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 . 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.
Specification
The lengthy specification has not been checked to the extent necessary to determine the presence of all possible minor errors. Applicant’s cooperation is requested in correcting any errors of which applicant may become aware in the specification.
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 12-14 and 18-21 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter.
The claims do not fall within at least one of the four categories of patent eligible subject matter because the broadest reasonable interpretation of a claim drawn to a computer readable medium (also called machine readable medium, computer readable storage medium, and other such variations) typically covers forms of non-transitory tangible media and transitory propagating signals per se in view of the ordinary and customary meaning of computer readable media, particularly when the specification is silent. See MPEP 2111.01. When the broadest reasonable interpretation of a claim covers a signal per se, the claim must be rejected under 35 USC 101 as covering non-statutory subject matter. See In re Nuijten, 500 F.3d 1346, 1356-57 (Fed. Cir. 2007) (transitory embodiments are not directed to statutory subject matter).
Claims reciting a musical composition, literary work, compilation of data, signal, or legal document (e.g., an insurance policy) per se do not appear to be a process, machine, manufacture, or composition of matter. See, e.g., In re Nuijten, 500 F.3d 1346, 1356-57 (Fed. Cir. 2007) (“A transitory, propagating signal like Nuijten’s is not a process, machine, manufacture, or composition of matter.’ … Thus, such a signal cannot be patentable subject matter.”).
A claim drawn to such a computer readable medium that covers both transitory and non-transitory embodiments may be amended to narrow the claim to cover only statutory embodiments to avoid a rejection under 35 USC 101 by adding the limitation "non-transitory" to the claim. Cf. Animals -Patentability, 1077 Off. Gaz. Pat. Office 24 (April 21, 1987) (suggesting that applicants add the limitation "non-human" to a claim covering a multi-cellular organism to avoid a rejection under 35 USC 101).
Claims 1-6, 11-14, 18-21, 23-27, and 29, are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
When considering subject matter eligibility under 35 U.S.C. 101, it must be determined whether the claim is directed to one of the four statutory categories of invention (i.e., process, machine, manufacture, or composition of matter) (step 1). If the claim does fall within one of the statutory categories, it must then be determined whether the claim is directed to a judicial exception (i.e., law of nature, natural phenomenon, and abstract idea) (step 2A), and if so, it must additionally be determined whether the claim is a patent-eligible application of the exception (step 2B). Alice Corp. Pty. Ltd. v. CLS Bank Int'l, 134 S. Ct. 2347, 189 L. Ed. 2d 296, 2014 U.S. LEXIS 4303, 110 U.S.P.Q.2D (BNA) 1976, 82 U.S.L.W. 4508, 24 Fla. L. Weekly Fed. S 870, 2014 WL 2765283 (U.S. 2014); MPEP 2106.
Step 1:
In the instant case claims 1-6 and 11 are directed to a machine and claims 23-27 and 29 are directed to a process. Claims 1-6, 11, 23-27, and 29, are therefore within statutory categories. Claims 12-14 and 18-21 are not as explained above. In the interest of compact prosecution all claims will nonetheless be considered herein. See MPEP 2106.03, Eligibility Step 1.
Step 2A, Prong 1:
These claims also recite, inter alia,
“(A) receiving, from the service provider … a request for the data product personalized to the user, the request comprising: (i) information identifying or that can be used to identify at least one data source storing user data for the user that the user has authorized the data product personalization service to access; and (ii) information indicating how to transform at least some of the user data to generate the data product personalized to the user; (B) obtaining, using the information identifying or that can be used to identify the at least one data source, the at least some of the user data from, or previously retrieved from, the at least one data source; (C) generating, using the information indicating how to transform the at least some of the user data and a data transformation service, the data product personalized to the user; and (D) providing, to the service provider … the generated data product personalized to the user.” Claim 23.
With recited additional elements reserved for consideration under step 2A prong two, a careful analysis of the remaining limitations above, each on its own and all together combined, results in the conclusion that each on its own recites an abstract idea and in combination they simply recite a more detailed abstract idea. The recited abstract idea falls within the grouping of abstract ideas described as certain methods of organizing human activity, for example managing personal behavior or relationships or interactions between people (including social activities, teaching, and following rules or instructions). See MPEP 2106.04(a); Eligibility Step 2A1. The claims must therefore be analyzed under the second prong of Eligibility Step 2 (Step 2A2; MPEP 2106.04(d)).
Step 2A, Prong 2:
In order to address prong 2 (MPEP 2106.04(d), Eligibility Step2A2) we must identify whether there are any additional elements beyond the abstract ideas and determine whether those additional elements (if there are any) integrate the abstract idea into a practical application. MPEP 2106.04(d), Eligibility Step 2A2. The additional elements in the present claims are at least one communication network in all claims. Claims 1-6, 11, 23-27, and 29, also include at least one hardware processor (claims 12-14 and 18-21 include only the potential of execution by a processor). These additional elements have been considered individually, in combination, and altogether as a whole together with the functions they perform, e.g., the network performs no particular function other than its presence as a communication medium, and the processor along with variously recited programming elements, is generically recited as broadly and generally performing all steps in terms of the intended results of functionally nonspecific activities (i.e., “apply it”).
These additional elements do not integrate the judicial exception into a practical application because they amount to no more than mere instructions to apply the exception using generic computer components. The claims are almost entirely a recitation of abstract ideas. The substantive process is recited only by descriptions of abstract intended results of steps without indicating any particular functional acts performed by any device or structural element to perform the steps or otherwise obtain the intended results. The additional elements do not improve the functioning of any computer or other technology or technical field, they do not apply the judicial exception with or by use of a particular machine, they do not transform or reduce a particular article to a different state or thing, and they fail to apply or use the judicial exception beyond generally linking the use of the judicial exception to a particular technological environment. See MPEP 2106.05.
If the disclosure describes any improvements to the functioning of a computer or to any other technology or technical field this improvement would need to be identifiable as the subject matter appearing in the claims. An indication that the claimed invention provides an improvement can include a discussion in the specification that identifies technical improvements realized by the claim over the prior art. The disclosure must provide sufficient details such that one of ordinary skill in the art would recognize the claimed invention as providing an improvement. MPEP 2106.05(a).
Claim limitations can integrate a judicial exception into a practical application by implementing the judicial exception with or using it in conjunction with a particular machine or manufacture that is integral to the claim. A general purpose computer that applies a judicial exception by use of generic computer functions does not qualify as a particular machine. Ultramercial, Inc. v. Hulu, LLC, (Fed. Cir. 2014); MPEP 2106.05(b),(f). There are no particular machines or manufactures identified in the present claims. Elements that are not abstract are identified broadly and generally as applying the method, and the method itself is described only by way of the intended functional results of unidentified activities, without reference to any particular functional acts or specific functions performed by any particularly identified machines, and without reference to its use in conjunction with any particular item of manufacture.
The claims do not affect the transformation or reduction of a particular article to a different state or thing. Changing to a different state or thing means more than simply using an article or changing the location of an article. A new or different function or use can be evidence that an article has been transformed. Purely mental processes in which data, thoughts, impressions, or human based actions are "changed" are not considered a transformation. MPEP 2106.05(c).
The claims do not apply or use the judicial exception in any other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment. As a result the claim as a whole appears to be a drafting effort designed to monopolize the exception. MPEP 2106.05(e),(h).
The additional elements have not been found to integrate the abstract idea into a practical application.
Step 2B:
Although the additional elements have not been found to integrate the abstract idea into a practical application the claims could still be eligible if they recite additional elements that amount to an inventive concept (“significantly more” than the judicial exception). MPEP 2106.05, Eligibility Step 2B.
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the sparse additional elements of the claim are mere props supporting instructions to implement an abstract idea or other exception on a computer. MPEP 2106.05(f). The claims invoke computers or other machinery merely as tools to perform an abstract process. Adding a general purpose computer or computer components after the fact to an abstract idea does not provide significantly more. MPEP 2106.05(f)(2); see also OIP Techs., Inc. v. Amazon.com, Inc., 788 F.3d 1359, 2015 U.S. App. LEXIS 9721, 115 U.S.P.Q.2D (BNA) 1090 (Fed. Cir. 2015) (“relying on a computer to perform routine tasks more quickly or more accurately is insufficient to render a claim patent eligible.”). Elements are recited at a high level of generality, merely implement abstract ideas using generic computers, and fail to present a technical solution to a technical problem created by the use of the surrounding technology. No technical problem is indicated and the claims are not directed to a technical solution to any such problem. Limitations that amount to merely indicating a field of use or technological environment in which to apply a judicial exception do not amount to significantly more than the exception itself. See Ret. Capital Access Mgmt. Co. v. U.S. Bancorp, 611 Fed. Appx. 1007, 2015 U.S. App. LEXIS 14351 (Fed. Cir. 2015) (“It may be very clever; it may be very useful in a commercial context, but they are still abstract ideas,” said Circuit Judge Alan Lourie.). MPEP 2106.05(h).
Finally, it is reiterated that the remaining dependent claims 2-6, 11, 13-14, 18-21, 24-27, and 29, do not contribute any additional elements other than those already discussed and do not add "significantly more" to establish eligibility because they merely recite additional abstract ideas that further identify the data and manipulations of data used in implementing the abstract idea. A more detailed abstract idea is still abstract. PricePlay.com, Inc. v. AOL Adver., Inc., 627 Fed. Appx. 925, 2016 U.S. App. LEXIS 611, 2016 WL 80002 (Fed. Cir. Jan. 7, 2016) (in addressing a bundle of abstract ideas stacked together during oral argument, U.S. Circuit Judge Kimberly Moore said, "All of these ideas are abstract…. It’s like you want a patent because you combined two abstract ideas and say two is better than one.").
All of the above leads to the conclusion that additional claim elements do not provide meaningful limitations to transform the claimed subject matter into significantly more than an abstract idea. MPEP 2106.05; Eligibility Step 2B. As a result the claims are rejected under 35 USC 101 as being directed to non-statutory subject matter because they recite an abstract idea without being directed to a practical application, and they do not amount to significantly more than the abstract idea. MPEP 2106.05, supra..
The preceding analysis applies to all statutory categories of invention. Accordingly, claims 1-6, 11-14, 18-21, 23-27, and 29, are rejected as ineligible for patenting under 35 USC 101 based upon the same analysis.
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claims 1, 12, and 23, are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Zheng et al. (Patent No.: US 12,099,558 B2).
Zheng teaches all the limitations of claims 1, 12, and 23. For example Zheng discloses a data product personalization service for a service provider and a data product personalized to a user with whom the service provider interacts. Zheng further teaches, pertaining to
Claim 1. A system for generating, by a data product personalization service and for a service provider, a data product personalized to a user with whom the service provider is to interact, the system comprising: ● at least one hardware processor (see at least Zheng abstract “The electronic device includes at least one processor”); and ● at least one computer-readable storage medium storing processor-executable instructions (see at least Zheng c2:5-10 “The computer program product includes a computer readable medium having stored thereon instructions, which when executed, cause at least one processor to carry out the above method”) that, when executed by the at least one hardware processor, cause the at least one hardware processor to perform a method comprising: ● (A) receiving, from the service provider and via at least one communication network, a request for the data product personalized to the user (see at least Zheng fig. 18, c19:15-25 “personalization information to be provided to the CP … may be … information requested by the CP,” c27:1-25 “terminal device of the user A may send a personalization information acquisition request to the terminal device B of the target user B to obtain the target user B's personalization information,” c41:5-15 “a communication between the electronic device 2000 and another device, to implement data reception and transmission”), the request comprising: ● (i) information identifying or that can be used to identify at least one data source storing user data for the user that the user has authorized the data product personalization service to access (see at least Zheng figs. 2-3, 11, 13, c24:25-30 “terminal device may determine a content of interest to the user according to the stored user's personalization information,” c25:55-60 “terminal device may collect another user's personalization information according to the user's need, and may share suitable personalization information between users according to the user's request and authentication”); and ● (ii) information indicating how to transform at least some of the user data to generate the data product personalized to the user (see at least Zheng abstract “electronic device for providing more accurate contents to a user based on obtained personalization information. … analyze the collected information of the user for generating a personal knowledge graph of the user, provide the personalization information generated based on the personal knowledge graph to a content provider,” figs. 9-13, 18); ● (B) obtaining, using the information identifying or that can be used to identify the at least one data source, the at least some of the user data from, or previously retrieved from, the at least one data source (see at least Zheng abstract “obtained personalization information. … collect information of a user based on user activities on the electronic device, analyze the collected information of the user for generating a personal knowledge graph of the user,” figs. 1, 13, 19, c6:60-70 “obtaining the user's most accurate personalization information based on the user's personalization information provided by the terminal device”); ● (C) generating, using the information indicating how to transform the at least some of the user data and a data transformation service, the data product personalized to the user (see at least Zheng abstract “provide the personalization information generated based on the personal knowledge graph to a content provider (CP), receive at least one content which is provided from the CP, and provide recommendation content selected from among the a least one content based on the personalized information,” figs. 6, 12, c28:15-25 “determined content in the interest template may be directly used as a content of interest to the target user, or the post-processing may be performed based on the determined content in the interest template to obtain a content of interest to the target user”); and ● (D) providing, to the service provider and via the at least one communication network, the generated data product personalized to the user (see at least Zheng abstract “provide the personalization information generated based on the personal knowledge graph to a content provider (CP),” figs. 8, 22).
Claim 12. At least one computer-readable storage medium storing processor-executable instructions that, when executed by at least one hardware processor, cause the at least one hardware processor to perform a method (see at least Zheng c2:5-10 “The computer program product includes a computer readable medium having stored thereon instructions, which when executed, cause at least one processor to carry out the above method”) for generating, by a data product personalization service and for a service provider, a data product personalized to a user with whom the service provider is to interact, the method comprising: ● (A) receiving, from the service provider and via at least one communication network, a request for the data product personalized to the user (see at least Zheng fig. 18, c19:15-25 “personalization information to be provided to the CP … may be … information requested by the CP,” c27:1-25 “terminal device of the user A may send a personalization information acquisition request to the terminal device B of the target user B to obtain the target user B's personalization information,” c41:5-15 “a communication between the electronic device 2000 and another device, to implement data reception and transmission”), the request comprising: ● (i) information identifying or that can be used to identify at least one data source storing user data for the user that the user has authorized the data product personalization service to access (see at least Zheng figs. 2-3, 11, 13, c24:25-30 “terminal device may determine a content of interest to the user according to the stored user's personalization information,” c25:55-60 “terminal device may collect another user's personalization information according to the user's need, and may share suitable personalization information between users according to the user's request and authentication”); and ● (ii) information indicating how to transform at least some of the user data to generate the data product personalized to the user (see at least Zheng abstract “electronic device for providing more accurate contents to a user based on obtained personalization information. … analyze the collected information of the user for generating a personal knowledge graph of the user, provide the personalization information generated based on the personal knowledge graph to a content provider,” figs. 9-13, 18); ● (B) obtaining, using the information identifying or that can be used to identify the at least one data source, the at least some of the user data from, or previously retrieved from, the at least one data source (see at least Zheng abstract “obtained personalization information. … collect information of a user based on user activities on the electronic device, analyze the collected information of the user for generating a personal knowledge graph of the user,” figs. 1, 13, 19, c6:60-70 “obtaining the user's most accurate personalization information based on the user's personalization information provided by the terminal device”); ● (C) generating, using the information indicating how to transform the at least some of the user data and a data transformation service, the data product personalized to the user (see at least Zheng abstract “provide the personalization information generated based on the personal knowledge graph to a content provider (CP), receive at least one content which is provided from the CP, and provide recommendation content selected from among the a least one content based on the personalized information,” figs. 6, 12, c28:15-25 “determined content in the interest template may be directly used as a content of interest to the target user, or the post-processing may be performed based on the determined content in the interest template to obtain a content of interest to the target user”); and ● (D) providing, to the service provider and via the at least one communication network, the generated data product personalized to the user (see at least Zheng abstract “provide the personalization information generated based on the personal knowledge graph to a content provider (CP),” figs. 8, 22).Claim 23. A method for generating, by a data product personalization service and for a service provider, a data product personalized to a user with whom the service provider is to interact, the method comprising: using software of the data product personalization service executing on at least one computer hardware processor (see at least Zheng c2:5-10 “The computer program product includes a computer readable medium having stored thereon instructions, which when executed, cause at least one processor to carry out the above method”) to perform: ● (A) receiving, from the service provider and via at least one communication network, a request for the data product personalized to the user (see at least Zheng fig. 18, c19:15-25 “personalization information to be provided to the CP … may be … information requested by the CP,” c27:1-25 “terminal device of the user A may send a personalization information acquisition request to the terminal device B of the target user B to obtain the target user B's personalization information,” c41:5-15 “a communication between the electronic device 2000 and another device, to implement data reception and transmission”), the request comprising: ● (i) information identifying or that can be used to identify at least one data source storing user data for the user that the user has authorized the data product personalization service to access (see at least Zheng figs. 2-3, 11, 13, c24:25-30 “terminal device may determine a content of interest to the user according to the stored user's personalization information,” c25:55-60 “terminal device may collect another user's personalization information according to the user's need, and may share suitable personalization information between users according to the user's request and authentication”); and ● (ii) information indicating how to transform at least some of the user data to generate the data product personalized to the user (see at least Zheng abstract “electronic device for providing more accurate contents to a user based on obtained personalization information. … analyze the collected information of the user for generating a personal knowledge graph of the user, provide the personalization information generated based on the personal knowledge graph to a content provider,” figs. 9-13, 18); ● (B) obtaining, using the information identifying or that can be used to identify the at least one data source, the at least some of the user data from, or previously retrieved from, the at least one data source (see at least Zheng abstract “obtained personalization information. … collect information of a user based on user activities on the electronic device, analyze the collected information of the user for generating a personal knowledge graph of the user,” figs. 1, 13, 19, c6:60-70 “obtaining the user's most accurate personalization information based on the user's personalization information provided by the terminal device”); ● (C) generating, using the information indicating how to transform the at least some of the user data and a data transformation service, the data product personalized to the user (see at least Zheng abstract “provide the personalization information generated based on the personal knowledge graph to a content provider (CP), receive at least one content which is provided from the CP, and provide recommendation content selected from among the a least one content based on the personalized information,” figs. 6, 12, c28:15-25 “determined content in the interest template may be directly used as a content of interest to the target user, or the post-processing may be performed based on the determined content in the interest template to obtain a content of interest to the target user”); and ● (D) providing, to the service provider and via the at least one communication network, the generated data product personalized to the user (see at least Zheng abstract “provide the personalization information generated based on the personal knowledge graph to a content provider (CP),” figs. 8, 22).
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 2-6, 11, 13-14, 18-21, 24-27, and 29, are rejected under 35 U.S.C. 103 as being unpatentable over Zheng et al. (Patent No.: US 12,099,558 B2) in view of Gong et al. (Pub. No.: US 2025/0219968 A1).
Zheng teaches all of the above as noted. It teaches, a) a data product personalized to a user, b) information indicating how to transform user data to generate the personalized data product, c) generating the data product personalized to the user, and teaches d) the “personal knowledge graph of the user may be generated by a learning model” (c42:25-35), but does not explicitly disclose wherein the data transformation service comprises a generative machine learning (ML) model.
Gong also teaches a) a data product personalized to a user, b) information indicating how to transform user data to generate the personalized data product, c) generating the data product personalized to the user, and Gong further discloses wherein the data transformation service comprises a generative machine learning (ML) model (see at least abstract “generating personalized knowledge session content using a generative machine learning model”), wherein the method further comprises, pertaining to
Claim 2. The system of claim 1, wherein the data transformation service comprises a generative machine learning (ML) model, and wherein generating the data product personalized to the user comprises: ● causing the data transformation service to process the at least some of the user data with the generative ML model based on the information indicating how to transform the at least some of the user data (see at least Gong figs.2, 4, 6, ¶0026 “machine learning model may include a text-to-text transformer that personalizes messages,” ¶0038 “Personalized message generation model 204 may include one or more machine learning models, including generative models (e.g., large language models) and/or neural networks in order to generate messages that are personalized for users with regard to both the user profile data (which captures the users' interests and requirements), as well as the service description data,” ¶0048 “instructions 408 describing how to generate a personalized message”).Claim 3. The system of claim 1, wherein the data transformation service comprises a generative machine learning (ML) model, and wherein generating the data product personalized to the user comprises: ● generating ML input using the at least some of the user data and the information indicating how to transform the at least some of the user data (see at least Gong ¶0026 “generate a customized message using the user profile data of each group of users as additional input. Thus, the second machine learning model may include a text-to-text transformer that personalizes messages for each group of users so that each personalized message includes features that relate to the interests, requirements, and the like,” ¶0048 “instructions 408 describing how to generate a personalized message”); ● providing the ML input to the data transformation service for processing by the generative ML model to obtain a respective output (see at least Gong fig.5, ¶0026 “generate a customized message using the user profile data of each group of users as additional input. … each personalized message includes features that relate to the interests, requirements, and the like,” ¶0039 “The output of personalized message generation model 204 includes a plurality of personalized messages”); and ● receiving the respective output from the data transformation service (see at least Gong fig.5, ¶0033 “Client devices 120A-120N may each enable users to receive messages that are personalized in accordance with the embodiments presented herein”).Claim 4. The system of claim 3, wherein the generative machine learning model is a large language model (LLM) (see at least Gong ¶0025 “In some embodiments, message generation module 112 employs one or more large language models”).Claim 5. The system of claim 4, wherein generating the ML input comprises generating a prompt for the LLM using the at least some of the user data and the information indicating how to transform the at least some of the user data (see at least Gong ¶0026 “trained machine learning model is used to generate an initial message based on the service description data, and a second machine learning model uses the initial message as a prompt to generate a customized message using the user profile data”).Claim 6. The system of claim 3, wherein the processor-executable instructions further cause the at least one hardware processor to perform providing the respective output from the data transformation service to the service provider as the generated data product personalized to the user (see at least Gong fig.5, ¶¶0039-0040 “The output of personalized message generation model 204 includes a plurality of personalized messages 210A-210N, with one message corresponding to each user group 212A-212N. … [0040] Calibration model 216 may analyze the feedback in order to determine whether, and how, to modify any of the user profile data, the various machine learning models, and/or the service description data. In some embodiments, calibration model 216 may use a large language model to analyze the feedback in order to identify any capabilities or other aspects that a service lacks and that are desired by users”).
Zheng in view of Gong further discloses, pertaining to
Claim 11. The system of claim 1, wherein the processor-executable instructions further cause the at least one hardware processor to perform: ● receiving, from the service provider and via the at least one communication network, information identifying the user (see at least Gong abstract “User profile data of a plurality of users,” figs. 2, 4-6); ● generating authorization data to represent a data association of the information identifying the user (see at least Zheng figs.11, 13, c28:45-55 “authorization database shown in FIG. 13 corresponds to the target user's authentication system, and when the other terminal device provides the target user's personalization information to the current terminal devices, it may pass the authentication of authentication system of the target user,” in view of Gong ¶0021 “contracts and licenses used associated with a user may include any subscription details relating to services offered by any provider, details on the number of licenses used by users, active clients, and/or any restrictions or permissions associated with each license”); and ● providing the authorization data to the service provider for generation of the request for the data product personalized to the user (see at least Zheng figs.11, 13, c28:45-55 “authorization database shown in FIG. 13 corresponds to the target user's authentication system, and when the other terminal device provides the target user's personalization information to the current terminal devices, it may pass the authentication of authentication system of the target user,” in view of Gong ¶0021 “contracts and licenses used associated with a user may include any subscription details relating to services offered by any provider, details on the number of licenses used by users, active clients, and/or any restrictions or permissions associated with each license”).Claim 13. The at least one computer-readable storage medium of claim 12, wherein the data transformation service comprises a generative machine learning (ML) model, and wherein generating the data product personalized to the user comprises: ● causing the data transformation service to process the at least some of the user data with the generative ML model based on the information indicating how to transform the at least some of the user data (see at least Gong figs.2, 4, 6, ¶0026 “machine learning model may include a text-to-text transformer that personalizes messages,” ¶0038 “Personalized message generation model 204 may include one or more machine learning models, including generative models (e.g., large language models) and/or neural networks in order to generate messages that are personalized for users with regard to both the user profile data (which captures the users' interests and requirements), as well as the service description data”).Claim 14. The at least one computer-readable storage medium of claim 12, wherein the data transformation service comprises a generative machine learning (ML) model, and wherein generating the data product personalized to the user comprises: ● generating ML input using the at least some of the user data and the information indicating how to transform the at least some of the user data (see at least Gong ¶0026 “generate a customized message using the user profile data of each group of users as additional input. Thus, the second machine learning model may include a text-to-text transformer that personalizes messages for each group of users so that each personalized message includes features that relate to the interests, requirements, and the like”); ● providing the ML input to the data transformation service for processing by the generative ML model to obtain a respective output (see at least Gong fig.5, ¶0026 “generate a customized message using the user profile data of each group of users as additional input. … each personalized message includes features that relate to the interests, requirements, and the like,” ¶0039 “The output of personalized message generation model 204 includes a plurality of personalized messages”); and ● receiving the respective output from the data transformation service (see at least Gong fig.5, ¶0033 “Client devices 120A-120N may each enable users to receive messages that are personalized in accordance with the embodiments presented herein”).Claim 18. The at least one computer-readable storage medium of claim 12, wherein the processor-executable instructions further cause the at least one hardware processor to perform: ● receiving, from the user via at the at least one communication network, authorization for the data product personalization service to obtain the at least some of the user data from the at least one data source (see at least Zheng figs.11, 13, c28:45-55 “authorization database shown in FIG. 13 corresponds to the target user's authentication system, and when the other terminal device provides the target user's personalization information to the current terminal devices, it may pass the authentication of authentication system of the target user”); ● obtaining the at least some of the user data from the at least one data source via the at least one communication network (see at least Zheng abstract “obtained personalization information. … collect information of a user based on user activities on the electronic device, analyze the collected information of the user for generating a personal knowledge graph of the user,” figs. 1, 13, 19, c6:60-70 “obtaining the user's most accurate personalization information based on the user's personalization information provided by the terminal device”); and ● storing the at least some of the user data in at least one datastore (see at least Zheng figs. 2-3, 11, 13, c24:25-30 “terminal device may determine a content of interest to the user according to the stored user's personalization information,” c25:55-60 “terminal device may collect another user's personalization information according to the user's need, and may share suitable personalization information between users according to the user's request and authentication”).Claim 19. The at least one computer-readable storage medium of claim 18, wherein storing the at least some of the user data in the at least one datastore comprises storing the at least some of the user data in the at least one datastore such that the at least one datastore is only accessible by the data product personalization service (see at least Zheng figs. 2-3, 11, 13, c24:25-30 “terminal device may determine a content of interest to the user according to the stored user's personalization information,” c25:55-60 “terminal device may collect another user's personalization information according to the user's need, and may share suitable personalization information between users according to the user's request and authentication”).Claim 20. The at least one computer-readable storage medium of claim 18, wherein storing the at least some of the user data in the at least one datastore comprises storing the at least some of the user data in the at least one datastore in accordance with a database schema comprising data fields representing categories of information in the user data (see at least Zheng c5:10-20 “engine sorts the retrieved contents according to the user's interests, preferences, hobbies, etc.,” c8:1-5 “may store personalization information of different users in the personalization information database”).Claim 21. The at least one computer-readable storage medium of claim 18, wherein the at least one data source comprises one or more first data sources and one or more second data sources, and wherein receiving authorization for the data product personalization service comprises receiving authorization for the data product personalization service to obtain the at least some of the user data from the one or more first data sources (see at least Zheng figs.11, 13, c28:45-55 “authorization database shown in FIG. 13 corresponds to the target user's authentication system, and when the other terminal device provides the target user's personalization information to the current terminal devices, it may pass the authentication of authentication system of the target user”).
Claim 24. The method of claim 23, wherein the data transformation service comprises a generative machine learning (ML) model, and wherein generating the data product personalized to the user comprises: ● causing the data transformation service to process the at least some of the user data with the generative ML model based on the information indicating how to transform the at least some of the user data (see at least Gong figs.2, 4, 6, ¶0026 “machine learning model may include a text-to-text transformer that personalizes messages,” ¶0038 “Personalized message generation model 204 may include one or more machine learning models, including generative models (e.g., large language models) and/or neural networks in order to generate messages that are personalized for users with regard to both the user profile data (which captures the users' interests and requirements), as well as the service description data”).Claim 25. The method of claim 23, wherein the data transformation service comprises a generative machine learning (ML) model, and wherein generating the data product personalized to the user comprises: ● generating ML input using the at least some of the user data and the information indicating how to transform the at least some of the user data (see at least Gong ¶0026 “generate a customized message using the user profile data of each group of users as additional input. Thus, the second machine learning model may include a text-to-text transformer that personalizes messages for each group of users so that each personalized message includes features that relate to the interests, requirements, and the like”); ● providing the ML input to the data transformation service for processing by the generative ML model to obtain a respective output (see at least Gong fig.5, ¶0026 “generate a customized message using the user profile data of each group of users as additional input. … each personalized message includes features that relate to the interests, requirements, and the like,” ¶0039 “The output of personalized message generation model 204 includes a plurality of personalized messages”); and ● receiving the respective output from the data transformation service (see at least Gong fig.5, ¶0033 “Client devices 120A-120N may each enable users to receive messages that are personalized in accordance with the embodiments presented herein”).Claim 26. The method of claim 25, wherein the generative machine learning model is a large language model (LLM) (see at least Gong ¶0025 “In some embodiments, message generation module 112 employs one or more large language models”).Claim 27. The method of claim 26, wherein generating the ML input comprises generating a prompt for the LLM using the at least some of the user data and the information indicating how to transform the at least some of the user data (see at least Gong ¶0026 “trained machine learning model is used to generate an initial message based on the service description data, and a second machine learning model uses the initial message as a prompt to generate a customized message using the user profile data”).Claim 29. The method of claim 23, further comprising: ● receiving, from the user via at the at least one communication network, authorization for the data product personalization service to obtain the at least some of the user data from the at least one data source (see at least Zheng figs.11, 13, c28:45-55 “authorization database shown in FIG. 13 corresponds to the target user's authentication system, and when the other terminal device provides the target user's personalization information to the current terminal devices, it may pass the authentication of authentication system of the target user”); ● obtaining the at least some of the user data from the at least one data source via the at least one communication network (see at least Zheng abstract “obtained personalization information. … collect information of a user based on user activities on the electronic device, analyze the collected information of the user for generating a personal knowledge graph of the user,” figs. 1, 13, 19, c6:60-70 “obtaining the user's most accurate personalization information based on the user's personalization information provided by the terminal device”); and ● storing the at least some of the user data in at least one datastore (see at least Zheng figs. 2-3, 11, 13, c24:25-30 “terminal device may determine a content of interest to the user according to the stored user's personalization information,” c25:55-60 “terminal device may collect another user's personalization information according to the user's need, and may share suitable personalization information between users according to the user's request and authentication”).
Therefore it would have been obvious to one of ordinary skill in the art at the time of invention (for pre-AIA applications) or filing (for applications filed under the AIA ) to modify the method of Zheng to include wherein the data transformation service comprises a generative machine learning (ML) model, as taught by Gong since the claimed invention is merely a combination of old elements and in the combination each element merely would have performed the same function as it did separately. One of ordinary skill in the art would have recognized that the results of the combination were predictable and would result in an improvement. This is because the level of ordinary skill in the art demonstrated by the references applied shows the ability to incorporate such features even from a variety of technical fields into methods and systems implemented using similar technological structures (i.e., generic computer and/or network hardware such as processors, servers, etc.). In this case the areas of technical endeavor are nonetheless similar and overlapping.
Applicant has not disclosed that the added feature solves any stated problem or is for any particular purpose beyond the performance of the functions they performed separately and since each element and its function are shown in the prior art the difference between the claimed subject matter and the prior art rests not on any individual element or function but in the very combination itself. It would therefore have been an obvious matter of design choice to include the feature from Gong in the method of Zheng. Furthermore the combination solved no long felt need. Incorporating cumulative known features is additionally obvious to one of ordinary skill in the art because doing so increases commercial use of a method by attracting users that previously might have chosen between one of the previously known methods.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
● Fidaleo, Patent No.: US 12,333,257 B2: teaches creating a personalized dialogue template for responding to a user, while protecting user data.
● LI, Pub. No.: US 2021/0319073 A1: teaches generation of customized data product based on user location.
● McDevitt, Pub. No.: US 2024/0296482 A1: teaches everything in the independent claims plus ML. Prior art based on provisional filing date.
● Durvasula et al., Pub. No.: US 2025/0086870 A1: teaches creation of a virtual environment customized for the user with a virtual avatar representing the user and including user attributes.
● Durvasula et al., Pub. No.: US 2025/0086554 A1: teaches obtaining user data associated with the user from a set of data sources and creating an extended reality for the user with representations of personalized recommendations.
● Kim et al., Pub. No.: US 2024/0428273 A1: teaches everything in the independent claims plus ML.
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/ADAM L LEVINE/Primary Examiner, Art Unit 3689 January 6, 2026