DETAILED ACTION
This office action is responsive to communication(s) filed on 3/2/2026.
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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 3/2/2026 has been entered.
Claims Status
Claims 1-20 are pending and are currently being examined.
Claims 1, 10 and 16 are independent.
Claims 1, 10, 13 and 16 are newly amended.
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 without significantly more.
Representative claim(s) 1 recite(s) steps of collecting and analyzing user data (including browser history , account data and user literacy level), creating a user record with stock information, determining a stock subset, retrieving relevant segment(s), generating a personalized output script and visuals, the personalized output script comprising a summarization of content of the one or more relevant segments, and displaying the output. None of these steps are precluded from performance in the human mind. E.g., “a script” can be generated and output mentally, a process known as "self-scripting," where people mentally create step-by-step thought processes to influence their behavior/thoughts. Furthermore, humans are capable of reviewing information and outputting a summary of the reviewed information. Therefore, the claim recites the abstract idea of a mental process. Note that data collection, analysis, and displaying results, have been considered abstract ideas, see MPEP 2106.04(a) and Electric Power Group v. Alstom, S.A., 830 F.3d 1350, 1353-54, 119 USPQ2d 1739, 1741-42 (Fed. Cir. 2016).
This judicial exception is not integrated into a practical application because although including additional elements, like “computer-implemented”, by “one or more processors”, data retrieval “from a vector database”, steps performed by a “machine learning model” these are reflective of mere instructions to implement an abstract idea on a computer or merely using a computer as a tool to perform an abstract idea (see MPEP 2106.05(f)) and/or generally linking the use of the judicial exception to a particular technological environment or field of use, e.g., of vector databases and machine learning – see MPEP 2106.05(h). These are not indicative of integration into a practical application.
The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because none of mere instructions to implement an abstract idea on a computer, merely using a computer as a tool to perform an abstract idea, and generally linking the use of the judicial exception to a particular technological environment or field of use, are indicative of significantly more.
As such claim 1 is not patent eligible under 101.
Claims 10 and 16 includes similar limitations and are also rejected for similar reasons.
Claims 2-9, 11-15 and 17-20 do nothing more than further recite the abstract idea and/or additional limitations that are reflective of mere instructions to implement an abstract idea on a computer or merely using a computer as a tool to perform an abstract idea and/or generally linking the use of the judicial exception to a particular technological environment or field of use, and are rejected for similar reasons. E.g., the claims apply the abstract idea to computers and the technological environment or field of use of “machine learning” and “vector databases”.
Claim Rejections - 35 USC § 112(a)
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention.
Claims 1-20 are rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention.
Claims 1, 10 and 16 involves a machine learning model that creates a user record. However, the Instant Specification doesn’t sufficiently disclose that the machine-learning model creates the user record.
Claims 2-9, 11-15, and 17-20 are also rejected as they depend on the claim(s) above.
Claim Rejections - 35 USC § 112(b)
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 1-20 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 1, 10 and 16 involves a machine learning model that creates a user record. Here, this is unclear because there is a lack of correspondence between the specification and the claims. The specification describes a machine-learning model that generates scripts based on user records, wherein the records are created by one or more processors of a computing device, ¶¶ 46 and 52 (as published, hereinafter, “as pub”), and not that the machine-learning model creates a user record. A claim, although clear on its face, may also be indefinite when a conflict or inconsistency between the claimed subject matter and the specification disclosure renders the scope of the claim uncertain as inconsistency with the specification disclosure or prior art teachings may make an otherwise definite claim take on an unreasonable degree of uncertainty. In re Moore, 439 F.2d 1232, 1235-36, 169 USPQ 236, 239 (CCPA 1971); In re Cohn, 438 F.2d 989, 169 USPQ 95 (CCPA 1971); In re Hammack, 427 F.2d 1378, 166 USPQ 204 (CCPA 1970). See MPEP 2173.03. For purposes of compact prosecution only, the examiner interprets the limitation(s) as referring to a computer using machine learning infrastructure for create user records. Correction required.
Claims 2-9, 11-15, and 17-20 are also rejected as they depend on the claim(s) above.
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 of this title, 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) 1-2, 4-7, 9-13, 15-17 and 19-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Guinn; Devon et al. (hereinafter Guinn – US 20240412030 A1) in view of Langdon, JR.; Charles A. (hereinafter Langdon – US 20210295434 A1) and Lee; Ji-yeon et al. (hereinafter Lee – US 20180150905 A1).
Independent Claim 1:
Guinn teaches:
A computer-implemented method for generating a customized output (computer system/method, Abstract and fig. 1)
based on account data of a user, (e.g., user identifier, ¶ 119, personas, embeddings, memories, ¶¶ 81 and 130)
the computer-implemented method comprising:
receiving, by one or more processors, user data (during a training phase, input information of related to user is received, e.g., user’s favorites/preferences and converted into embeddings, ¶¶ 106-107 and figs. 6, and these embeddings are stored in association with the user, Abstract and ¶¶ 57-59, e.g., in a knowledge layer, ¶ 100. Using such inputs, users create personalized AI personas by employing natural language, documents, and UI-based customization to define specific knowledge, memories, and communication styles, which are then used to perform targeted tasks for the user or to interact with a broader audience, Abstract and ¶ 81. Note that Guinn makes references a “first” and “second individual”, a “second individual” can refer to the same or different person as a “first individual”, ¶ 5)
wherein the user data includes user browser history, (user related information includes, e.g., memories including query history, ¶ 46, wherein the queries are in a Web Browser environment [user browser history], ¶ 55. )
user literacy level, (Paragraph 78 describes using digital avatars for tailored education, which implies adapting content to user literacy and comprehension levels through "personalized educational experiences" (adaptive learning). This is supported by ¶ 33 of Guinn, which teaches that neural network outputs are customized based on an individual's knowledge, directly connecting, in this context, to user literacy, ¶¶ 33 and 78.)
and user account data; (e.g., user identifier, ¶ 119, embeddings/memories, ¶ 130)
creating, by a machine learning model, a user record based on the user account data, (a persona is created by a training engine [machine learning infrastructure] for a user, ¶ 52. This paragraph reflects a machine learning infrastructure that is creating a user record because it describes a training engine that is collecting user-provided or selected information to update a classifier or regression model, which in turn acts as a personalized, stored "persona" [or user record]. Information of the user may be stored, e.g., in association with an embedding [user record] data structure, ¶¶ 118-119)
[…];
retrieving, by the one or more processors, one or more relevant segments from a vector database, (embeddings of the user related information are stored as portion(s) [segments] of vectorized knowledge and used to match, access, search, and use the vast amount of data in the knowledge layer [retrieving], ¶¶ 102 and 130)
wherein the one or more relevant segments correspond to one or more relevant events (the relevant segments may be used for representing and matching certain topics [correspond to one or more relevant events] about the user, e.g., favorite foods, preferences, etc., ¶¶ 107 and 133)
that are relevant to the [financial investments], (the system offers personalized financial advice and investment recommendations, ¶ 79)
[…];
generating, by the machine learning model, a personalized output script based on the one or more relevant segments (as noted above, the system offers “personalized” financial advice and investment recommendations, ¶ 79. A user can train a persona by creating custom scripts to “ensuring that each interaction is unique and tailored to the needs of the users' audience” [personalized output script], ¶ 86. A computation engine can automatically perform reinforced learning or generate a revised classier or regression model, ¶ 53. As such, this is reflective of an automatic reinforcement training of a digital human avatar, combined with user-created custom scripts, and is considered a form of generating or dynamically refining scripts [personalized output script]. In this context, the training process uses user input to shape the AI's behavior, allowing the avatar to generate unique, context-aware responses or scripts in real-time. Because the computation engine is modifying the training/persona, it is considered part of the machine learning model [generating, by the machine learning model, a personalized output script])
and the user literacy level, […]; (As explained above, in reference to ¶¶ 33 and 78, Guinn teaches that neural network outputs are customized based on an individual's knowledge, directly connecting, in this context, to user literacy, ¶¶ 33 and 78.)
creating, by the machine learning model, an output based on the personalized output script, (a personalized response [generating…a personalized output script] is generated [creating…an output based on the personalized output script] by the neural network [machine learning model], ¶¶ 138-139 and fig. 9:938)
the output including one or more graphic visuals corresponding to the personalized output script; (the output may be in the form of displayable text [one or more graphic visuals], fig. 9:946 and ¶¶ 139-140)
and displaying, by the one or more processors, the output on one or more user interfaces of a user device. (dialogue output, e.g., displayed via text, fig. 9:94-946 and ¶¶ 139-140)
Guinn further suggests:
that the user data is received from one or more databases, (Guinn teaches the concept of retrieving information from database(s), e.g., vector database, ¶ 118, and that the data can be pulled from external sources, for extending the versatility and reach of the method, ¶ 88)
Accordingly, it would have been obvious to a person having ordinary skill in the art, before the effective filing date of the claimed invention, to modify the method of Guinn to include that the user data is received from one or more database, as suggested by Guinn.
One would have been motivated to make such a combination in order to improve the versatility and reach of the method by obtaining information from external databases, Guin ¶¶ 88 and 118.
Guinn does not appear to expressly teach, but Langdon teaches:
wherein the user record includes one or more stocks; (Searches, include information such “stock symbols” [one or more stocks], Langdon ¶ 125)
determining, by the one or more processors, a stock subset of the one or more stocks; (e.g., filter by top ranked companies, ¶ 138)
that the financial investment are the stock subset (e.g., filter by top ranked companies, ¶ 138)
Accordingly, it would have been obvious to a person having ordinary skill in the art, before the effective filing date of the claimed invention, to modify the method of Guinn to include wherein the user record includes one or more stocks, determining, by the one or more processors, a stock subset of the one or more stocks, and that the financial investment are the stock subset, as taught by Langdon.
One would have been motivated to make such a combination in order to improve the versatility of the method by modifying the method to apply to financial stock investment advice in a fast and functional way, Langdon 26 and Guinn ¶ 79.Guinn-Langdon does not appear to expressly teach, but Lee teaches:
the personalized output script comprising a summarization of content of the one or more relevant segments (a machine learning unit that is able to generate summarized content related to one or more segments, e.g., summarization range selection, Abstract and ¶¶ 3, 21 and 57, and figs. 5A-5C).
Accordingly, it would have been obvious to a person having ordinary skill in the art, before the effective filing date of the claimed invention, to further modify the method of Guinn to include the personalized output script comprising a summarization of content of the one or more relevant segments, as taught by Lee.
One would have been motivated to make such a combination in order to improve the efficiency and convenience offered by the method, e.g., by provide the user the ability to check/understand “a large amount of content more quickly and conveniently”, Lee ¶ 5.
Claim 2:
The rejection of claim 1 is incorporated. Guinn further teaches:
the computer-implemented method further comprising:
storing, by the one or more processors, the output in a database record in the one or more databases, (the data structure or database return relevant memories or knowledge to the correct user, ¶ 130, which includes prior conversations, ¶ 66)
wherein the database record includes a reference to the user record. (user identifier used to match to correct user, ¶ 130)
Claim 4:
The rejection of claim 1 is incorporated. Guinn further teaches:
wherein the output includes at least one of:
a video, a text-based output, or an audio output. (the output may include text, audio, and/or an image, ¶ 8)
Claim 5:
The rejection of claim 1 is incorporated. Guinn, as modified, Langdon further teaches:
the computer-implemented method further comprising:
ranking, by the one or more processors, the one or more stocks of the user record based on a priority, wherein the priority is based on one or more heuristic rules. (Guinn teaches that the computational techniques include machine learning techniques [based on one or more heuristic rules], Langdon teaches ranking the investment data, ¶¶ 80 and 100, e.g., by top ranked companies, based on certain criteria ¶ 138, and rules, e.g., machine learning logic [based on one or more heuristic rules], ¶ 80)
Claim 6:
The rejection of claim 5 is incorporated. Guinn, as modified, Langdon further teaches:
wherein the ranking is performed by a machine-learning model. (Langdon teaches ranking the investment data, ¶¶ 80 and 100, e.g., by top ranked companies, based on certain criteria ¶ 138, and rules, e.g., machine learning logic [based on one or more heuristic rules], ¶ 80)
Claim 7:
The rejection of claim 1 is incorporated. Guinn further teaches:
wherein retrieving the one or more relevant segments from the vector database comprises:
building, by the one or more processors, the vector database; (converting entries to vector-based embeddings, e.g., see ¶¶ 107 and 126)
and querying, by the one or more processors, the vector database for the one or more relevant events and the corresponding one or more relevant segments. (searching [querying] the embeddings for matching certain criteria [relevant events] and user identifier [relevant segments], ¶¶ 102)
Claim 9:
The rejection of claim 7 is incorporated. Guinn, as modified, further teaches:
wherein querying the vector database comprises:
receiving, by the one or more processors, the stock subset; (Guinn teaches information of related to user is received, e.g., user’s favorites/preferences, ¶ 107 and figs. 6, and stored in association with the user, Abstract and ¶¶ 57-59. Langdon teaches that the information includes one or more stocks, Langdon ¶ 125)
processing, by the one or more processors, stock information corresponding to each of the one or more stocks in the stock subset to produce one or more vector queries, (the vectorized information is used for searching/querying, Guinn ¶¶ 44 and 102. Langdon teaches that the information includes one or more stocks, Langdon ¶ 125)
the stock information including at least one stock ticker, at least one name, or at least one company name; (stock ticker, or company name, Langdon ¶ 151)
and querying, by the one or more processors, the vector database for information related to each of the one or more vector queries. (the vectorized information is used for searching/querying, Guinn ¶¶ 44 and 102, and is stored in a database, Guinn ¶ 118)
Independent Claims 10 and 16:
Claim(s) 10 and 16 is/are directed to a system and a medium for accomplishing the steps of the method in claim 1, and are rejected using similar rationale(s).
Claims 11 and 20:
The rejection of claims 10 and 16 are incorporated. Claim(s) 11 and 20 is/are directed to a system and a medium for accomplishing the steps of the method in claim 4, and are rejected using similar rationale(s).
Claim 12:
The rejection of claim 10 is incorporated. Claim(s) 12 is/are directed to a system for accomplishing the steps of the method in claim 5, and is rejected using similar rationale(s).
Claim 13:
The rejection of claim 12 is incorporated. Claim(s) 13 is/are directed to a system for accomplishing the steps of the method in claim 6, and is rejected using similar rationale(s). However, note the correction required, as explained in 112(b) rejection section above, due to insufficient antecedent basis.
Claims 15 and 17:
The rejection of claim 10 and 16 are incorporated. Claim(s) 15 and 17 is/are directed to a system and a medium for accomplishing the steps of the method in claim 7, and are rejected using similar rationale(s).
Claim 19:
The rejection of claim 17 is incorporated. Claim(s) 19 is/are directed to a medium for accomplishing the steps of the method in claim 9, and are rejected using similar rationale(s).
Claim(s) 3 and 14 is/are rejected under 35 U.S.C. 103 as being unpatentable over Guinn (US 20240412030 A1) in view of Langdon (US 20210295434 A1) and Lee; Ji-yeon et al. (hereinafter Lee – US 20180150905 A1), as applied to claims 1 and 10 above, and further in view of Luo; Ping et al. (hereinafter Luo – US 20150127602 A1).
Claim 3:
The rejection of claim 1 is incorporated. Guinn teaches that customization of output may be based on a second individual different from a first, Abstract.
Guinn-Langdon-Lee does not appear to expressly teach, but Luo teaches:
wherein the one or more stocks include at least one of:
at least one stock owned by the user, at least one stock previously owned by the user, at least one stock followed by the user, at least one stock owned by one or more other users that the user follows, at least one stock watched by the one or more other users that the user follows, at least one stock owned by one or more other users with a similar portfolio, or at least one stock watched by the one or more other users with the similar portfolio. (an investment portfolio recommendation application that mines the data of experienced investors to identify quality or interesting patterns and make investment plan recommends to new investors based on these patterns, ¶ 14).
Accordingly, it would have been obvious to a person having ordinary skill in the art, before the effective filing date of the claimed invention, to further modify the method of Guinn to include wherein the one or more stocks include at least one of: at least one stock owned by the user, at least one stock previously owned by the user, at least one stock followed by the user, at least one stock owned by one or more other users that the user follows, at least one stock watched by the one or more other users that the user follows, at least one stock owned by one or more other users with a similar portfolio, or at least one stock watched by the one or more other users with the similar portfolio, as taught by Luo.
One would have been motivated to make such a combination in order to improve the quality of investment recommendations provided by the method, Luo ¶ 14.
Claim 14:
The rejection of claim 10 is incorporated. Claim(s) 14 is/are directed to a system for accomplishing the steps of the method in claim 3, and is rejected using similar rationale(s).
Claim(s) 8 and 18 is/are rejected under 35 U.S.C. 103 as being unpatentable over Guinn (US 20240412030 A1) in view of Langdon (US 20210295434 A1) and Lee; Ji-yeon et al. (hereinafter Lee – US 20180150905 A1), as applied to claims 7 and 17 above, and further in view of Smith Lewis; Andrew et al. (hereinafter Smith – US 20240289863 A1).
Claim 8:
The rejection of claim 7 is incorporated. Although the added limitations of this claim seem to be common steps for building and maintaining a vector database, assuming arguendo that that they are not, Guinn-Langdon-Lee does not appear to expressly teach, but Smith teaches:
wherein the building the vector database comprises:
for one or more time periods, analyzing, by the one or more processors, one or more sources of information; (e.g., user profile information is used for creating vectors, ¶ 72, and the profile is continually and periodically updated [analyzing] to ensure accuracy and personalization of system outputs, ¶ 116)
converting, by the one or more processors, data of the one or more sources to one or more short segments using a machine-learning model; (splitting documents into smaller chunks, ¶ 46, machine learning usable for the processing, ¶ 60)
storing, by the one or more processors, the one or more short segments in the one or more databases; (storing the chunks, ¶ 46, e.g., in a vector database, ¶ 49)
processing, by the one or more processors, the one or more short segments to determine a corresponding latent vector for each of the one or more short segments; (chunks are pass through an embedding model to generate vectors [latent vector or embedding], ¶ 50)
and storing, by the one or more processors, the processed one or more short segments and the corresponding latent vector in the vector database (vectors and chunks are stored in vector database, ¶ 49).
Accordingly, it would have been obvious to a person having ordinary skill in the art, before the effective filing date of the claimed invention, to further modify the method of Guinn to include wherein the building the vector database comprises: for one or more time periods, analyzing, by the one or more processors, one or more sources of information; converting, by the one or more processors, data of the one or more sources to one or more short segments using a machine-learning model; storing, by the one or more processors, the one or more short segments in the one or more databases; processing, by the one or more processors, the one or more short segments to determine a corresponding latent vector for each of the one or more short segments; and storing, by the one or more processors, the processed one or more short segments and the corresponding latent vector in the vector database, as taught by Smith.
One would have been motivated to make such a combination in order to ensure the accuracy and personalization of outputs produced by applying the method, Smith ¶ 116.
Claim 18:
The rejection of claim 17 is incorporated. Claim(s) 18 is/are directed to a medium for accomplishing the steps of the method in claim 8, and is rejected using similar rationale(s).
Response to Arguments
112(b) rejection of claim 13 in previous office action has been overcome by claim amendment.
Applicant's 101 arguments have been fully considered but they are not persuasive.
101 Argument(s)/Response(s):
First, the applicant alleges the Instant Specification describes a “practical application” of the abstract idea by disclosing the increasing of “user engagement by providing users with interactive an engaging financial recaps”, and that producing the “out scripts, such as via a trained machine learning model, based on a user’s literacy level, may provide a quick and automatic way to display aggregated financial data that would not be possible using conventional solution”, and that the such “output would not be possible or practical to perform manually, let along in context with the other features recited in the independent claims”. (See Remarks Page 12)
The examiner respectfully disagrees because:
creating scripts tailored to literacy levels is a mental process that humans can perform rendering the automation of this task a mere "speed and efficiency" improvement rather than a technological solution.
Since the machine learning model simply replicates this abstract human activity on a computer, it lacks the "inventive concept" required to transform the idea into a patent-eligible practical application.
Consequently, the increased user engagement and automation are considered incidental benefits of implementing on a computer or particular technological environment or field of use, rather than a specific improvement to the technological field itself. The instant invention doesn’t improve the field of machine learning, is simply implements machine learning for speed and efficiency.
Second, the applicant compares the instant invention to the claimed invention in Ex Parte Desjardins, which included adjusting value of parameters to optimize performance of the machine learning model, and alleging that the instant claims also “recite a specific and particular manner to optimize the performance of the machine learning model via creating an output based on user data and the resulting personalize output script, and thus integrate the alleged abstract idea into a practical application” (Remarks Page 13).
The examiner respectfully disagrees because the instant invention merely applies a standard machine learning model to user data to generate an output, which is a generic implementation of a concept rather than a technical improvement to the model's functionality. This is nothing like the claim in Ex Parte Desjardins, which involved specific, active modification of parameters to optimize the model's training process itself—a technical, non-abstract improvement to the machine learning technology. The instant invention neither involves adjusting parameters of a machine model for optimizing the performance of the machine learning model itself, nor does it do anything else to optimize the performance of the model or to contribute any benefit to the machine learning technology.
Third, the applicant alleges that the instant invention uses the abstract idea of determining an output script in a manner that is meaningful, by using it to “identify a personalized output script and then display[ing]” the script. Remarks Pages 13-14.
The examiner respectfully disagrees because:
As explained in the response(s) above and/or 101 rejection section above, a personalized script can be produced by a human mind. E.g., “a script” can be generated and output mentally in a process known as "self-scripting," where people mentally create step-by-step thought processes to influence their behavior/thoughts.
Furthermore, the step of displaying is a mental step. Humans are capable of reviewing information and outputting a summary of the reviewed information. Displaying information has been shown by the courts to be an example of a mental process. See MPEP 2106.04(a)(2).III.A and Electric Power Group v. Alstom.
103 Argument(s)/Response(s):
Applicant's 103 arguments have been fully considered but they are unpersuasive or otherwise moot in view of the new ground(s) of rejection presented above.
First, the applicant alleges that the cited art fails to teach each and every element of the pending claims, Remarks Page 15, because they do not teach newly added limitations related to machine learning model and user literacy level, Remarks Page 16.
At least in this general sense, this argument is moot based on the new grounds of rejection presented above.
Second, the applicant alleges that Guinn’s disclosure of “personalized education experiences” and “personalized recommendations to users” is “different from a literacy level”, and that “the Office is stretching and reading features into the references that are not there”. Remarks Page 16.
The examiner respectfully disagrees because:
It was well within the capabilities of a person having ordinary skill in the art to have realized that Guinn’s “personalized education experiences” (also known as “adaptive learning”) imply different “education” levels (literacy level) in order to tailor education material differently for each student.
Furthermore, even if this implication was not clear enough, it is not a “stretch” because this implication is clearly supported by other portions of Guinn. E.g., Paragraph 78 describes using digital avatars for tailored education, which implies adapting content to user literacy and comprehension levels through "personalized educational experiences" (adaptive learning). And ¶ 33, teaches that neural network outputs are customized based on an individual's knowledge, directly connecting, in this context, to user literacy, ¶¶ 33 and 78.
Third, the applicant relies on the arguments above to allege patentability of the remaining claims.
The examiner respectfully disagrees for the reasons above.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Below is a list of these references, including why they are pertinent:
Hwang; Jin-young US 20190042551 A1, is pertinent to claim 1 for disclosing AI technology is composed of machine learning (for example, deep learning), ¶ 5, which generates summary information based on user history information, e.g., knowledge level [literacy], ¶¶ 51, 90 and 127.
Sajda; Paul et al. US 20240005398 A1, is pertinent to claim 1 for disclosing a trading platform that generates custom user interfaces for multiple users, ¶ 850, outputting expert trading decisions, Abstract.
Gambhir; Prerana Dharmesh US 20240045581 A1, is pertinent to claim 1 for disclosing a system for optimizing and personalizing the home screen of an application, Abstract, where a user’s profile information may include user preference information, location information, search history, stock tracking history, and the like. Widget types may include weather widgets, social media widgets, productivity application widgets, news widgets, stock widgets, and so forth. Given the known history of a user, such as their browsing history, ¶ 33.
Currell; Nathan et al. US 20250068625 A1, is pertinent to claim 1 for disclosing methods for processing natural language queries, Abstract, including collecting personalized financial information, ¶ 63, and soring chunks of information in a vector database, ¶ 75.
Fleming; Kala et al. US 20190057071 A1, is pertinent to claim 1 for disclosing delivering a summarization of the digital content to the at least one device where the summarizations are tailored [personalized] to a user in an educational setting, Abstract and ¶ 20.
Kumbure, Mahinda Mailagaha et al., Non-Patent Literature, Machine learning techniques and data for stock market forecasting: A literature review (2022), is pertinent to claim 1 for disclosing machine learning techniques that are applied for stock market prediction, Abstract.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to GABRIEL S MERCADO whose telephone number is (408)918-7537. The examiner can normally be reached Mon-Fri 8am-5pm (Eastern Time).
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/Gabriel Mercado/Primary Examiner, Art Unit 2171