraev, teaches the computer-implemented method of claim 1, as described above.
Zhou and Boguraev fail to further teach the method wherein, for each portion of the context included in the plurality of portions of the context, determining whether the portion of the context supports at least one portion of the response comprises: performing one or more operations to compute a first entailment of the at least one portion of the response by the portion of the context.
However, Bharadwaj teaches using entailment to determine semantic similarity (or dissimilarity) between context and response (see Bharadwaj, paragraphs 0147 [describing the use of various techniques to determine semantic similarity or dissimilarity between the prompt, context, and response, including entailment] and 0154 [describing the use of various techniques to determine semantic similarity or dissimilarity between the prompt, context, and response, including entailment]).
Accordingly, it would have been obvious to one of ordinary skill in the art, having the teachings of Zhou, Boguraev, and Bharadwaj before him before the effective filing date of the claimed invention, to modify the method of Zhou, as modified by Boguraev, to incorporate entailment as taught by Bharadwaj. One would have been motivated to make such a combination because this generates content while maintaining high levels of semantic quality, as taught by Bharadwaj (see Bharadwaj, paragraph 0003 [“Accordingly, a need has been identified for generative AI systems that can generate content while both maintaining high levels of semantic quality and fitting generated content to known user preferences and use cases.”]).
Zhou, as modified by Boguraev and Bharadwaj, further teach prompting the second machine
Performing one or more operations to compute a second entailment of a negation of the at least one portion of the response by the portion of the context; Bharadwaj further teaches computing negative entailment on a negated portion of the context (see Bharadwaj, paragraphs 0147 and 0154, described supra).
Determining whether the portion of the context supports the at least one portion of the response based on the first entailment and the second entailment; Bharadwaj further teaches determine support based on the combination of entailment analyses (see Bharadwaj, paragraphs 0147 and 0154, described supra) .
Dependent claims 15 recites one or more non-transitory computer-readable media storing instructions that, when executed by at least one processor, cause the at least one processor to perform the steps of the method recited in dependent claim 5. Accordingly, dependent claim 15 is rejected under the same rationales used to reject dependent claim 5, which are incorporated herein.
Conclusion
The prior art made of record and not relied upon is considered pertinent to Applicants’ disclosure. See PTO-892.
It is noted that any citation to specific pages, columns, figures, or lines in the prior art references 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 1331-33, 216 USPQ 1038-39 (Fed. Cir. 1983) (quoting In re Lemelson, 397 F.2d 1006, 1009, 158 USPQ 275, 277 (CCPA 1968)).
Any inquiry concerning this communication or earlier communications from the Examiner should be directed to ERIC J. BYCER whose telephone number is (571) 270-3741. The Examiner can normally be reached Monday - Thursday 9am-6pm, and alternate Fridays 9am-5pm.
Examiner interviews are available via a variety of formats. See MPEP § 713.01. To schedule an interview, Applicants are encouraged to use the USPTO Automated Interview Request (AIR) Form at https://www.uspto.gov/InterviewPractice.
If attempts to reach the Examiner by telephone are unsuccessful, the Examiner’s supervisor, MATT ELL can be reached on (571) 270-3264. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/ERIC J. BYCER/
Primary Examiner
Art Unit 2141
1 Zhou claims the benefit of U.S. Provisional Patent Application No. 63/487,477, filed on February 28, 2023.
Prosecution Insights