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 .
DETAILED ACTION
This responds to Applicant’s Arguments/Remarks filed 10/15/2025. Claims 1-20 are now pending in this Application.
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 rejected under 35 U.S.C. 101 because:
Claim 1, 14 nad 17 appears to be directed to an abstract idea without reciting additional limitations that tie it to a practical application or without reciting additional limitations that amount to significantly more than the abstract idea. One can mentally generate graph with nodes for spaces in a building as well as assets that are contained within those spaces. Then one can also mentally associate and classify senor readings and generate relationships between spaces, assets and sensors. The additional limitations are receiving data. These additional limitations are mere data gathering which are insignificant extra solution activities under step 2A prong II and well understood routine and conventional under step 2B (For Berkhiemer See MPEP 2106.05(d)(II) Versata.)
Step 2A, Prong One: Mathematical Concepts
Independent claims 1, 14, and 17 are directed to social platform specific content creation using machine learning:
receiving a description of an event and an indication of a social platform;
selecting a query template based in part on the indicated social platform;
generating, using the query template, a query for a machine learning (ML) model based on the description of the event; providing the generated query to the ML model and, responsive thereto, receiving social content for the event generated by the ML model for the indicated social platform; and providing, for display on the indicated social platform, the social content generated for the event for the indicated social platform.
These limitations describe collecting information, analyzing the information using rules or modes, generating content, and distributing the content which constitutes information processing and content creation for social media. This step can be performed mentally.
Step 2A Prong Two and Step 2B
Machine learning model used as a toll, no technical improvement to computer. It would constitute use of a generic computer used as tool to implement the abstract idea discussed above.
The step of receiving data associated with a building constitutes an insignificant extra-solution activity in the form of mere data gather, see MPEP 2106.05(g)
i. Performing clinical tests on individuals to obtain input for an equation, In re Grams, 888 F.2d 835, 839-40; 12 USPQ2d 1824, 1827-28 (Fed. Cir. 1989);
the claim does not include additional elements sufficient to transform the abstract idea into a patent-eligible application. The recited steps are performed using generic computer functionality, including receiving data, selecting templates, generating queries, invoking a machine learning model and displaying content. The clam does not recite any specific improvement to the operation of the machine leaning model, any specialized training technique by which computer functionality is improved. Instead, the machine learning model is used as a generic tool generate content based on provided inputs.
Accordingly, the claim merely implements the abstract idea using conventional computer components performing their ordinary functions.
Claims 2-13, 15-16, 18-20 depend from independent claims 1, 14 and 17 and therefore includes all of the limitations of claims 1, 14, and 17, which is directed to abstract idea as discussed above.
Claims 2-13, 15-16 and 18-20 do not add significantly more, as the machine learning model is recited at a high level and is used as a generic tool to perform the abstract idea. Accordingly, the additional limitation amounts to no more than applying the abstract idea using conventional computer technology.
Response to Arguments
Applicant’s arguments with respect to claim(s) 1-20 have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
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 (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.
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.
Claim(s) 1-2, 8-9, 14, 16-17 is/are rejected under 35 U.S.C. 103 as being unpatentable over Kuipers et al (U.S. Pub No. 2018/0101614 A1), and in view of Terrell et al (U.S. Pub No. 2024/0193661).
As per claim 1, Kuipers discloses a computer-implemented method comprising:
receiving a description of an event and an indication of a social platform (Par [0022]);
selecting a query template based in part on the indicated social platform (Par [0016, 0036]);
generating, using the query template, a query for a machine learning (ML) model based on the description of the event (Par [0038]);
providing the generate query, responsive thereto, receiving social content for the event for the indicated social platform (par [0002-0005]);
providing, for display on the indicated social platform, the social content generated for the event for the indicated social platform (Par [0042]).
Kuipers discloses receiving news content from to determine keywords/phrases related to topics of interest then creating a search query from the keyword/phrases and machine learning. Kuipers does not explicitly disclose providing the generated query to the ML model and, responsive thereto, receiving social content for the event generated by the ML model for the indicated social platform; and providing, for display on the indicated social platform, the social content generated for the event for the indicated social platform.
However, Terrell discloses providing the generated query to the ML model and, responsive thereto, receiving social content for the event generated by the ML model for the indicated social platform; and providing, for display on the indicated social platform, the social content generated for the event for the indicated social platform (Par [0089]).
It would have been obvious to one of ordinary skill in the art before the effective filling date of the claimed invention was made to incorporate the features as disclosed in Terrell into the teaching of Kuipers in order to provide user experience and interact platform that address the problem and issues (Par [0044]).
As per claim 2, Kuipers discloses the computer-implemented method of claim 1, further comprising:
transmitting the social content to a user device for displaying the social content on the user device;
receiving an indication from the user device for providing the social content for display on the indicated social platform (Par [0051, 0056]); and
updating a performance attribute associated to the query template based in part on a performance of the social content on the indicated social platform (Par [0025]).
As per claim 8, Kuipers discloses the computer-implemented method of claim 2, wherein updating the performance attribute of the query template comprise: determining that the indication of the social platform was received from the user device; determining that the indication of providing the social content for displaying on the indicated social platform was not received from the user device; and in response to determining that the indication of providing the social content to the indicated social platform was not received from the user device; determining that the social content was not used for the social content; and based on the determination (par [0017-0025]).
Kuipers does not explicitly disclose updating the performance attribute of the query template.
However, Terrell discloses updating the performance attribute of the query template (Par [0089]).
It would have been obvious to one of ordinary skill in the art before the effective filling date of the claimed invention was made to incorporate the features as disclosed in Terrell into the teaching of Kuipers in order to provide user experience and interact platform that address the problem and issues (Par [0044]).
As per claim 9, Kuipers discloses the computer-implemented method of claim 8, wherein in response to determining that the social content was not used for the social content, retraining the ML model to generate an alternative social content (par [0017-0025]).
As per claim 14, Kuipers discloses a system, comprising:
a processor; and a memory device containing instructions which, when executed by the processor, cause the processor to (Par [0072]):
receiving a description of an event and an indication of a social platform (par [0022]);
selecting a query template based in part on the indicated social platform (Par [0016, 0036]);
generating, using the query template, a query for a machine learning (ML) model based on the description of the event (par [0038]);
transmitting the social content to a user device for displaying the social content on the user device; receiving an indication that the social content was provided for display on the indicated social platform (Par [0051, 0056]); and
updating a performance attribute associated with the query template based in part on a performance of the social content on the indicated social platform (Par [0025]).
Kuiper discloses receiving news content from to determine keywords/phrases related to topics of interest then creating a search query from the keyword/phrases and machine learning. Kuipers does not explicitly disclose providing the generated query to the ML model and, responsive thereto, receiving social content for the event generated by the ML model for the indicated social platform.
However, Terrell discloses providing the generated query to the ML model and, responsive thereto, receiving social content for the event generated by the ML model for the indicated social platform (Par [0089]).
It would have been obvious to one of ordinary skill in the art before the effective filling date of the claimed invention was made to incorporate the features as disclosed in Terrell into the teaching of Kuipers in order to provide user experience and interact platform that address the problem and issues (Par [0044]).
As per claim 16, Kuipers discloses the system of claim 2, wherein updating the performance attribute of the query template comprise: determining that the indication of the social platform was received from the user device; determining that the indication of providing the social content for displaying on the indicated social platform was not received from the user device; and in response to determining that the indication of providing the social content to the indicated social platform was not received from the user device; determining that the social content was not used for the social content; and based on the determination (par [0017-0025]).
Kuipers does not explicitly disclose updating the performance attribute of the query template.
However, Terrell discloses updating the performance attribute of the query template (Par [0089]).
It would have been obvious to one of ordinary skill in the art before the effective filling date of the claimed invention was made to incorporate the features as disclosed in Terrell into the teaching of Kuipers in order to provide user experience and interact platform that address the problem and issues (Par [0044]).
As per claim 17, Kuipers discloses a computer program product comprising code stored in a tangible computer- readable storage medium, the code comprising:
code for receiving an indication of an event and an indication of a social platform (Par [0022]);
code for selecting a query template based in part on the indicated social platform and a performance metric associated with the query template, the performance metric indicating a historical performance of the query template with respect to the indicated social platform (Par [0016, 0036]);
code for generating, using the query template, a query for a machine learning (ML) model based on a description of the indicated event (Par [0038]);
Kuipers discloses receiving news content from to determine keywords/phrases related to topics of interest then creating a search query from the keyword/phrases and machine learning. Kuipers does not explicitly disclose code for providing the generated query to the ML model and, responsive thereto, receiving social content for the event generated by the ML model for the indicated social platform; and code for transmitting the social content to a user device for displaying the social content on the user device.
However, Terrell discloses code for providing the generated query to the ML model and, responsive thereto, receiving social content for the event generated by the ML model for the indicated social platform; and code for transmitting the social content to a user device for displaying the social content on the user device (Par [0089]).
It would have been obvious to one of ordinary skill in the art before the effective filling date of the claimed invention was made to incorporate the features as disclosed in Terrell into the teaching of Kuipers in order to provide user experience and interact platform that address the problem and issues (Par [0044]).
Claim(s) 3-7, 10, 15, 18-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Kuipers et al (U.S. Pub No. 2018/0101614 A1), and Terrell et al (U.S. Pub No. 2024/0193661), and further in view of Marey (U.S. Pub No. 2022/0012296 A1).
As per claim 3, Terrell discloses the computer-implemented method of claim 2, wherein updating the performance attribute associated to the query template comprises: determining a performance metric of the social content on the indicated social platform (Par [0051, 0056]) and social content is generated by the ML model using the query (Par [0038]).
Kuipers and Terrell do not explicitly disclose determining a similarity between the query and a static query, wherein the static query is a historical query selected for updating the performance attribute associated to the query template; a performance metric of a static social content on the indicated social platform, wherein the static social content is generated by the ML model using the static query;
determining whether the performance of the social content is more than the performance of the static social content; and in response to determining that the performance of the social content is more than the performance of the static social content: updating the performance attribute associated to the query template.
However, Marey discloses determining a similarity between the query and a static query, wherein the static query is a historical query selected for updating the performance attribute associated to the query template (Par [0025, 0031, 0034, 0039]);
a performance of a static social content on the indicated social platform, wherein the static social content is generated by the ML model using the static query; determining whether the performance of the social content is more than the performance of the static social content; and in response to determining that the performance of the social content is more than the performance of the static social content (par [0032, 0034-0035, 0042, 0045]):
updating the performance attribute associated to the query template (Par [0035]).
It would have been obvious to one of ordinary skill in the art before the effective filling date of the claimed invention was made to incorporate the features as disclosed in Smirnov into the teaching of Terrell as modified by Kuiper in order to improve the social media system.
As per claim 4, Marey discloses the computer-implemented method of claim 3, wherein determining the similarity between the query and the static query comprises:
processing the query and the social content to generate a first embedding; processing the static query and the static social content to generate a second embedding; and determining a similarity between the first embedding and the second embedding (Par [0032, 0034-0035, 0042, 0045]).
As per claim 5, Marey discloses the computer-implemented method of claim 3, wherein determining the performance of the social content and the performance of the static social content comprises querying the indicated social platform for the respective performances of the social content and the static social content (Par [0032, 0034-0035, 0042, 0045]).
As per claim 6, Marey discloses the computer-implemented method of claim 5, wherein the performance of the social content comprises a number of impressions of the social content on the indicated social platform, a number of user interactions with the social content on the indicated social platform, and a number of times the social content was reused after the user interactions (par [0030] and fig 2).
As per claim 7, Marey discloses the computer-implemented method of claim 3, wherein the performance attribute associated to the query template is a score based on the respective performances of the social content, wherein the score is generated using a scoring ML model (Par [0027, 0033, 0035]).
As per claim 10, Marey discloses the computer-implemented method of claim 1, wherein selecting the query template comprises: querying the indicated social platform for one or more attributes corresponding to the indicated social platform (par [0022]);
receiving, from the indicated social platform, the one or more attributes corresponding to the indicated social platform; and selecting the query template based in part on the one or more attributes of the indicated social platform (Par [0044, 0060-0061]).
As per claim 15, Terrell discloses the system of claim 14, wherein updating the performance attribute associated to the query template comprises: determining a performance metric of the social content on the indicated social platform and social content is generated by the ML model using the query (Par [0088]).
Kuipers and Terrell do not explicitly disclose determining a similarity between the query and a static query, wherein the static query is a historical query selected for updating the performance attribute associated to the query template; determining a performance metric of a static social content on the indicated social platform, wherein the static social content is generated by the ML model using the static query; and in response to determining that the performance of the social content is more than the performance of the static social content: updating the performance attribute associated to the query template.
However, Marey discloses determining a similarity between the query and a static query, wherein the static query is a historical query selected for updating the performance attribute associated to the query template (Par [0025, 0031, 0034, 0039]);
determining a performance metric of a static social content on the indicated social platform, wherein the static social content is generated by the ML model using the static query; and in response to determining that the performance of the social content is more than the performance of the static social content (par [0032, 0034-0035, 0042, 0045]):
updating the performance attribute associated to the query template (Par [0035]).
It would have been obvious to one of ordinary skill in the art before the effective filling date of the claimed invention was made to incorporate the features as disclosed in Smirnov into the teaching of Terrell as modified by Kuiper in order to improve the social media system.
As per claim 18, Terrel discloses the computer program product of claim 17, code for updating the performance attribute associated to the query template based in part on a performance of the social content on the indicated social platform, wherein updating the performance attribute associate to the query template comprises: code for determining a performance metric of the social content on the indicated social platform and social content is generated by the ML model using the query (Par [0088]).
Kuipers and Terrell do not explicitly disclose code for determining a similarity between the query and a static query, wherein the static query is a historical query selected for updating the performance attribute associated to the query template; code for determining a performance of the social content on the indicated social platform and a performance of a static social content on the indicated social platform, wherein the static social content is generated by the ML model using the static query; and in response to determining that the performance of the social content is more than the performance of the static social content: updating the performance attribute associated to the query template.
However, Marey discloses code for determining a similarity between the query and a static query, wherein the static query is a historical query selected for updating the performance attribute associated to the query template (Par [0025, 0031, 0034, 0039]);
code for determining a performance of the social content on the indicated social platform and a performance of a static social content on the indicated social platform, wherein the static social content is generated by the ML model using the static query; and in response to determining that the performance of the social content is more than the performance of the static social content (par [0032, 0034-0035, 0042, 0045]):
updating the performance attribute associated to the query template (Par [0035]).
It would have been obvious to one of ordinary skill in the art before the effective filling date of the claimed invention was made to incorporate the features as disclosed in Smirnov into the teaching of Terrell as modified by Kuiper in order to improve the social media system.
As per claim 19, Marey discloses the computer program product of claim 18, wherein determining the similarity between the query and the static query comprises:
Code for processing the query and the social content to generate a first embedding; code for processing the static query and the static social content to generate a second embedding; and code for determining a similarity between the first embedding and the second embedding (Par [0032, 0034-0035, 0042, 0045]).
As per claim 20, Kuipers discloses the computer program product of claim 18, wherein, updating the performance attribute of the query template comprises: code for determining that the indication of the social platform was received from the user device; code for determining that the indication of providing the social content for display on the indicated social platform was not received from the user device; and in response to determining that the indication of providing the social content to the indicated social platform was not received from the user device; code for determining that the social content was not used for the social content; and based on the determination (par [0017-0025]).
Kuipers does not explicitly disclose updating the performance attribute of the query template.
However, Terrell discloses updating the performance attribute of the query template (Par [0089]).
It would have been obvious to one of ordinary skill in the art before the effective filling date of the claimed invention was made to incorporate the features as disclosed in Terrell into the teaching of Kuipers in order to provide user experience and interact platform that address the problem and issues (Par [0044]).
Claim(s) 11 is/are rejected under 35 U.S.C. 103 as being unpatentable over Kuipers et al (U.S. Pub No. 2018/0101614 A1), and Terrell et al (U.S. Pub No. 2024/0193661), and further in view of Struttion et al (U.S. Pub No. 2016/0156373).
As per claim 11, Kuiper discloses the computer-implemented method of claim 10, wherein the one or more attributes of the social platform (par [0022]).
Kuiper and Terrel social platform comprises a character limit of the social content that is allowed to be displayed on the social platform.
However, Strutton discloses social platform comprises a character limit of the social content that is allowed to be displayed on the social platform (par [0044]).
It would have been obvious to one of ordinary skill in the art before the effective filling date of the claimed invention was made to incorporate the features as disclosed in Strutton into the teaching of Terrel as modified by Kuiper in order to track consumer behaviors and trends (Par [0010]).
Claim(s) 12-13 is/are rejected under 35 U.S.C. 103 as being unpatentable over Kuipers et al (U.S. Pub No. 2018/0101614 A1), and Terrell et al (U.S. Pub No. 2024/0193661), and further in view of Whalin et al (U.S. Pub No. 2025/0054045 A1).
As per claim 12, Smirnov and Terrell do not explicitly disclose the computer-implemented method of claim 1, wherein the ML model is a large language machine learning model (LLM), wherein the LLM is trained to process the query to generate the social content.
However, Whalin discloses wherein the ML model is a large language machine learning model (LLM), wherein the LLM is trained to process the query to generate the social content (Par [0050]).
It would have been obvious to one of ordinary skill in the art before the effective filling date of the claimed invention was made to incorporate the features as disclosed in Smirnov into the teaching of Terrell as modified by Kuiper in order to provide solution to technical support (Par [0002]).
As per claim 13, Whalin discloses the computer-implemented method of claim 12, wherein the LLM is trained on a training dataset, wherein the training dataset comprises a plurality of training samples, each training sample comprising the query, the social content, a label indicating the social platform, and one or more attributes of the social platform (Par [0050-0052]).
Conclusion
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January 24, 2026
/THU N NGUYEN/Examiner, Art Unit 2154