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 .
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 4/24/26 has been entered.
Remarks
This action is in response to the request for continuation received on 4/24/26. Claims 1-5, 7-17, 19 and 22 are pending in the application. Claims 6, 18, 20, and 21 have been cancelled and claim 22 has been added. Applicant’s arguments have been respectfully considered.
Claims 1-5, 7-17, 19 and 22 are rejected under 35 U.S.C. 101.
Claim(s) 1-3, 8, 9, 11-15, 17, and 19 are rejected under 35 U.S.C. 103 as being unpatentable over Lavallee (US 10,789,539), and further in view of Lee et al. (US 2020/0193153).
Claim 4 is rejected under 35 U.S.C. 103 as being unpatentable over Lavallee in view of Lee, and further in view of Qadrud-Din et al. (US 2024/0289561).
Claim 7 is rejected under 35 U.S.C. 103 as being unpatentable over Lavallee in view of Lee, and further in view of Vaswani et al. (US 2021/0390410).
Claims 10 and 16 are rejected under 35 U.S.C. 103 as being unpatentable over Lavallee in view of Lee, and further in view of Applicant Admitted Prior Art (AAPA).
Claim 22 is rejected under 35 U.S.C. 103 as being unpatentable over Lee et al. (US 2020/0193153), and further in view of Vaswani et al. (US 2021/0390410).
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-5, 7-17, 19 and 22 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 2A, Prong One asks: Is the claim directed to a law of nature, a natural phenomenon (product of nature) or an abstract idea? See MPEP 2106.04 Part I. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas. See MPEP 2106.04(a).
With respect to claims 1, 11, and 15, the limitation of “generating a respective prompt comprising a respective query”, as drafted, is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components. That is, nothing in the claim element precludes the step from practically being performed in the mind. For example, language, “generating” in the context of this claim encompasses the user mentally determining a problem to solve. Similarly, the limitation of “computing a plurality of pairwise scores based on, for each respective pairwise comparison of the plurality of pairwise comparisons between a plurality of candidate results” and “performing, by the generative sequence processing model, the respective pairwise comparison between the respective first set of text and the respective second set of text based on the query”, as drafted, is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components. For example, “computing” and “performing” in the context of this claim encompasses the user comparing text. The limitation of “generating, by the generative sequence processing model based on the respective pairwise comparison, a respective output”, as drafted, is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components. For example, “generating” in the context of this claim encompasses the user determining an answer to the problem. Additionally, “assigning, when the respective output indicates that the respective first set of text is preferred to the respective second set of text, a respective pairwise score for the respective first set of text, or, when the respective output indicates that the respective second set of text is preferred to the respective first set of text, the respective pairwise score for the respective second set of text”, as drafted, is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components. For example, “assigning” in the context of this claim encompasses the user mentally associating a score with text. The limitation of “aggregating the plurality of pairwise scores”, as drafted, is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components. For example, “aggregating” in the context of this claim encompasses the user combining scores.
If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
At step 2a, prong two, this judicial exception is not integrated into a practical application. The claims discuss “a generative sequence processing model”, however, this appears to be a generic computer component. Claims 11, 15, and 21 recite computing devices and a processor to execute the operations, however, this is recited as a high-level of generality (i.e., as a generic processor performing a generic computer function) such that it amounts to no more than mere instructions to apply the exception using a generic computer component. Additionally, the claims recite “prompting a generative sequence processing model with the prompt.” These elements do not integrate the abstract idea into a practical application because they do not impose a meaningful limit on the judicial exception and provide only insignificant extra solution activity that is mere data gathering in conjunction with the abstract idea.
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements amount to no more than mere instructions to apply an exception using generic computer components. Mere instructions to apply an exception using generic computer components cannot provide an inventive concept.
With respect to “prompting a generative sequence processing model with the prompt”, the courts have found limitations directed towards data gathering to be well-understood, routine, and conventional. See MPEP 2106.05(d)(II). Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information).
Considering the additional elements individually and in combination and the claim as a whole, the additional elements do not provide significantly more than the abstract idea. The claim is not patent eligible.
With respect to claim 2, the limitations disclose “processing” input data, which is a mental process encompassing a user analyzing data. The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception.
With respect to claims 3, 12, and 17, the limitations disclose “adding” the scores which is a mental process encompassing mental math. The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception.
With respect to claims 4 and 5, the limitations disclose “assigning” points, which is a mental process encompassing the user mentally associating a score with text. The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception.
With respect to claims 7-10 and 14, the limitations further define the above mental processes and do not include additional elements that are sufficient to amount to significantly more than the judicial exception.
With respect to claims 13 and 19, the limitations disclose “performing” comparisons, “assigning” scores, and “aggregating” scores, which each disclose mental processes. These encompass the user comparing text, the user mentally associating a score with text, and the user combining scores, respectively. The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception.
With respect to claim 16, the limitations disclose “providing” the text via an API. These elements do not integrate the abstract idea into a practical application because they do not impose a meaningful limit on the judicial exception and provide only insignificant extra solution activity in conjunction with the abstract idea.
The courts have found limitations directed towards outputting to be well-understood, routine, and conventional. See MPEP 2106.05(d)(II). Presenting offers and gathering statistics, OIP Techs., 788 F.3d at 1362-63, 115 USPQ2d at 1092-93. Considering the additional elements individually and in combination and the claim as a whole, the additional elements do not provide significantly more than the abstract idea.
With respect to claim 22, the limitation of “assigning… a respective pairwise score” , is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components. For example, “assigning” in the context of this claim encompasses the user mentally associating a score with text. The limitation of “aggregating the plurality of pairwise scores”, is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components. For example, “aggregating” in the context of this claim encompasses the user combining scores.
If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
At step 2a, prong two, this judicial exception is not integrated into a practical application. The claims discuss “one or more model instances of a machine-learned sequence processing model”, however, this appears to be a generic computer component. Additionally, the claim discloses computing devices, however, this is recited as a high-level of generality (i.e., as a generic processor performing a generic computer function) such that it amounts to no more than mere instructions to apply the exception using a generic computer component. Additionally, the claims recite “providing… a plurality of prompts” and “obtaining… a plurality of outputs.” These elements do not integrate the abstract idea into a practical application because they do not impose a meaningful limit on the judicial exception and provide only insignificant extra solution activity that is mere data gathering in conjunction with the abstract idea.
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements amount to no more than mere instructions to apply an exception using generic computer components. Mere instructions to apply an exception using generic computer components cannot provide an inventive concept.
With respect to “providing… a plurality of prompts” and “obtaining… a plurality of outputs”, the courts have found limitations directed towards data gathering to be well-understood, routine, and conventional. See MPEP 2106.05(d)(II). Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information).
Considering the additional elements individually and in combination and the claim as a whole, the additional elements do not provide significantly more than the abstract idea. The claim is not patent eligible.
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 1-3, 8, 9, 11-15, and 17 are rejected under 35 U.S.C. 103 as being unpatentable over Lavallee (US 10,789,539), and further in view of Lee et al. (US 2020/0193153).
With respect to claim 1, Lavallee teaches a computer-implemented method for ranking, the method performed by one or more computing devices and comprising:
computing a plurality of pairwise scores based on, for each respective pairwise comparison of the plurality of pairwise comparisons between a plurality of candidate results (Lavallee, Col. 11 Li. 59-61, At step 305, the computing device may use a pairwise classifier to determine scores for element pairs in the potential results & Col. 12 Li. 41-43, At step 307, the computing device may determine one or more pairwise probabilities based on the scores for the element pairs determined by the pairwise classifier):
generating a respective prompt comprising a respective query (Lavelle, Col. 10 Li. 66 – Col. 11-1, request may be received by the computing device and the request may identify previously stored text that is to be processed.), a respective first set of text associated with a respective first candidate result of the plurality of candidate results, and a respective second set of text associated with a respective second candidate result of the plurality of candidate results (Lavallee, Li. 32-41, System 200 may comprise a tagging (TE) engine 208, which may process a transcription or query for tagging. TE 208 may leverage/process device and/or user metadata that may be stored in a database and/or on the device. For example, TE 208 may parse a string of words (e.g., using grammars, named entity processing, and/or internal concept processing) to determine whether any of the words in the string match any of the user metadata, such as a name in a contact list ( e.g., a contact list stored on a user's device, such as a mobile phone). & Li. 53-54, The tagged data may be stored in one or more databases or caches, such as database 210);
prompting a generative sequence processing model with the respective prompt (Lavallee, Col. 11 Li. 1-5, the computing device may retrieve the previously stored text and provide the text as natural language input for processing in order to, for example, generate natural language understanding output.);
performing, by the generative sequence processing model, the respective pairwise comparison between the respective first set of text and the respective second set of text based on the query (Lavallee, Col. 11 Li. 59-61, At step 305, the computing device may use a pairwise classifier to determine scores for element pairs in the potential results); and
generating, by the generative sequence processing model based on the respective pairwise comparison, a respective output indicating whether the respective first set of text is preferred to the respective second set of text or the respective second set text is preferred to the respective first set of text (Lavallee, Col. 12 Li. 41-43, At step 307, the computing device may determine one or more pairwise probabilities based on the scores for the element pairs determined by the pairwise classifier); and
assigning, when the respective output indicates that the respective first set of text is preferred to the respective second set of text, a respective pairwise score for the respective first set of text, or, when the respective output indicates that the respective second set of text is preferred to the respective first set of text, the respective pairwise score for the respective second set of text (Lavallee, Col. 13 Li. 27-33, At step 309, the computing device may determine, for each of the potential results, an estimation of the probability that the current result is the top ranked, or best, result among the potential results. In other words, the probability estimate being determined at step 309 is the probability that a particular element in the potential results is the best choice given all the alternatives in the potential results.).
aggregating the plurality of pairwise scores into an aggregate score to generate a ranking for the plurality of candidate results (Lavallee, Col. 18 Li. 5-7, At step 311, the computing device may determine a ranking of the potential results based on the top-rank probability estimates. For example, the potential results may be sorted so that the element with the highest top-rank probability is first in the list and is followed by the element with the second highest top-rank probability, and so forth).
Lavallee doesn't expressly discuss wherein the plurality of pairwise comparisons are performed in parallel.
Lee teaches wherein the plurality of pairwise comparisons are performed in parallel (Lee, pa 0209, The comparisons may be performed partly or completely in parallel to enable a fast processing time.).
It would have been obvious at the effective filing date of the invention to a person having ordinary skill in the art to which said subject matter pertains to have modified Lavallee with the teachings of Lee because it may significantly improve the data processing speed (Lee, pa 0209).
With respect to claim 2, Lavallee in view of Lee teaches the computer-implemented method of claim 1, wherein performing, by the generative sequence processing model, the respective pairwise comparison comprises:
processing a first input comprising the first set of text ordered before the second set of text; and processing a second input comprising the second set of text ordered before the first set of text (This was well known way to perform pairwise comparisons at the time of the invention. See Lavalle Col. 3 Li. 64-67).
With respect to claim 3, Lavallee in view of Lee teaches the computer-implemented method of claim 1, wherein aggregating the respective pairwise score into the aggregate score comprises adding the respective pairwise score to a cumulative total of pairwise scores associated with the respective first set of text being the preferred set of text or to a cumulative total of pairwise scores associated with the respective second set of text being the preferred set of text (Lee, pa 0254, The user interface presented to the user may include components for allowing the user accept or reject the portions of text presented as similar. Acceptance indicates that the user agrees that the portion of text identified by the search engine is in fact similar to the portion of text that the user had entered. Rejection indicates that the user does not think that the portions of text are similar. The acceptances and rejections from the user may be used to train and improve the machine learning model for text similarity. When an acceptance is received, the value of the association between the two portions may be increased and the machine learning model may be trained to make it more likely to recommend the stored text segment for the input text segment, or text like it, in the future).
With respect to claim 8, Lavallee in view of Lee teaches the computer-implemented method of claim 1, wherein the output corresponds to a likelihood of the generative sequence processing model generating a token that identifies the set of text as the preferred text (Lavallee, Col. 12 Li. 41-43, At step 307, the computing device may determine one or more pairwise probabilities based on the scores for the element pairs determined by the pairwise classifier).
With respect to claim 9, Lavallee in view of Lee teaches the computer-implemented method of claim 8, wherein the output comprises text corresponding to the token (Lavallee, Col. 18 Li. 12-17, the potential results may be sorted so that the person determined to have the greatest top-rank probability estimate is first in the list (e.g., Andy Smith is sorted to be first in the list if the top-rank probability estimate for Andy Smith is greater than the top-rank probability estimate for Andy Jones).).
With respect to claim 11, Lavallee teaches a computer-implemented method for prompt-based ranking, the method performed by one or more computing devices and comprising:
providing, for a plurality of pairwise comparisons by a generative sequence processing model (Lavallee, Col. 11 Li. 59-61, At step 305, the computing device may use a pairwise classifier to determine scores for element pairs in the potential results & Col. 12 Li. 41-43, At step 307, the computing device may determine one or more pairwise probabilities based on the scores for the element pairs determined by the pairwise classifier), a plurality of sets of text, each set of text of the plurality of sets of text associated with a candidate result of a plurality of candidate results (Lavalle, Li. 32-41, System 200 may comprise a tagging (TE) engine 208, which may process a transcription or query for tagging. TE 208 may leverage/process device and/or user metadata that may be stored in a database and/or on the device. For example, TE 208 may parse a string of words (e.g., using grammars, named entity processing, and/or internal concept processing) to determine whether any of the words in the string match any of the user metadata, such as a name in a contact list ( e.g., a contact list stored on a user's device, such as a mobile phone). & Li. 53-54, The tagged data may be stored in one or more databases or caches, such as database 210);
receiving, as output from the generative sequence processing model, outputs for each of the plurality of pairwise comparisons (Lavallee, Col. 13 Li. 27-33, At step 309, the computing device may determine, for each of the potential results, an estimation of the probability that the current result is the top ranked, or best, result among the potential results. In other words, the probability estimate being determined at step 309 is the probability that a particular element in the potential results is the best choice given all the alternatives in the potential results.);
assigning, for each of the plurality of pairwise comparisons, when a respective output indicates that a respective first set of text is preferred to a respective second set of text, a pairwise score for the respective first set of text, or, when the respective output indicates that the respective second set of text is preferred to the respective first set of text, the pairwise score for the respective second set of text (Lavallee, Col. 13 Li. 38-40, Once each probability estimate or approximate has been determined, the elements may be sorted into a rank order by the obtained probability estimates.).
Lavallee doesn't expressly discuss generating, based on the assigned pairwise scores, an aggregate score for each of the plurality of sets of text to generate a ranking of the plurality of sets of text.
Lee teaches generating, based on the assigned pairwise scores (Lee, pa 0223, In step 1003, a similarity score between each input text segment and a plurality of stored text segments in document storage 124 may be provided by the text similarity machine learning model.), an aggregate score for each of the plurality of sets of text to generate a ranking of the plurality of sets of text (Lee, pa 0224, The candidate combinations may then be scored based on the scoring criteria so that a highest scoring candidate combination is selected as the final combination.).
It would have been obvious at the effective filing date of the invention to a person having ordinary skill in the art to which said subject matter pertains to have modified Lavallee with the teachings of Lee because it allows for optimizing scores across variables of documents (Lee, pa 0218).
With respect to claims 12 and 14, the limitations are essentially the same as claims 3 and 8, and are rejected for the same reasons.
With respect to claim 13, Lavallee in view of Lee teaches the computer implemented method of claim 11, further comprising: performing a plurality of pairwise comparisons for a plurality of sets of text in parallel; assigning pairwise scores for the preferred sets of texts for each of the plurality of pairwise comparisons (Lavallee, Col. 13 Li. 38-40, Once each probability estimate or approximate has been determined, the elements may be sorted into a rank order by the obtained probability estimates.); and aggregating the pairwise scores for each of the plurality of sets of text into an aggregate score for each of the plurality of sets of text to generate the ranking (Lee, pa 0224, The candidate combinations may then be scored based on the scoring criteria so that a highest scoring candidate combination is selected as the final combination.).
With respect to claims 15 and 21, the limitations are essentially the same as claim 11, and are rejected for the same reasons.
Claim 4 is rejected under 35 U.S.C. 103 as being unpatentable over Lavallee in view of Lee, and further in view of Qadrud-Din et al. (US 2024/0289561).
With respect to claim 4, Lavallee in view of Lee teaches the computer-implemented method of claim 3, as discussed above.
Qadrud-Din teaches assigning one point to the preferred set of text (Qadrud-Din, pa 0186-0191, 2. If you extracted any passages, assign each one a score from 1 to 5, representing how the passage relates to the question: [0187] 5 (complete answer) [0188] 4 (one piece of a multipart answer) [0189] 3 (relevant definition or fact) [0190] 2 (useful context) [0191] 1 (marginally related)).
It would have been obvious at the effective filing date of the invention to a person having ordinary skill in the art to which said subject matter pertains to have modified Lavallee in view of Lee with the teachings of Qadrud-Din because in assists in finding relevant text (Qadrud-Din, pa 0183).
Claim 7 is rejected under 35 U.S.C. 103 as being unpatentable over Lavallee in view of Lee, and further in view of Vaswani et al. (US 2021/0390410).
With respect to claim 7, Lavallee in view of Lee teaches the computer-implemented method of claim 6, as discussed above.
Vaswani teaches wherein the generative sequence processing model processes a batch of input requests in parallel, the batch of input requests comprising the respective prompt (Vaswani, each layer input includes a “batch” dimension, where the neural network processes a batch of multiple input images in parallel and the layer input 210 includes a respective index along the batch dimension for each input image in the batch.).
It would have been obvious at the effective filing date of the invention to a person having ordinary skill in the art to which said subject matter pertains to have modified Lavallee in view of Lee with the teachings of Vaswani because it significantly reduces the time and memory required to both train the neural network and perform inference using the neural networks (Vaswani, pa 0006).
Claims 10 and 16 are rejected under 35 U.S.C. 103 as being unpatentable over Lavallee, and further in view of Applicant Admitted Prior Art (AAPA).
With respect to claim 10, Lavallee in view of Lee teaches the computer-implemented method of claim 8, as discussed above.
AAPA teaches wherein the output is received via an application programming interface that does not output calibrated prediction probabilities (AAPA, specification, pa 0005, with the existing methods, ranking requires generative sequence processing models to output calibrated prediction probabilities before sorting, which is very difficult and not supported by generation only APIs).
It would have been obvious at the effective filing date of the invention to a person having ordinary skill in the art to which said subject matter pertains to have modified Lavallee in view of Lee with the teachings of AAPA because this is how existing methods operate (AAPA, pa 0005).
With respect to claim 16, the limitations are essentially the same as claim 10, and are rejected for the same reasons.
Claim 22 is rejected under 35 U.S.C. 103 as being unpatentable over Lee et al. (US 2020/0193153), and further in view of Vaswani et al. (US 2021/0390410).
With respect to claim 22, Lee teaches a computer-implemented method for ranking, the method performed by one or more computing devices and comprising:
providing, for input to one or more model instances of a machine-learned sequence processing model, a plurality of prompts, wherein each respective prompt of the plurality of prompts corresponds to a respective pairwise comparison between a plurality of candidate results and comprises a respective query, a respective first set of text associated with a respective first candidate result, and a respective second set of text associated with a respective second candidate result (Lee, pa 0089-0092, In step 302, an input text block may be received from a user. In step 303, the input text block may be split into input text segments. In step 304, text similarity matching may be performed on each input text segment using the machine learning model. … the input text segments may be considered query text segments and the stored text segments in document storage 124 may be considered candidate text segments.);
obtaining, using the one or more model instances of the machine-learned sequence processing model, a plurality of outputs descriptive of a plurality of pairwise comparisons associated with the plurality of prompts (Lee, pa 0223, In step 1003, a similarity score between each input text segment and a plurality of stored text segments in document storage 124 may be provided by the text similarity machine learning model.),
assigning, when the respective output indicates that the respective first set of text is preferred to the respective second set of text, a respective pairwise score for the respective first set of text, or, when the respective output indicates that the respective second set of text is preferred to the respective first set of text, the respective pairwise score for the respective second set of text (Lee, pa 0224, The candidate combinations may then be scored based on the scoring criteria so that a highest scoring candidate combination is selected as the final combination.); and
aggregating the plurality of pairwise scores into an aggregate score to generate a ranking for the plurality of candidate results (Lee, pa 0224, multiple candidate combinations may be output rather than selecting a single combination for output, in order to provide more options to the user.).
Lee doesn't expressly discuss wherein the one or more model instances of the machine-learned sequence processing model are configured with an input structure having a batch dimension for processing inputs in parallel, the plurality of prompts distributed across the batch dimension of the input structure.
Vaswani teaches wherein the one or more model instances of the machine-learned sequence processing model are configured with an input structure having a batch dimension for processing inputs in parallel, the plurality of prompts distributed across the batch dimension of the input structure (Vaswani, each layer input includes a “batch” dimension, where the neural network processes a batch of multiple input images in parallel and the layer input 210 includes a respective index along the batch dimension for each input image in the batch.).
It would have been obvious at the effective filing date of the invention to a person having ordinary skill in the art to which said subject matter pertains to have modified Lavallee in view of Lee with the teachings of Vaswani because it significantly reduces the time and memory required to both train the neural network and perform inference using the neural networks (Vaswani, pa 0006).
Response to Arguments
35 U.S.C. 101
Applicant argues that the claims now reflect the improvement by specifying that the pairwise comparisons are performed in parallel. The Examiner respectfully disagrees. Neither the claims nor the specification calls for any parallel processing system different from those available in existing systems. Rather, to the extent that parallel processing is discussed in the specification pa 0141, it is characterized as generic parallel processing. Applicant does not explain how the claimed parallel processing functions with the other claim elements to provide the alleged improvement to technology.
35 U.S.C. 103
Applicant seems to argue a newly amended limitation. Applicant’s amendment has rendered the previous rejection moot. Upon further consideration of the amendment, a new grounds of rejection is made in view of Lee et al. (US 2020/0193153) and Vaswani et al. (US 2021/0390410)
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Li et al., Paradigms of Parallelism, www.colossalai.org, fetched from Wayback Machine on October 2, 2022.
Qin et al., Large Language Models are Effective Text Rankers with Pairwise Ranking Prompting, June 30, 2023.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to BRITTANY N ALLEN whose telephone number is (571)270-3566. The examiner can normally be reached M-F 9 am - 5:00 pm EST.
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/BRITTANY N ALLEN/ Primary Examiner, Art Unit 2169
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