DETAILED ACTION
1.
This is in reply to an application filed on 06/17/2024. Claims 1-20 are pending examination.
2.
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
3.
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 15-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
The claims recite a process of obtaining a set of user context corresponding to a set of users; generating a group context based on the set of user context; encoding the group context to assess the group attention; and identifying a collective interest of the group from the group attention.
The claimed process is similar to a method of mental processes, particularly concepts performed in the human minds (including an observation, evaluation, judgement, opinion), which is one of the groupings of abstract ideas according to Prong One in Step 2A of the 2019 Patent Subject Matter Eligibility Guidance since the steps of obtaining data; generating data; encoding data; and identifying data --are directed to a series of thought processes (i.e. mental processes).
Also this judicial exception is not integrated into a practical application because generating is merely indicating allowing processes (e.g. association process) to happen, which does not mean the process of predicting will actually occur and result in a practical application.
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. Additional elements (e.g. user context, group context, group attention and collective interest of the group) are directed to types of information being manipulated. The types of information being manipulated does not impose a meaningful limit on the judicial exception, such that the claims are more than a drafting effort design to monopolize exception, because the claimed steps could be performed in a same manner to achieve the same outcome with other types of information other than the ones being used in the claims.
The additional processes (e.g. obtaining a set of user context, generating a group context, encoding the group context and identifying a collective interest of the group) are merely directed to intended usages since the processes are not being performed nor integrated into a practical application.
Hence, the claims do not include additional elements or the combination of the elements are sufficient to amount to significantly more than the judicial exception and fail to integrate the judicial exception into practical application according to Prong Two in
The limitations of claim 15 recite “generating a group context based on the set of user context; encoding the group context to assess the group attention; and identifying a collective interest of the group from the group attention.” These limitations, as drafted, is a process that, under its broadest reasonable interpretation, covers performance of the limitations in the human mind with the aid of pen and paper. Nothing in the claim element precludes the steps from practically being performed in the human mind with the aid of pen and paper. For example, the steps of “generating … encoding … and identifying …” encompass a user identifying a collective interest of a group derived from a set of user context. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind, then it falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
This judicial exception is not integrated into a practical application. In particular, the claim recites the additional element “obtaining a set of user context corresponding to a set of users.” This limitation merely recites insignificant extra-solution activity (MPEP 2106.05(g), “data gathering”). The claim is directed to an abstract idea.
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional element of “obtaining a set of user context corresponding to a set of users” is mere insignificant extra solution activity. Moreover, the claim does not recite any means to obtain the user context. Hence, these additional elements do not amount to an inventive concept. The claim is not patent eligible.
Dependent claims 16, 17 and 20 recite additional attributes of the group context and the collective interests, which are limitations that only recite abstract ideas.
Therefore, the dependent claims 16, 17 and 20 are also rejected for the same rationales as the independent claims.
Dependent claims 18 and 19 recite using edge devices and user-specific database to capture and retrieve respectively the set of user context. However, the use of edge devices and database to collect and retrieve information is well-understood, routine and convention. See MPEP 2106.05(d).II. (i. Receiving or transmitting data over a network, e.g., using the Internet to gather data; iv. Storing and retrieving information in memory). Claims 18 and 19 are not eligible.
4.
Claim Rejections - 35 USC § 103
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 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.
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 1 is rejected under 35 U.S.C. 103 as unpatentable over Anand et al, US 2025/0088692 (hereinafter Anand), and further in view of Goldshtein et al, CN-120266123 (hereinafter Goldshtein).
Regarding claim 1 Anand teaches a method for synthesizing group attention, comprising: creating a group comprising a set of members; retrieving a set of member personas corresponding to the set of members; generating a group persona based on the set of member personas (Anand teaches content platforms can add a user to one or more user interest groups that indicate a topic of interest of the user or another characteristic of the user each time a request for content is sent to the content platform from the client device of the user [0012]);
obtaining a set of member context corresponding to the set of members;
generating a group context based on the set of member context (Anand teaches a machine learning models can be used to predict user interest groups that are related to topics that are likely to be of interest to a user based on contextual data included in a single request for content [0014], [0056-0057] and fig. 2, wherein the contextual data can include, for example, coarse location information indicating a general location of the client device that sent the digital component request, a resource (e.g., website or native application) with which the selected digital component will be presented, a spoken language setting of the application or client device , the number of digital component slots in which digital components will be presented with the resource, the types of digital component slots, text and/or images that are, or will be, displayed, and other appropriate contextual information [0034]);
generating a query based on the group context; and transmitting the query (Anand teaches a machine learning models can be used to predict user interest groups that are related to topics that are likely to be of interest to a user based on contextual data included in a single request for content [0014], and enabling the user to render content presented in a gaming application online [0026]). Anand does not teach opening a session state of a foundation model based on information. Goldshtein substantially teaches a processor may generate a fine-tuned LLM and use the fine-tuned LLM as a chat robot that participates in the corresponding session on behalf of the user (pg. 3).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention was made to modify Anand such that the invention further includes opening a session state of a foundation model based on information. One would have been motivated to do so because LLM enables stateful interactions and it handles data with an efficient manner.
5.
Claims 2-7 are rejected under 35 U.S.C. 103 as unpatentable over Anand and Goldshtein as mentioned above, and further in view of Klingen et al, US 2015/0170141 (hereinafter Klingen).
Regarding claim 2 Anand as modified teaches the method of claim 1. The combination of Anand and Goldshtein does not teach set of member personas are retrieved from a user-specific database. Klingen substantially teaches retrieving data from any number of disparate database systems for the purpose of constructing an accurate persona of a consumer or group of consumers [0031].
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention was made to modify Anand and Goldshtein such that the invention further includes set of member personas are retrieved from a user-specific database. One would have been motivated to do so to process the persona such as providing demographic information, so an offer or a promotion can be delivered to a relevant person [0004].
Regarding claim 3 Anand as modified teaches the method of claim 2, where generating the group persona comprises generating a set of tokens based on the set of member personas (Anand: [0038]).
Regarding claim 4 Anand as modified teaches the method of claim 2, where each member of the set of members individually authorizes the group to access persona information (Klingen teaches a consumer may be required to opt-in to allow his or her personal information to be shared [0012]).
Regarding claim 5 Anand as modified teaches the method of claim 1, where the set of member context are obtained from edge devices corresponding to the set of members (Klingen teaches retrieving data from any number of disparate database systems for the purpose of constructing an accurate persona of a consumer or group of consumers [0031].)
Regarding claim 6 Anand as modified teaches the method of claim 5, where generating the group context comprises generating a set of tokens based on the set of member context (Anand: [0038]).
Regarding claim 7 Anand as modified teaches the method of claim 5, where each member of the set of members individually authorizes the group to collect edge data (Klingen teaches a consumer may be required to opt-in to allow his or her personal information to be shared [0012]).
6.
Claims 8-11, 14-15 and 18-20 are rejected under 35 U.S.C. 103 as unpatentable over Anand as mentioned above, and further in view of Fletcher et al, US H001837 (hereinafter Fletcher).
Regarding claim 8 Anand teaches an apparatus, comprising:
a network interface (fig.1A):
a processor [0088]; and
a non-transitory computer-readable medium comprising instructions that, when executed by the processor, causes the processor to:
obtain a user request; obtain user context from multiple users based on the user request and assess group attention (Anand teaches a Machine learning models can be used to predict user interest groups that are related to topics that are likely to be of interest to a user based on contextual data included in a single request for content [0014]); access network resources based on the group attention (Anand teaches enabling the user to render content presented in a gaming application online [0026]). The combination of Anand and does not teach encoding a request and context associated with the request. Fletcher substantially teaches encoding and decoding messages and requests made by a client, including the parameters identified in a message or request (col. 11, lin.22-37).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention was made to modify Anand such that the invention further includes encoding a request and context associated with the request. One would have been motivated to do so to reduces transmission costs, compresses data size, allows for error detection, and ensures compatibility across different platforms.
In response to Claim 9: Rejected for the same reason as claim 1
Regarding claim 10 Anand as modified teaches the apparatus of claim 8, where the multiple users are members of an anonymized group (Anand teaches a user may engage in gaming applications, for example, in which the user has control over one or more characters, avatars, or other rendered content presented in the gaming application [0026]).
Regarding claim 11 Anand as modified teaches the apparatus of claim 8, where the user context comprises instantaneous user context collected by the multiple users (Anand teaches a machine learning models can be used to predict user interest groups that are related to topics that are likely to be of interest to a user based on contextual data included in a single request for content [0014], [0056-0057] and fig. 2.
Regarding claim 14 Anand as modified teaches the apparatus of claim 8, where the user context is based on multiple modalities of data [0014].
Regarding claim 15 Anand teaches a method for synthesizing a group from group attention, comprising: obtaining a set of user context corresponding to a set of users;
generating a group context based on the set of user context; assess the group attention; and identifying a collective interest of the group from the group attention (Anand teaches a Machine learning models can be used to predict user interest groups that are related to topics that are likely to be of interest to a user based on contextual data included in a single request for content [0014]). Anand does not teach encoding a group context. Fletcher substantially teaches encoding and decoding messages and requests made by a client, including the parameters identified in a message or request (col. 11, lin.22-37).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention was made to modify Anand such that the invention further includes encoding a group context. One would have been motivated to do so to reduces transmission costs, compresses data size, allows for error detection, and ensures compatibility across different platforms.
Regarding claim 18 Anand teaches the method of claim 15, where the set of user context is captured by a set of edge devices [0024].
In response to Claim 19: Rejected for the same reason as claim 12
Regarding claim 20 Anand teaches the method of claim 15, where the collective interest is anonymously representative of the set of users [0026].
7.
Claim 12 is rejected under 35 U.S.C. 103 as unpatentable over Anand, Fletcher and Klingen as mentioned above.
Regarding claim 12 Anand as modified teaches the apparatus of claim 8. The combination of Anand and Fletcher does not teach a user context comprises accumulated user context associated with the multiple users retrieved from a user-specific database. Klingen substantially teaches retrieving data from any number of disparate database systems for the purpose of constructing an accurate persona of a consumer or group of consumers [0031].
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention was made to modify Anand and Fletcher such that the invention further includes a user context comprises accumulated user context associated with the multiple users retrieved from a user-specific database. One would have been motivated to do so to process the persona such as providing demographic information, so an offer or a promotion can be delivered to a relevant person [0004].
8.
Claim 13 is rejected under 35 U.S.C. 103 as unpatentable over Anand, Fletcher and Goldshtein as mentioned above.
Regarding claim 13 Anand as modified teaches the apparatus of claim 8. The combination of Anand and Fletcher does not teach network resources comprise a large language model. Goldshtein substantially teaches a processor may generate a fine-tuned LLM and use the fine-tuned LLM as a chat robot that participates in the corresponding session on behalf of the user (pg. 3).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention was made to modify Anand and Fletcher such that the invention further network resources comprise a large language model. One would have been motivated to do so because LLM enables stateful interactions and it handles data with an efficient manner.
9.
Claims 16 and 17 are rejected under 35 U.S.C. 103 as unpatentable over Anand and Fletcher as mentioned above, and further in view of Gordon et al, US 8,659,318 (hereinafter Gordon).
Regarding claim 16 Anand as modified teaches the method of claim 15, where the group context is generated from the set of user context (Anand a machine learning models can be used to predict user interest groups that are related to topics that are likely to be of interest to a user based on contextual data included in a single request for content [0014], [0056-0057] and fig. 2). The combination of Anand and Fletcher does not teach a group data is generated in a unidirectionally format. Gordon substantially teaches identifying groups of unidirectional signals, such as a group of the input, output enable, and output signals and the unidirectional signals are encoded (col. 17. lin. 30-46).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention was made to modify Anand and Fletcher such that the invention further includes a group data is generated in a unidirectionally format. One would have been motivated to do so to prevent data conflicts, and allow for easier monitoring.
Regarding claim 17 Anand as modified teaches the method of claim 15, where the group attention is unidirectionally encoded from the group context (Gordon teaches identify groups of unidirectional signals, such as a group of the input, output enable, and output signals and the unidirectional signals are encoded (col. 17. lin. 30-46).
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to AYOUB ALATA whose telephone number is (313)446-6541. The examiner can normally be reached on Monday - Friday 7:30 - 5:00 Est.
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/AYOUB ALATA/Primary Examiner, Art Unit 2494