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 .
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
With regard to claim 1:
Step 2A, Prong One:
The claim recites the following limitations which are drawn towards an abstract idea:
A method comprising: generating, by a DR generator, based on the first input data, a first set of one or more data representations; generating, by the DR generator, based on the second input data, a second set of one or more data representations (recites mental process steps of evaluation converting data from one form to another form such as coding information, possibly with the use of an aid or mapping table, e.g. assigning numerical codes to words);
As seen from above, the identified limitations recite concepts associated with an abstract idea and thus the respective claim recites a judicial exception (see 2106.04(a)) and thus requires further analysis as discussed below.
Step 2A, Prong Two:
The following limitations have been identified as being additional elements as discussed below.
receiving a first data representation (DR) generation request from a first calling entity (recites insignificant extrasolution activity of receiving information over a network, see MPEP 2106.05(g));
in response to receiving the first DR generation request: retrieving first input data based on the first DR generation request (recites insignificant extrasolution activity of receiving information over a network, see MPEP 2106.05(g));
making the first set of one or more data representations available to the first calling entity (recites insignificant extrasolution activity of sending information over a network as discussed in paragraph [0058] of applicant’s specification, see MPEP 2106.05(g));
receiving a second data representation (DR) generation request from a second calling entity that is different than the first calling entity (recites insignificant extrasolution activity of receiving information over a network, see MPEP 2106.05(g));
in response to receiving the second DR generation request: retrieving second input data based on the second DR generation request (recites insignificant extrasolution activity of receiving information over a network, see MPEP 2106.05(g));
making the second set of one or more data representations available to the second calling entity (recites insignificant extrasolution activity of sending information over a network as discussed in paragraph [0058] of applicant’s specification, see MPEP 2106.05(g));
wherein the method is performed by one or more computing devices (recites apply it limitations of merely using generic computer hardware elements to perform generic computer functions to implement the abstract idea, see MPEP 2106.05(f)).
As seen from the above discussion, the identified limitations did not integrate the judicial exception into a practical application (see MPEP 2106.04(d)). This judicial exception is not integrated into a practical application because the claimed invention generically recites computer elements to implement the abstract idea as well as recite additional elements of merely receiving and sending/transmitting data.
Step 2B:
Below is the analysis of the claims:
receiving a first data representation (DR) generation request from a first calling entity (recites well-understood, routine, and conventional activity of receiving information over a network, see MPEP 2106.05(d));
in response to receiving the first DR generation request: retrieving first input data based on the first DR generation request (recites well-understood, routine, and conventional activity of receiving information over a network, see MPEP 2106.05(d));
making the first set of one or more data representations available to the first calling entity (recites well-understood, routine, and conventional activity of sending information over a network as discussed in paragraph [0058] of applicant’s specification, see MPEP 2106.05(d));
receiving a second data representation (DR) generation request from a second calling entity that is different than the first calling entity (recites well-understood, routine, and conventional activity of receiving information over a network, see MPEP 2106.05(d));
in response to receiving the second DR generation request: retrieving second input data based on the second DR generation request (recites well-understood, routine, and conventional activity of receiving information over a network, see MPEP 2106.05(d));
making the second set of one or more data representations available to the second calling entity (recites well-understood, routine, and conventional activity of sending information over a network as discussed in paragraph [0058] of applicant’s specification, see MPEP 2106.05(d));
wherein the method is performed by one or more computing devices (recites apply it limitations of merely using generic computer hardware elements to perform generic computer functions to implement the abstract idea, see MPEP 2106.05(f)).
As seen from above, the respective claim elements taken individually do not amount to significantly more than the judicial exception. When taken as a whole (in combination), the claim also does not amount to significantly more than the abstract idea because the additional elements generically recites computer elements to implement the abstract idea as well as recite additional elements of merely receiving and sending/transmitting data.
With regard to claim 2, this claim recites prior to generating the first set of one or more data representations, selecting the DR generator from among a plurality of DR generators, each corresponding to a different input modality type (recites to mental process/decision steps of determining the best tool to use).
With regard to claim 3, this claim recites wherein the DR generator is a first DR generator that corresponds to a first input modality type (recites field of use limitations indicating that a particular tool/program/service is for a particular data type, see MPEP 2106.05(h)), the method further comprising: receiving a third data representation (DR) generation request from a third calling entity; in response to receiving the third DR generation request: retrieving third input data based on the third DR generation request (recites insignificant extrasolution activity of receiving information over a network which amounts to well-understood, routine, and conventional activity of receiving information over a network, see MPEP 2106.05(d));
generating, by a second DR generator that is different than the first DR generator and that corresponds to a second input modality type that is different than the first input modality type, based on the third input data, a third set of one or more data representations (recites mental process steps of evaluation converting data from one form to another form such as coding information, possibly with the use of an aid or mapping table, e.g. assigning numerical codes to words);
making the third set of one or more data representations available to the third calling entity (recites insignificant extrasolution activity of transmitting information over a network which amounts to well-understood, routine, and conventional activity of sending information over a network as discussed in paragraph [0058] of applicant’s specification, see MPEP 2106.05(d)).
With regard to claim 4, this claim recites wherein: the plurality of DR generators correspond to a plurality of input modality types that include two or more modality types in a set consisting of text, document, image, video, audio, time series, and tabular; the first input data is a text string, a document, an image file, a video file, an audio file, times series data, or tabular data (recites field of use limitations indicating that a particular tool/program/service is for a particular data type, see MPEP 2106.05(h)).
With regard to claim 5, this claim recites wherein the first DR generation request includes an input modality type indicator that indicates a particular input modality type of the DR generator (recites field of use limitations describing the particular data type and expected meaning of the data value included in a communication request, see MPEP 2106.05(h)).
With respect to claim 6, this claim recites wherein: the first DR generation request includes storage location identification data that indicates where the first input data is stored (recites field of use limitations describing the particular data type and expected meaning of the data value included in a communication request, see MPEP 2106.05(h));
retrieving the first input data comprises using the storage location identification data to retrieve the first input data (recites insignificant extrasolution activity of receiving information over a network which amounts to well-understood, routine, and conventional activity of receiving information over a network, see MPEP 2106.05(d)).
With regard to claim 7, this claim recites wherein making the first set of one or more data representations available comprises storing the first set of one or more data representations at a storage location that is accessible to the first calling entity (recites insignificant extrasolution activity of storing data in memory which amounts to well-understood, routine, and conventional activity of storing data in memory, see MPEP 2106.05(d)).
With regard to claim 8, this claim recites wherein the first DR generation request includes storage location identification data that identifies the storage location (recites field of use limitations describing the particular data type and expected meaning of the data value included in a communication request, see MPEP 2106.05(h)).
With regard to claim 9, this claim recites receiving a customization request from a third calling entity (recites insignificant extrasolution activity of receiving information over a network which amounts to well-understood, routine, and conventional activity of receiving information over a network, see MPEP 2106.05(d));
in response to receiving the customization request, updating a model of the DR generator to generate a customized version of the model (recites updating/retraining a machine learning model at a high-level of generality which amounts to apply-it type limitations of using the computer as a tool to implement the abstract idea, see MPEP 2106.05(f));
in response to receiving a third DR generation request from the third calling entity: retrieving third input data based on the third DR generation request (recites insignificant extrasolution activity of receiving information over a network which amounts to well-understood, routine, and conventional activity of receiving information over a network, see MPEP 2106.05(d));
generating, using the customized version of the model of the DR generator, based on the third input data, a third set of one or more data representations (recites mental process steps of evaluation converting data from one form to another form such as coding information, possibly with the use of an aid or mapping table, e.g. assigning numerical codes to words);
making the third set of one or more data representations available to the third calling entity (recites insignificant extrasolution activity of transmitting information over a network which amounts to well-understood, routine, and conventional activity of sending information over a network as discussed in paragraph [0058] of applicant’s specification, see MPEP 2106.05(d)).
With regard to claim 10, this claim recites wherein: the customization request includes storage location identification data that identifies a storage location where training data is stored (recites field of use limitations describing the particular data type and expected meaning of the data value included in a communication request, see MPEP 2106.05(h));
the method further comprising retrieving the training data from the storage location (recites insignificant extrasolution activity of receiving information over a network which amounts to well-understood, routine, and conventional activity of receiving information over a network, see MPEP 2106.05(d));
wherein updating the model comprises re-training the model based on the training data (recites updating/retraining a machine learning model at a high-level of generality which amounts to apply-it type limitations of using the computer as a tool to implement the abstract idea, see MPEP 2106.05(f)).
With regard to claim 11, this claim recites in response to receiving the customization request, storing entity identification data that associates the customized version of the model with the third calling entity (recites mental process steps of associating entity/user with their tool; similar to how someone can recognize a person based on their customized avatar or other customized belongings);
in response to receiving the third DR generation request, determining an identity of the third calling entity (recites mental process steps of evaluating information to form a judgement with respect to identifying someone);
prior to generating the third set of one or more data representations, selecting the customized version based on the identity of the third calling entity (recites mental process steps of being able to evaluate and form a judgement/selection as to what tool relates to the requesting user, similar to how someone can remember a customer and what they ordered and be able to select the appropriate product based on identification/recollection of the entity/customer).
With regard to claim 12, this claim is substantially similar to claims 1 and 2 and is rejected for similar reasons as discussed above.
With regard to claim 13, this claim is substantially similar to claim 1 and is rejected for similar reasons as discussed above. The main difference between claims 1 and 13 is that claim 13 recites “one or more non-transitory storage media storing instructions” which amounts to generic computer hardware being recited at a high-level of generality for generic computer functions such as storing data and amounts to merely applying the abstract idea on a computer (See MPEP 2106.05(f)).
With regard to claims 14-20, these claim are substantially similar to claims 2-6, 9, and 10 respectively and are rejected for similar reasons as discussed above.
Claim Rejections - 35 USC § 102
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 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)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claims 1-4, 7, and 12-16 are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Li et al [US 2023/0325391 A1].
With regard to claim 1, Li teaches a method comprising: receiving a first data representation (DR) generation request from a first calling entity (see paragraphs [0038] and [0040]; the system can allow a user device or first calling entity to send requests to add data/content to the system);
in response to receiving the first DR generation request: retrieving first input data based on the first DR generation request (see paragraph [0040] and [0044]; the system can retrieve/receive the respective content/input data);
generating, by a DR generator, based on the first input data, a first set of one or more data representations (see paragraph [0048] and [0050]; the system can form vector embeddings of the content);
making the first set of one or more data representations available to the first calling entity (see paragraphs [0051] and [0052] and [0054]; the system allows users to have access to the embeddings as part of their interaction with the system);
receiving a second data representation (DR) generation request from a second calling entity that is different than the first calling entity; in response to receiving the second DR generation request: retrieving second input data based on the second DR generation request; generating, by the DR generator, based on the second input data, a second set of one or more data representations; making the second set of one or more data representations available to the second calling entity (see paragraphs [0063] and [0066]-[0071]; multiple users may utilize the system with different users providing different content);
wherein the method is performed by one or more computing devices (see paragraph [0093] and [0029]; computing devices are utilized to perform the method).
With regard to claim 2, Li teaches prior to generating the first set of one or more data representations, selecting the DR generator from among a plurality of DR generators, each corresponding to a different input modality type (see paragraphs [0037] and [0047]; the system has multiple different generators that can be used for different types of content).
With regard to claim 3, Li teaches wherein the DR generator is a first DR generator that corresponds to a first input modality type, the method further comprising: receiving a third data representation (DR) generation request from a third calling entity; in response to receiving the third DR generation request: retrieving third input data based on the third DR generation request; generating, by a second DR generator that is different than the first DR generator and that corresponds to a second input modality type that is different than the first input modality type, based on the third input data, a third set of one or more data representations (see paragraphs [0047], [0063], and [0066]-[0071]; multiple users may utilize the system with different users providing different content);
making the third set of one or more data representations available to the third calling entity (see paragraph [0093] and [0029]; computing devices are utilized to perform the method).
With regard to claim 4, Li teaches wherein: the plurality of DR generators correspond to a plurality of input modality types that include two or more modality types in a set consisting of text, document, image, video, audio, time series, and tabular (see paragraph [0047]; the generators/representation models relate to different modality types);
the first input data is a text string, a document, an image file, a video file, an audio file, times series data, or tabular data (see paragraph [0040] and [0044]; the data can be of various types including image).
With regard to claim 7, Li teaches wherein making the first set of one or more data representations available comprises storing the first set of one or more data representations at a storage location that is accessible to the first calling entity (see paragraphs [0042] and [0064]; the system stores the respective content at a storage location that the user/entity is able to access).
With regard to claim 12, this claim is substantially similar to claims 1 and 2 and is rejected for similar reasons as discussed above.
With regard to claim 13, this claim is substantially similar to claim 1 and is rejected for similar reasons as discussed above. The main difference between claims 1 and 13 is that claim 13 recites “one or more non-transitory storage media storing instructions” (see Li, paragraph [0096]).
With regard to claims 14-16, these claim are substantially similar to claims 2-4 respectively and are rejected for similar reasons as discussed above.
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.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 5 and 17 are rejected under 35 U.S.C. 103 as being unpatentable over Li et al [US 2023/0325391 A1] in view of Chen [US 2013/0151638 A1].
With regard to claim 5, Li teaches all the claim limitations of claim 1 as discussed above.
Li teaches various DR generators for different modalities but does not appear to explicitly teach: wherein the first DR generation request includes an input modality type indicator that indicates a particular input modality type of the DR generator.
Chen teaches request includes an input modality type indicator (see paragraphs [0045] and [0050]; messages can include metadata including type of file).
It would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to modify the content uploading/transfer request of Li by including metadata such as the file type or modality indicator as taught by Chen in order to allow the receiving system to be able to receive and know information about the file without having to do any additional processing/classification of the file contents to know what type of file the file is.
Li in view of Chen teach wherein the first DR generation request includes an input modality type indicator that indicates a particular input modality type of the DR generator (see Chen, paragraphs [0045] and [0050]; see Li, see paragraphs [0038] and [0040]; the system can allow a user device or first calling entity to send requests to add data/content to the system where the request can include metadata such as file type).
With regard to claim 17, this claim is substantially similar to claim 5 and is rejected for similar reasons as discussed above.
Claims 6 and 18 are rejected under 35 U.S.C. 103 as being unpatentable over Li et al [US 2023/0325391 A1] in view of Karlekar et al [US 2021/0124765 A1].
With regard to claim 6, Li teaches all the claim limitations of claim 1 as discussed above.
Li teaches requesting the system to be able to add content items does not appear to explicitly teach: wherein: the first DR generation request includes storage location identification data that indicates where the first input data is stored; retrieving the first input data comprises using the storage location identification data to retrieve the first input data.
Karlekar teaches request includes storage location identification data that indicates where the first input data is stored; retrieving the first input data comprises using the storage location identification data to retrieve the first input data (see paragraph [0051]; the system can allow the request to specify the source location of the data and can retrieve the respective file from that location).
It would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to modify the content uploading/transfer request of Li by including a location identifier as part of the request as taught by Karlekar in order to allow provide greater flexibility of the system by not having to utilize multiple rounds of communications to request content to be added; wait for confirmation and have the user then proceed to manually select the file to be uploaded when the request can include the location path for the file so that the respective storage server, when the request is granted, can automatically proceed with acquiring the file without bothering the client user with additional tasks.
Li in view of Karlekar teach wherein: the first DR generation request includes storage location identification data that indicates where the first input data is stored (see Karlekar, paragraph [0051]; see Li, paragraphs [0038] and [0040]; the user’s request can include means to identify the location of the content to be added to the remote system).
With regard to claim 18, this claim is substantially similar to claim 6 and is rejected for similar reasons as discussed above.
Claim 8 is rejected under 35 U.S.C. 103 as being unpatentable over Li et al [US 2023/0325391 A1] in view of Hansen et al [US 10,091,290].
With regard to claim 8, Li teaches all the claim limitations of claims 1 and 7 as discussed above.
Li does not appear to explicitly teach wherein the first DR generation request includes storage location identification data that identifies the storage location.
Hansen teaches request includes storage location identification data that identifies the storage location (see Figure 5 and col 6, lines 29-47; col 5, lines 35-54; the user is able to form a request to indicate the location where the user wants particular data to be stored).
It would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to modify the content uploading/transfer request of Li by including means for the user to specify or indicate their desired destination location as taught by Hansen in order to the user the ability to be able to organize their respective remote content in their desired or preferred manner instead of a one folder-fits-all approach, thereby helping users of the system to be able to organize their remotely stored files that allows users to quickly identify particular content that they want through either search query or browsing.
Li in view of Hansen teach wherein the first DR generation request includes storage location identification data that identifies the storage location (see Li, paragraphs [0038] and [0040]; see paragraphs [0042] and [0064]; see Hansen, ; the user’s request can include means to identify the location of the content to for where the content is to be stored in the remote system).
Claims 9 and 19 are rejected under 35 U.S.C. 103 as being unpatentable over Li et al [US 2023/0325391 A1] in view of Bramble et al [US 2022/0207444 A1].
With regard to claim 9, Li teaches all the claim limitations of claim 1 as discussed above.
Li does not appear to explicitly teach:
receiving a customization request from a third calling entity;
in response to receiving the customization request, updating a model of the DR generator to generate a customized version of the model;
in response to receiving a third DR generation request from the third calling entity:
retrieving third input data based on the third DR generation request;
generating, using the customized version of the model of the DR generator, based on the third input data, a third set of one or more data representations;
making the third set of one or more data representations available to the third calling entity.
Bramble teaches receiving a customization request from a third calling entity (see paragraph [0029]; the system allows users to be able to customize machine learning models).
It would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to modify the machine learning system of Li by providing means to allow users to request/customize new models as taught by Bramble in order to allow the cloud-based system to provide additional services to the user including monetization to earn money for the cloud provider while expanding the functionality to allow users to be able to generate customized models tailored towards their preferred training data.
Li in view of Bramble teach in response to receiving the customization request, updating a model of the DR generator to generate a customized version of the model (see Bramble, paragraphs [0029]-[0031]; see Li, paragraph [0032]; the system can provide ongoing training for various models including being able to create a new model from an existing (baseline) model);
in response to receiving a third DR generation request from the third calling entity: retrieving third input data based on the third DR generation request; generating, using the customized version of the model of the DR generator, based on the third input data, a third set of one or more data representations (see Li, paragraphs [0047], [0063], and [0066]-[0071]; multiple users may utilize the system with different users providing different content);
making the third set of one or more data representations available to the third calling entity (see Li, paragraph [0093] and [0029]; computing devices are utilized to perform the method).
With regard to claim 19, this claim is substantially similar to claim 9 and is rejected for similar reasons as discussed above.
Claims 10 and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Li et al [US 2023/0325391 A1] in view of Bramble et al [US 2022/0207444 A1] in further view of Park et al [US 2018/0189609 A1].
With regard to claim 10, Li in view of Bramble teach all the claim limitations of claims 1 and 9 as discussed above.
Li in view of Bramble do not appear to explicitly teach:
wherein: the customization request includes storage location identification data that identifies a storage location where training data is stored;
the method further comprising retrieving the training data from the storage location;
wherein updating the model comprises re-training the model based on the training data.
Park teaches wherein: the customization request includes storage location identification data that identifies a storage location where training data is stored; the method further comprising retrieving the training data from the storage location; (see paragraph [0028]; the user can specify a location to the server for where the training data is stored and the server is able to retrieve the data from that location).
It would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to modify the machine learning system of Li in view of Bramble by allowing the for the identification of the location where training data is stored as taught by Park in order to allow users to be able to send requests and indicate particular training data without forcing the user’s active device to have the training data that needs to be uploaded thus allowing user devices to save storage space by being able to indicate locations where training data is stored so that the servers can retrieve the data without overburdening the storage space on the user’s device with having to store all the training data as well as not congesting the bandwidth of the user’s device from having to transmit/send all the training data to the server.
Li in view of Bramble in further view of Park wherein updating the model comprises re-training the model based on the training data (see Park, paragraph [0028]; Bramble, paragraphs [0029]-[0031]; see Li, paragraph [0032]; the system can receive a storage location of the training data and be able to retrieve the training data from that location and utilize it in updating/customizing/re-training the model).
With regard to claim 20, this claim is substantially similar to claim 10 and is rejected for similar reasons as discussed above.
Claim 11 is rejected under 35 U.S.C. 103 as being unpatentable over Li et al [US 2023/0325391 A1] in view of Bramble et al [US 2022/0207444 A1] in further view of Mehta et al [US 2022/0108035 A1].
With regard to claim 11, Li in view of Bramble teach all the claim limitations of claims 1 and 9 as discussed above.
Li in view of Bramble do not appear to explicitly teach:
in response to receiving the customization request, storing entity identification data that associates the customized version of the model with the third calling entity;
in response to receiving the third DR generation request, determining an identity of the third calling entity;
prior to generating the third set of one or more data representations, selecting the customized version based on the identity of the third calling entity.
Mehta teaches in response to receiving the customization request, storing entity identification data that associates the customized version of the model with the third calling entity (see paragraphs [0028]-[0029]; the system can allow multiple users to utilize their services including customization of models to form new versions of the model while ensuring that particular models are only associated with their particular tenant/customer of the system).
It would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to modify the machine learning system of Li in view of Bramble by providing compartmentalized storage means similar to a multi-tenant system as taught by Mehta in order to allow a plurality of tenants and users of the system to be able to create and generate models while providing security to their models so that the particular customer data (including sensitive data) is appropriately siloed (protected).
Li in view of Bramble in further view of Mehta teach in response to receiving the third DR generation request, determining an identity of the third calling entity (see Mehta, paragraph [0056]-[0057]; the user provides means to allow the system to determine the identity of the user to determine if their request for the model is appropriate and should be granted);
prior to generating the third set of one or more data representations, selecting the customized version based on the identity of the third calling entity (see Mehta, paragraph [0056]; the respective model associated with the user can be determined/selected in order to process the user’s request).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
Bergonzo et al [US 2024/0303516 A1] teaches at paragraph [0021] a server that uses embedding models to create vectors on behalf of downstream services.
Likhomanov et al [US 2024/0005660 A1] teaches at Figure 1 and paragraph [0104] that input is provided to an encoder model and embeddings are provided to customized classifier model where some models can be local to a particular device while other models, such as encoder model, is remote to that device.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to MARC S SOMERS whose telephone number is (571)270-3567. The examiner can normally be reached M-F 11-8 EST.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Ann Lo can be reached at 5712729767. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/MARC S SOMERS/Primary Examiner, Art Unit 2159 1/16/2026