DETAILED ACTION
This examination is in response to the communication filed on 01/08/2025. Claims 1-20 are currently pending, where claims 1, 8 and 15 are independent.
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 .
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 03/17/2025 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
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 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea and/or mathematical algorithm without significantly more. Independent claims 1, 8 and 15 recite “processing…ongoing user interaction patterns…and historical pattern of prompts…to predict and generate a next probable prompt” and “transmitting the probable next prompt…for storage…”
The limitations of “processing…” and “transmitting…” as drafted, are a process that, under a broadest reasonable interpretation, covers the abstract idea of “mental processes” because they cover concepts performed in the human mind, including observation, evaluation, judgement and opinion. See MPEP 2106.04(a)(2). That is, other than reciting “a predictive model”, a “cloud server”, a “edge server”, a “memory” (claim 8) and “a processor coupled to the memory” (claim 8), nothing in the claimed elements preclude the steps from practically being performed by a person
processing,
This judicial exception is not integrated into a practical application because the additional elements of “a predictive model”, a “cloud server”, a “edge server”, a “memory” (claim 8) and “a processor coupled to the memory” (claim 8), are all recited at a high-level of generality, and paragraph [0083] of the Specification describes the use of a general-purpose processor. Accordingly, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. In addition, the added limitation of using “a predictive model” is not recited with sufficient specificity as to provide any details about how the predictive model operates or how the processing of the user and historical data is performed and the plain meaning of “processing” encompasses mental observations or evaluations, e.g., an person’s mental observation or evaluation as to the pattern between subsequent questions/prompts. Thus, the claims as a whole are directed to an abstract idea (Step 2A, prong two).
Claims 1, 8 and 15 do not include any additional elements that are sufficient to amount to significantly more than the judicial exception because, as discussed above with respect to integration of the abstract idea into a practical application, the additional elements of “a predictive model”, a “cloud server”, a “edge server”, a “memory” (claim 8) and “a processor coupled to the memory” (claim 9), amount to no more than mere instructions to apply the exception using generic computer components. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept (Step 2B).
With respect to dependent claims 4-6, 11-13 and 18-20, these claims are directed the criteria used remove predicted probable prompts from storage. These limitations also relate to the abstract idea of “mental processes.” That is nothing in the claimed elements preclude the steps from practically being performed by a person using the recited criteria editing the list of probable next questions/prompts. No additional elements are present. Claims 3, 10 and 17, recite the additional element of “a transformer.” However, the added limitation of “a transformer” is not recited with sufficient specificity as to provide any details about how the transformer operates, Thus, as discussed with respect to independent claims 1, 8 and 15, the additional element in the claims amounts to no more than mere instructions to apply the exception using a generic computer component. The same analysis applies here, i.e., mere instructions to apply an exception using a generic computer component, e.g., a transformer, cannot integrate a judicial exception into a practical application at Step 2A or provide an inventive concept in Step 2B. Step 2B = No, the claim does not provide an inventive concept (significantly more than the abstract idea).
With respect to dependent claims 2-3, 9-10 and 16-17, these claims relate to the current prompt being an input to the predictive model and the probable next prompts being outputs of a transformer model. These limitations also relate to the abstract idea of “mental processes.” That is nothing in the claimed elements preclude the steps from practically being performed by the person the indented use of the current prompts or source of the predicted probable prompts are an intend use which have not effect on the processing of the information to predicted the next probable response.
With respect to dependent claims 7 and 14, these claims relate to where user specific predictions are stored. These claims are considered post solution activity.
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 non-obviousness.
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. Applicant is 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, 6-9, 13-16 and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Appel et al. (US 2017/0351962 A1, herein “Appel”) cited in IDS filed on 03/07/2025 in view of Li et al. (“Predictive Edge Caching through Deep Mining of Sequential Patterns in User Content Retrievals” Computer Networks 233 (2023); herein “Li”) citing in IDS filed on 03/07/2025.
Regarding claims 1, 8 and 15, Appel teaches a method (Fig. 11), a system (Fig. 13) comprising a memory (Fig. 13, memory 16) and processor coupled to the memory (Fig. 13, processor(s) 12), and a computer readable medium (¶[0066] teaches “…may be a system, a method, and/or a computer program product…”) comprising a computer readable storage medium having code when executed by a processor performs operations, comprising:
processing, in a cloud server (¶[0058] teaches “Fig. 13 may include…server computer systems” and ¶[0069] teaches that “…the program instructions may execute…entirely on the remote computer or server” Accordingly, Appel teaches that the prediction process may be), ongoing user interaction patterns of a user (¶[0029] teaches “…user-provided content, for example, at 116, allows the system and/or method of the present disclosure to make custom predictions based on the user who is currently using the question and answer system” ) and historical pattern of prompts from previous user interaction patterns of other users (¶[0049] teaches “The interaction history analyzer 220 may access the users’ interaction history databases 216 and use all the users’ interactions data available to identify possible new links between the questions. That is, if a high percentage of users ask a question Qy after Qx, then there is probably a link between the questions…”) to predict and generate a probable next prompt based on one or more current prompts, via a predictive model (Fig. 11, step 1108 “Predict the future question…” and ¶[0052] teaches “FIG. 11 is a flow diagram illustrating a method of predicting a future question…The method may predict and present a future question…an input question is received…At 1104, the automated question answering system may provide an answer…a conditional probability of one question to be asked given that a previous question was asked…the conditional probability may be determined based on, for example, one or more of a supervised learning algorithm, natural language processing that determines distance metric between the questions, and user data obtained from a plurality of sources…At 1108, the future question may be predicted based on the conditional probability stored in the question database given the input question…” The predicted question is interpreted as a probable next prompt).
Appel fails to disclose that the predicted next questions/prompt are stored on an edge server. Therefore, Appel fails to disclose transmitting the probable next prompt to an edge server for storage in a cache of the edge server.
Li teaches a predictive edge caching system and method that predicts the future content popularity using fine-grained leaning models that mine sequential patterns in user content retrieval behaviors. (Li, Abstract) More specifically, Li teaches transmitting the probable next prompt to an edge server for storage in a cache of the edge server (Page 3, Section 3 teaches “In edge caching, we want to minimize the content retrieval time by placing popular contents in an edge cache server close to users…”)
Appel differs from the claimed invention, as defined in claims 1, 8 and 15, in that Appel fails to specifically disclose caching/storing the predicted question in an edge sever. Caching content predicted to be retrieved is known in the art as evidenced by Li. Therefore, it would have been obvious to one skilled in the art before the effective filing date of the invention to have modified the question/answer retrieval system of Appel to include caching the predicted answer database in an edge server as taught by Li in order minimize the content retrieval time (Li, page 3, section 3).
Regarding claims 2, 9 and 16, the combination of Appel and Li teaches all of the elements of claims 1, 8 and 15 (see detailed element mapping above). In addition, Appel further teaches the one or more current prompts is an input to the predictive model, and wherein the probable next prompt is an output of the predictive model (Fig. 11, step 1108 teaches the future question is predicted based on conditional probability and ¶[0052] teaches that “the conditional probability may be determined based on…one or more supervised learning algorithm” and Fig. 2, ML + NLP Model 210 and ¶[0039] teaches “the dataset 208 is used to train a machine learning model 210 to create additional links to questions in the directed weighted graph…” the new links are interpreted as question probabilities thus the probable next prompt is an output the predictive model).
Regarding claims 6, 13 and 20, the combination of Appel and Li teaches all of the elements of claims 1, 8 and 15 (see detailed element mapping above). In addition, Li further teaches a plurality of probable next prompts comprise predicted prompts that are deleted in the cache based on a probability of occurrence of future prompts combined with idle time in the cache (Under a broadest reasonable interpretation, idle time in the cache is interpreted as a time before the content is predicted to be requested. Page 5, section 5.1 Time-sensitive predictive caching score teaches that the caching score is based on both the content popularity and time until it is predicted to be requested).
Appel differs from the claimed invention, as defined in claims 6, 13 and 20, in that Appel fails to specifically disclose the edge caching is time-sensitive. Time-sensitive edge caching is known in the art as evidenced by Li. Therefore, it would have been obvious to one skilled in the art before the effective filing date of the invention to have modified the question/answer retrieval system of Appel to include the time-sensitive caching of the predicted answer database in an edge server as taught by Li in order minimize the content retrieval time (Li, page 3, section 3).
Regarding claims 7 and 14, the combination of Appel and Li teaches all of the elements of claims 1 and 8 (see detailed element mapping above). In addition, Appel further teaches a system cache of a client device of the user is used to store user specific predictions to free up storage space in edge servers (¶[0069] teaches “The computer readable program instructions may execute entirely on the user’s computer, partly on the user’s computer…partly on the user’s computer and partly on a remote computer” Accordingly, the user specific predictions/answer database could be stored on the user’s computers which would inherently free up storage space on the edge server).
Claims 3-5, 10-12 and 17-19 are rejected under 35 U.S.C. 103 as being unpatentable over the combination of Appel and Li as applied to claims 1, 8 and 15 above, and further in view of Shao et al., "Transformer-Based Neural Network for Answer Selection in Question Answering," in IEEE Access, vol. 7, pp. 26146-26156, 2019l herein “Shao”.
Regarding claims 3, 10 and 17, the combination of Appel and Li teaches all of the elements of claims 2, 9 and 16 (see detailed element mapping above). In addition, Appel further teaches the probable next prompt is a predicted next prompt (¶[0003] teaches “The method may also include predicting the future question…given the input question as the previous question” The future question is interpreted as a predicted next prompt ), wherein based on the predicted prompt, that includes the predictive model generates responses for the predicted next prompt (¶[0003] teaches “The method may further include providing an answer to the future question”), and wherein processes predicted prompts and generates contextually relevant responses, incorporating semantic understanding of the input (¶[0003] teaches “The method may further include providing an answer to the future question…repeating the searching, the predicting, the suggesting and updating, with the future question as the input question…”).However, Appel fails to disclose or suggestion and a transformer model to utilized to answer the input and future questions.
Shao teaches a transformer-based neural network for question/answer systems. More specifically, Shao teaches “a Transformer-based neural network for answer selection, where we deploy a bidirectional long short-term memory (BiLSTM) behind the Transformer to acquire both global information and sequential features in the question or answer sentence.”
The combination of Appel and Li differs from the claims invention, as defined in claims 3, 10 and 17, in that the combination fails to specifically disclose utilizing a transformer-based model for generating the answers to the input and future questions. Transformer-based question/answer systems are known in the art as evidenced by Shao. Therefore, it would have been obvious to one having ordinary skill in the art to modify the question and answering system taught by the combination of Appel and Li to include utilizing a transformer-based model to generate that answers to the input and future questions in order to improve performance compared to competitive baselines such as RNN or CNN. (Shao, Abstract)
Regarding claims 4, 11 and 18, the combination of Appel, Li and Shao teaches all of the elements of claims 3, 10 and 17 (see detailed element mapping above). In addition, Appel further teaches a hierarchical structure of the predicted prompts is maintained (Figs. 3-10 all show that the directed graph is acyclic and therefore may be considered an hierarchical structure ).
In addition, Li further teaches deletions from the cache occur not only based on scheduled time but also based on the hierarchical structure of the predicted prompts (page 5, section 5.1, second column teaches “For each
u
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A
(
t
)
, based on here most recent content request sequence, we can generate the top-n list…of contents that u is mostly likely to request the next using the fusion model in Section 4.1.3….” Because the cache is time sensitive, the deletions are not only based on the predicted time of request but also the popularity which as shown in Fig. 2 has an hierarchical structure ).
Regarding claims 5, 12 and 19, the combination of Appel, Li and Shao teaches all of the elements of claims 4, 11 and 18 (see detailed element mapping above). In addition, Li further teaches the hierarchical structure is updated based on actual prompts when the actual prompts deviate from a prediction which would result in deletion of certain branches in the hierarchical structure that are likely to be skipped (Page 6, section 5.2.2 Content prefetching and replacement teaches that the predicted content cache is updated based on actual user requests for content and if there is a cache miss, i.e., the actual prompt is not in the predicted cache, the prompt is added to the cache and the predictive scores are updated).
Appel differs from the claimed invention, as defined in claims 5, 12 and 19, in that Appel fails to specifically disclose the predicted content/questions in the directed graph are updated when a prediction miss occurs. Updating a pre-fetch cache based on prediction miss is known in the art as evidenced by Li. Therefore, it would have been obvious to one skilled in the art before the effective filing date of the invention to have modified the question/answer retrieval system of Appel to include the pre-fetch predictive caching of as taught by Li in order minimize the content retrieval time (Li, page 3, section 3).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Lovic (US 20250165752 A1) teaches system and method for processing input data for large language models; and
Kovvuri et al. (US 2015/0281390 A1) teaches a pre-fetch cache based on access patterns.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to PENNY L CAUDLE whose telephone number is (703)756-1432. The examiner can normally be reached M-Th 8:00 am to 5:00 pm eastern.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Daniel Washburn can be reached at 571-272-5551. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/PENNY L CAUDLE/Examiner, Art Unit 2657
/DANIEL C WASHBURN/Supervisory Patent Examiner, Art Unit 2657
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