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 .
The amendments were received on 4/22/2026. Claims 1-20 are pending where claims 1-20 were previously presented.
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 the 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, from a first calling entity, at a data science platform, workflow data for a workflow that comprises a plurality of operands and a plurality of operators (recites insignificant extrasolution activity of receiving information over a network, see MPEP 2106.05(g));
wherein the plurality of operators includes a data representation (DR) generator and a machine learned (ML) model that is downstream from the DR generator (recites field of use limitations describing the intended meaning of the workflow and what functions are intended to be performed, see MPEP 2106.05(h));
receiving a first DR generation request from the 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, the data science platform: 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));
based on the workflow data associated with the first calling entity, inputting the first set of one or more data representations from the DR generator into the ML model that produces output based on the first set of one or more data representations (recites insignificant extrasolution activity of sending information over a network as discussed in paragraph [0058] of applicant’s specification, see MPEP 2106.05(g); Examiner’s Note: the claim limitation requires the sending of information to a ML model ‘that’ produces output, i.e. describes the intended purpose of the ML model but does not actual require the model to produce output yet);
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, from a first calling entity, at a data science platform, workflow data for a workflow that comprises a plurality of operands and a plurality of operators (recites well-understood, routine, and conventional activity of receiving information over a network, see MPEP 2106.05(d));
wherein the plurality of operators includes a data representation (DR) generator and a machine learned (ML) model that is downstream from the DR generator (recites field of use limitations describing the intended meaning of the workflow and what functions are intended to be performed, see MPEP 2106.05(h));
receiving a first DR generation request from 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 first DR generation request, the data science platform: 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));
based on the workflow data associated with the first calling entity, inputting the first set of one or more data representations from the DR generator into the ML model that produces output based on the first set of one or more data representations (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); Examiner’s Note: the claim limitation requires the sending of information to a ML model ‘that’ produces output, i.e. describes the intended purpose of the ML model but does not actual require the model to produce output yet);
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));
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:
Step 2A, Prong One:
The claim recites the following limitations which are drawn towards an abstract idea:
A method comprising: … selecting a first DR generator from among the plurality of DR generators, each corresponding to a different input modality type, wherein the first DR generator corresponds to a first input modality type (recites to mental process/decision steps of determining the best tool to use);
generating, by the first DR generator, based on the first input data, a first 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);
selecting a second DR generator from among the plurality of DR generators, wherein the second DR generator corresponds to a second input modality type that is different than the first input modality type (recites to mental process/decision steps of determining the best tool to use);
generating, by the second 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, from a first calling entity, at a data science platform, workflow data for a workflow that comprises a plurality of operands and a plurality of operators (recites insignificant extrasolution activity of receiving information over a network, see MPEP 2106.05(g));
wherein the plurality of operators includes a plurality of data representation (DR) generators and a joint DR generator that is downstream from the plurality of DR generators (recites field of use limitations describing the intended meaning of the workflow and what functions are intended to be performed, see MPEP 2106.05(h));
receiving a first DR generation request from the 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, the data science platform: retrieving first input data and second input data based on the first DR generation request (recites insignificant extrasolution activity of receiving information over a network, see MPEP 2106.05(g));
based on the workflow data associated with the first calling entity, inputting the first set of one or more data representations from the first DR generator and the second set of one or more data representations from the second DR generator into the joint DR generator that produces output based on the first set and second set of one or more data representations (recites insignificant extrasolution activity of sending information over a network as discussed in paragraph [0058] of applicant’s specification, see MPEP 2106.05(g); Examiner’s Note: the claim limitation requires the sending of information to another component (joint DR generator) model ‘that’ produces output, i.e. describes the intended purpose of the joint DR generator but does not actual require the component to produce output yet);
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, from a first calling entity, at a data science platform, workflow data for a workflow that comprises a plurality of operands and a plurality of operators (recites well-understood, routine, and conventional activity of receiving information over a network, see MPEP 2106.05(d));
wherein the plurality of operators includes a plurality of data representation (DR) generators and a joint DR generator that is downstream from the plurality of DR generators (recites field of use limitations describing the intended meaning of the workflow and what functions are intended to be performed, see MPEP 2106.05(h));
receiving a first DR generation request from 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 first DR generation request, the data science platform: retrieving first input data and second 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));
based on the workflow data associated with the first calling entity, inputting the first set of one or more data representations from the first DR generator and the second set of one or more data representations from the second DR generator into the joint DR generator that produces output based on the first set and second set of one or more data representations (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); Examiner’s Note: the claim limitation requires the sending of information to another component (joint DR generator) model ‘that’ produces output, i.e. describes the intended purpose of the joint DR generator but does not actual require the component to produce output yet);
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 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 § 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 1-4, 7, and 12-16 are rejected under 35 U.S.C. 103 as being unpatentable over Li et al [US 2023/0325391 A1] in view of Geigel [US 2022/0180245 A1] (Geigel’245) and Banis et al [US 2020/0210867 A1].
With regard to claim 1, Li teaches a method comprising: receiving a first DR generation request from the 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, the data science platform: 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 the 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);
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).
Li does not appear to explicitly teach:
receiving, from a first calling entity, at a data science platform, workflow data for a workflow that comprises a plurality of operands and a plurality of operators;
wherein the plurality of operators includes a data representation (DR) generator and a machine learned (ML) model that is downstream from the DR generator;
based on the workflow data associated with the first calling entity, inputting the first set of one or more data representations from the DR generator into the ML model that produces output based on the first set of one or more data representations.
Geigel’245 teaches receiving, from a first calling entity, at a data science platform, workflow data for a workflow that comprises a plurality of operands and a plurality of operators (see paragraphs [0029]-[0031]; the user can provide information to the central processing system including workflow data).
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 representation engine of Li by being able to not only have multiple models but have workflows of how various functions in combination with models can be utilized as taught by Geigel’245 in order to allow for a modular approach to an overall machine learning task with multiple modules being utilized together instead of trying to always have a single large model to perform the task thus allowing for creation of changes to various workflows with small modular changes instead of having to scrap the entire model and start a new one.
Li in view of Geigel’245 teaches wherein the plurality of operators includes a data representation (DR) generator and a machine learned (ML) model that is downstream from the DR generator (see Li, paragraph [0037]; see Geigel’245, paragraphs [0032]-[0033] and Figure 7; the operators can include a machine learning model that is downstream from other operators with a first module/model relating to vector representations of content and other ML models downstream that performs other tasks on the representative data);
Li in view of Geigel’245 do not appear to explicitly teach based on the workflow data associated with the first calling entity, inputting the first set of one or more data representations from the DR generator into the ML model that produces output based on the first set of one or more data representations.
Banis teaches based on the workflow data associated with the first calling entity (see paragraph [0158]; the request from a user can include the identifier of the model or workflow that they want to use).
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 representation engine of Li in view of Geigel’245 by being able to have multiple models and workflows with means for users to specify their desired model/workflow as taught by Banis in order to allow users greater control on how their data is handled by being able to explicitly request the desired model/workflow to be used by the system instead of letting the system determine the model/workflow to use which may not be compatible with the desired output that the requesting user wants.
Li in view of Geigel’245 and Banis teach based on the workflow data associated with the first calling entity, inputting the first set of one or more data representations from the DR generator into the ML model that produces output based on the first set of one or more data representations (see Banis, paragraph [0158]; see Geigel’245, Figures 7 and 19 and paragraphs [0048], and [0032]-[0033]; the system allows a request to be associated with a calling entity by allowing that entity to specify the workflow to be used and then executing that workflow).
With regard to claim 2, Li in view of Geigel’245 and Banis teach 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 Li, 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 in view of Geigel’245 and Banis teach 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 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 4, Li in view of Geigel’245 and Banis teach 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 Li, 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 Li, paragraph [0040] and [0044]; the data can be of various types including image).
With regard to claim 7, Li in view of Geigel’245 and Banis teach 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, Li teaches a method comprising: receiving a first data representation (DR) generation request from the 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, the data science platform: retrieving first input data and second input data based on the first DR generation request (see paragraph [0040] and [0044] and paragraphs [0063] and [0066]-[0071]; the system can retrieve/receive the respective content/input data);
selecting a first DR generator from among the plurality of DR generators, each corresponding to a different input modality type, wherein the first DR generator corresponds to a first input modality type (see Li, paragraphs [0037] and [0047]; the system has multiple different generators that can be used for different types of content);
generating, by the first 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);
selecting a second DR generator from among the plurality of DR generators, wherein the second DR generator corresponds to a second input modality type that is different than the first input modality type (see Li, paragraphs [0037] and [0047]; the system has multiple different generators that can be used for different types of content);
generating, by the second DR generator, based on the second input data, a second set of one or more data representations (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).
Li does not appear to explicitly teach:
receiving, from a first calling entity, at a data science platform, workflow data for a workflow that comprises a plurality of operands and a plurality of operators;
wherein the plurality of operators includes a plurality of data representation (DR) generators and a joint DR generator that is downstream from the plurality of DR generators;
based on the workflow data associated with the first calling entity, inputting the first set of one or more data representations from the DR generator and the second set of one or more data representations from the second DR generator into the joint DR generator that produces output based on the first set and second set of one or more data representations.
Geigel’245 teaches receiving, from a first calling entity, at a data science platform, workflow data for a workflow that comprises a plurality of operands and a plurality of operators (see paragraphs [0029]-[0031]; the user can provide information to the central processing system including workflow data).
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 representation engine of Li by being able to not only have multiple models but have workflows of how various functions in combination with models can be utilized as taught by Geigel’245 in order to allow for a modular approach to an overall machine learning task with multiple modules being utilized together instead of trying to always have a single large model to perform the task thus allowing for creation of changes to various workflows with small modular changes instead of having to scrap the entire model and start a new one.
Li in view of Geigel’245 teaches wherein the plurality of operators includes a plurality of data representation (DR) generators and a joint DR generator that is downstream from the plurality of DR generators (see Li, paragraphs [0037], [0055], and [0056]; see Geigel’245, paragraphs [0032]-[0033] and Figure 7; the operators can include a joint module that takes the two DR generators output and combines them);
Li in view of Geigel’245 do not appear to explicitly teach based on the workflow data associated with the first calling entity, inputting the first set of one or more data representations from the DR generator and the second set of one or more data representations from the second DR generator into the joint DR generator that produces output based on the first set and second set of one or more data representations.
Banis teaches based on the workflow data associated with the first calling entity (see paragraph [0158]; the request from a user can include the identifier of the model or workflow that they want to use).
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 representation engine of Li in view of Geigel’245 by being able to have multiple models and workflows with means for users to specify their desired model/workflow as taught by Banis in order to allow users greater control on how their data is handled by being able to explicitly request the desired model/workflow to be used by the system instead of letting the system determine the model/workflow to use which may not be compatible with the desired output that the requesting user wants.
Li in view of Geigel’245 and Banis teach based on the workflow data associated with the first calling entity, inputting the first set of one or more data representations from the DR generator and the second set of one or more data representations from the second DR generator into the joint DR generator that produces output based on the first set and second set of one or more data representations (see Banis, paragraph [0158]; see Geigel’245, Figures 7 and 19 and paragraphs [0048], and [0032]-[0033]; see Li, paragraphs [0055]-[0056]; the system allows a request to be associated with a calling entity by allowing that entity to specify the workflow to be used and then executing that workflow).
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.
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 Geigel [US 2022/0180245 A1] (Geigel’245) and Banis et al [US 2020/0210867 A1] in further view of Chen [US 2013/0151638 A1].
With regard to claim 5, Li in view of Geigel’245 and Banis teach all the claim limitations of claim 1 as discussed above.
Li in view of Geigel’245 and Banis teach 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 in view of Geigel’245 and Banis 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 Geigel’245 and Banis in further 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 Geigel [US 2022/0180245 A1] (Geigel’245) and Banis et al [US 2020/0210867 A1] in further view of Karlekar et al [US 2021/0124765 A1].
With regard to claim 6, Li in view of Geigel’245 and Banis teach all the claim limitations of claim 1 as discussed above.
Li in view of Geigel’245 and Banis teach 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 in view of Geigel’245 and Banis 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 Geigel’245 and Banis in further 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 Geigel [US 2022/0180245 A1] (Geigel’245) and Banis et al [US 2020/0210867 A1] in further view of Hansen et al [US 10,091,290].
With regard to claim 8, Li in view of Geigel’245 and Banis teach all the claim limitations of claims 1 and 7 as discussed above.
Li in view of Geigel’245 and Banis do 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 in view of Geigel’245 and Banis 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 Geigel’245 and Banis in further 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 Geigel [US 2022/0180245 A1] (Geigel’245) and Banis et al [US 2020/0210867 A1] in further view of Bramble et al [US 2022/0207444 A1].
With regard to claim 9, Li in view of Geigel’245 and Banis teach all the claim limitations of claim 1 as discussed above.
Li in view of Geigel’245 and Banis do 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 in view of Geigel’245 and Banis 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 Geigel’245 and Banis in further 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 Geigel [US 2022/0180245 A1] (Geigel’245) and Banis et al [US 2020/0210867 A1] in further view of Bramble et al [US 2022/0207444 A1] and in further view of Park et al [US 2018/0189609 A1].
With regard to claim 10, Li in view of Geigel’245 and Banis in further view of Bramble teach all the claim limitations of claims 1 and 9 as discussed above.
Li in view of Geigel’245 and Banis in further 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 Geigel’245 and Banis in further 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 Geigel’245 and Banis in further view of Bramble and 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 Geigel [US 2022/0180245 A1] (Geigel’245) and Banis et al [US 2020/0210867 A1] in further view of Bramble et al [US 2022/0207444 A1] and in further view of Mehta et al [US 2022/0108035 A1].
With regard to claim 11, Li in view of Geigel’245 and Banis in further view of Bramble teach all the claim limitations of claims 1 and 9 as discussed above.
Li in view of Geigel’245 and Banis in further 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 Geigel’245 and Banis in further 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 Geigel’245 and Banis in further view of Bramble and 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).
Response to Arguments
Applicant's arguments (see the first paragraph on page 14 through the last paragraph on page 16) have been fully considered but they are not persuasive. The applicant argues (a) that the claims recite a DR generator (e.g. neural network) that is not something that can be done in the human mind (first paragraph on page 15); and (b) that the claims improve computer-related technology for data representation-as-a-service by providing a service that can leverage machine learning workflows of various configurations (see the last paragraph on page 15 through the last paragraph on page 16). The Examiner respectfully disagrees.
With regard to argument (a) about the claims not reciting a mental process step, the Examiner notes that although applicant indicates that the DR generator is a neural network, the particular architecture is not claimed and that a simple neural network such as a perceptron can be used which can be implemented or utilized in a manner by a human’s mind when evaluating an input and determining a coded output such as assigning numerical codes to words. Therefore, due to the high-level of generality of the claims, the respective arguments by the applicant are not persuasive.
With regard to argument (b), regarding applicant’s arguments about improvement to the functioning of a computer or to any other technology or technical field, the Examiner notes that, per MPEP 2106.05(a), that “[a]n important consideration in determining whether a claim improves technology is the extent to which the claim covers a particular solution to a problem or a particular way to achieve a desired outcome, as opposed to merely claiming the idea of a solution or outcome. McRO, 837 F.3d at 1314-15, 120 USPQ2d at 1102-03; DDR Holdings, 773 F.3d at 1259, 113 USPQ2d at 1107.” (emphasis added). Additionally, it is important to note that “the judicial exception alone cannot provide the improvement” and that “the claim reflects the asserted improvement”. As illustrated in the 35 USC 101 rejections above, the claims broadly indicates at a high-level of generality the data representation generator being used and its output being transmitted elsewhere. It appears from applicant’s arguments that the improvement relates to using the generator’s output and providing it elsewhere; however, the transmittal process is recited at a high-level of generality with no particular details on how or what the improvement is or implemented with the exception that other entities can use the DR generator output. As such, applicant’s arguments are not persuasive.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
Geigel [US 2022/0207287] (Geigel’287) teaches various flows that include a machine learning model and options to select one (flows can be clustered, Figure 16 and para 41).
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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.
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/MARC S SOMERS/Primary Examiner, Art Unit 2159 6/9/2026
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