Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
DETAILED ACTION
This Office Action corresponds to application 17/958,189 which was filed on 9/30/2022. Claims 1-20 are currently pending.
Specification
The title of the invention is not descriptive. A new title is required that is clearly indicative of the invention to which the claims are directed.
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.
Step 1
The claims recite a computing device (claim 1), a method (claim 9), and a non-transitory computer-readable storage medium (claim 16). These claims fall within at least one of the four categories of patentable subject matter.
Step 2A, Prong One
Claim 1 recites receiving a request for a data object, identifying a semantic structure for the data object, identifying one or more relationships associated with the semantic structure of the data object, determining a view of the data object based on the relationships, and provided the view to the user.
The recited steps request information, identify the type of information, identifying relationships associated with the data type for the information, determine a view for the information and provide the view, which are acts of information evaluation and retrieval that can be practically performed in the human mind. For example, a person asking someone for information on a document and that person identifying the document is an image and they should handle the document like an image. Thus, these steps are an abstract idea in the “mental processes” grouping.
Dependent claims 2-8 recite additional elements of predicting types with machine learning that is trained with workload traces, determining the view by updating metadata, prefetching by filtering, training the machine learning model with the data object, and that the relationships comprise data types. These are all further extensions of the abstract idea or mere extra-solution activity. For example, with claim 2, a person can predict an item type; and claim 5, a person determine a view of the object by updating the contextual information for the object.
Claim 9 recites receiving a request for a data object, identifying a semantic structure for the data object, identifying one or more relationships associated with the semantic structure of the data object, determining a view of the data object based on the relationships, provided the view to the user, and sending the data object to train a machine learning model.
The recited steps request information, identify the type of information, identifying relationships associated with the data type for the information, determine a view for the information, provide the view, and train a machine learning model, which are acts of information evaluation and retrieval that can be practically performed in the human mind. For example, a person asking someone for information on a document and that person identifying the document is an image, they should handle the document like an image, and can tell other people to identify this object as an image. Thus, these steps are an abstract idea in the “mental processes” grouping.
Dependent claims 10-15 recite additional elements of predicting types with machine learning that is trained with workload traces, determining the view by updating metadata, prefetching by filtering, and that the relationships comprise data types. These are all further extensions of the abstract idea or mere extra-solution activity. For example, with claim 11, a person can predict an item type; and claim 13, a person determine a view of the object by updating the contextual information for the object.
Claim 16 recites receiving a request for a data object, identifying a semantic structure for the data object, identifying one or more relationships associated with the semantic structure of the data object, determining a view of the data object based on the relationships, and provided the view to the user.
The recited steps request information, identify the type of information, identifying relationships associated with the data type for the information, determine a view for the information and provide the view, which are acts of information evaluation and retrieval that can be practically performed in the human mind. For example, a person asking someone for information on a document and that person identifying the document is an image and they should handle the document like an image. Thus, these steps are an abstract idea in the “mental processes” grouping.
Dependent claims 17-20 recite additional elements of predicting types with machine learning that is trained with workload traces, determining the view by updating metadata, and training the machine learning model with the data object. These are all further extensions of the abstract idea or mere extra-solution activity. For example, with claim 17, a person can predict an item type; and claim 19, a person determine a view of the object by updating the contextual information for the object.
Step 2A, Prong Two
This judicial exception is not integrated into a practical application because the combination of additional elements includes only generic computer elements which do not add a meaningful limitation to the abstract idea because they amount to simply implementing the abstract idea on a computer.
For claims 1-8, the additional elements include the memory, one or more processors, the data object, training server, and machine learning models.
For claims 9-15, the additional elements include the data object, training server, machine learning models.
For claims 16-20, the additional elements include a non-transitory computer-readable storage medium, processors, the data object, training server, and machine learning models.
The data object, machine learning models, training server, non-transitory computer-readable storage medium, processor, and memory are recited at a high-level of generality (i.e., as a generic computer functions) such that it amounts no more than mere instructions to apply the exception using a generic computer component. Accordingly, these additional elements do not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. The claims are directed to an abstract idea.
Step 2B
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements of using a data object, machine learning models, training server, non-transitory computer-readable storage medium, processor, and memory to perform the steps amounts to no more than part of the abstract idea, mere extra-solution activity, and mere instructions to apply the exception using a generic computer component. The claims are not patent eligible.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claim(s) 1-5, 7-13, and 15-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Kurian et al. (US2023/0289325), hereinafter Kurian, in view of Wilczynski et al. (US9760606), hereinafter Wilczynski.
Regarding Claim 1:
Kurian teaches:
A computing device comprising: a memory; and one or more processors that are configured to execute machine readable instructions stored in the memory for performing a method (Kurian, figure 4, note memory and processor) comprising:
receiving a query from a client device to access a data object (Kurian, figures 2A-2E and 3, [0039, 0041, 0046, 0050], note requesting and receiving data);
identifying a semantic structure associated with the data object (Kurian, abstract, figures 2A-2E and 3, [0024, 0039, 0052], note analyzing the received data to identify a type of data, e.g., semantic structure);
identifying one or more relationships associated with the semantic structure of the data object (Kurian, abstract, figures 2A-2E and 3, [0024, 0039, 0052, 0054], note identifying relationships based on data type);
determining a view of the data object based on the one or more relationships (Kurian, abstract, figures 2A-2E and 3, [0024, 0039, 0054-0055, 0071], note storing the data element and connections/relationships; note a user may receive the stored data and understand connections to the other data based on the stored connections, which is interpreted to mean the stored data element and connections is a view of the data object); and
providing the view of the data object to a user interface to consume the data (Kurian, abstract, figures 2A-2E and 3, [0024, 0039, 0054-0055, 0071], note storing the data element and connections/relationships; note a user may receive the stored data and understand connections to the other data based on the stored connections, which is interpreted as providing the view of the data object to the user).
While Kurian as modified teaches data object views, Kurian as modified doesn’t specifically state the data request is a user query. However, Wilczynski is in the same field of endeavor, data management, and Wilczynski teaches:
receiving a user query from a client device to access a data object (Wilczynski, column 2 lines 36-56, column 3 lines 8-61, note the user is making the request for the data object);
determining a view of the data object based on the one or more relationships (Wilczynski, figure 5, column 2 lines 36-56, column 3 lines 8-61, column 10 lines 56-67, note determining a data object view for the request); and
providing the view of the data object to a user interface to consume the data (Wilczynski, figure 5, column 2 lines 36-56, column 3 lines 8-61, column 10 lines 56-67, note determining a data object view for the request and providing it to the user).
It would have been obvious to one of ordinary skill in the art before the effective date of filing to modify the cited references to incorporate the teachings of Wilczynski because all references are directed towards data management and because Wilczynski would expand upon the teachings of the previously cited references in data object management which would improve the performance and flexibility of the system by determining and providing data object views (Wilczynski, column 1 lines 12-58, column 2 lines 36-56).
Regarding Claim 2:
Kurian as modified shows the computing device as disclosed above;
Kurian as modified further teaches:
wherein identifying semantic structure comprises predicting semantic structure associated with the data object (Kurian, abstract, figures 2A-2E and 3, [0024, 0039, 0052], note using machine learning to identify a type of data, e.g., semantic structure, which is interpreted as predicting the semantic structure).
Regarding Claim 3:
Kurian as modified shows the computing device as disclosed above;
Kurian as modified further teaches:
wherein identifying one or more relationships associated with the semantic structure comprises implementing one or more machine learning models to determine the one or more relationships (Kurian, abstract, figures 2A-2E and 3, [0024, 0039, 0052, 0054], note using machine learning models to identify relationships based on data type).
Regarding Claim 4:
Kurian as modified shows the computing device as disclosed above;
Kurian as modified further teaches:
wherein the one or more machine learning models comprise a recurrent neural network trained with known typical workload traces (Kurian, [0023, 0033-0035, 0061], note the use of artificial neural network algorithms, which include recurrent neural networks; note the machine learning models are trained on historical data, outputs or outcomes of actions, etc., which are interpreted as typical workload traces. It is also noted that this limitation is nonfunctional descriptive material as explained in section 2111.05 of the MPEP and does not hold patentable weight).
Regarding Claim 5:
Kurian as modified shows the computing device as disclosed above;
Kurian as modified further teaches:
wherein determining a view of the data object comprises prefetching the data object, caching the data object in a higher-level cache, or updating metadata associated with the data object (Kurian, abstract, figures 2A-2E and 3, [0024, 0039, 0054-0055, 0071], note storing the data element and connections/relationships is interpreted as updating metadata associated with the data object; note a user may receive the stored data and understand connections to the other data based on the stored connections, which is interpreted to mean the stored data element and connections is a view of the data object).
Regarding Claim 7:
Kurian as modified shows the computing device as disclosed above;
Kurian as modified further teaches:
sending the data object to a training server to train a plurality of machine learning models (Kurian, figure 1B, [0023, 0033-0035, 0061], note the machine learning models are trained on historical data, outputs or outcomes of actions, etc., which are interpreted to include the data object; note the use of one or more machine learning models).
Regarding Claim 8:
Kurian as modified shows the computing device as disclosed above;
Kurian as modified further teaches:
wherein the one or more relationships comprise data type, data size, data attributes, or access protocol (Kurian, abstract, figures 2A-2E and 3, [0024, 0039, 0052, 0054], note identifying relationships based on data type. It is also noted that this limitation is nonfunctional descriptive material as explained in section 2111.05 of the MPEP and does not hold patentable weight).
Regarding Claim 9:
Kurian teaches:
A method comprising: receiving a query from a client device to access a data object (Kurian, figures 2A-2E and 3, [0039, 0041, 0046, 0050], note requesting and receiving data);
identifying semantic structure associated with the data object (Kurian, abstract, figures 2A-2E and 3, [0024, 0039, 0052], note analyzing the received data to identify a type of data, e.g., semantic structure);
identifying one or more relationships associated with the semantic structure of the data object (Kurian, abstract, figures 2A-2E and 3, [0024, 0039, 0052, 0054], note identifying relationships based on data type);
determining a view of the data object based on the one or more relationships (Kurian, abstract, figures 2A-2E and 3, [0024, 0039, 0054-0055, 0071], note storing the data element and connections/relationships; note a user may receive the stored data and understand connections to the other data based on the stored connections, which is interpreted to mean the stored data element and connections is a view of the data object);
providing the view of the data object to a user interface to consume the data (Kurian, abstract, figures 2A-2E and 3, [0024, 0039, 0054-0055, 0071], note storing the data element and connections/relationships; note a user may receive the stored data and understand connections to the other data based on the stored connections, which is interpreted as providing the view of the data object to the user); and
sending the data object to a training server to train a plurality of machine learning models (Kurian, figure 1B, [0023, 0033-0035, 0061], note the machine learning models are trained on historical data, outputs or outcomes of actions, etc., which are interpreted to include the data object; note the use of one or more machine learning models).
While Kurian as modified teaches data object views, Kurian as modified doesn’t specifically state the data request is a user query. However, Wilczynski is in the same field of endeavor, data management, and Wilczynski teaches:
receiving a user query from a client device to access a data object (Wilczynski, column 2 lines 36-56, column 3 lines 8-61, note the user is making the request for the data object);
determining a view of the data object based on the one or more relationships (Wilczynski, figure 5, column 2 lines 36-56, column 3 lines 8-61, column 10 lines 56-67, note determining a data object view for the request); and
providing the view of the data object to a user interface to consume the data (Wilczynski, figure 5, column 2 lines 36-56, column 3 lines 8-61, column 10 lines 56-67, note determining a data object view for the request and providing it to the user).
It would have been obvious to one of ordinary skill in the art before the effective date of filing to modify the cited references to incorporate the teachings of Wilczynski because all references are directed towards data management and because Wilczynski would expand upon the teachings of the previously cited references in data object management which would improve the performance and flexibility of the system by determining and providing data object views (Wilczynski, column 1 lines 12-58, column 2 lines 36-56).
Regarding Claim 10:
Kurian as modified shows the method as disclosed above;
Kurian as modified further teaches:
wherein identifying semantic structure comprises predicting semantic structure associated with the data objects the client device is trying to access (Kurian, abstract, figures 2A-2E and 3, [0024, 0039, 0052], note using machine learning to identify a type of data, e.g., semantic structure, which is interpreted as predicting the semantic structure) (Wilczynski, column 2 lines 36-56, column 3 lines 8-61, note the user is making the request for the data object).
It would have been obvious to one of ordinary skill in the art before the effective date of filing to modify the cited references to incorporate the teachings of Wilczynski because all references are directed towards data management and because Wilczynski would expand upon the teachings of the previously cited references in data object management which would improve the performance and flexibility of the system by determining and providing data object views (Wilczynski, column 1 lines 12-58, column 2 lines 36-56).
Regarding Claim 11:
Kurian as modified shows the method as disclosed above;
Kurian as modified further teaches:
wherein identifying one or more relationships associated with the semantic structure comprises implementing one or more machine learning models to determine the one or more relationships (Kurian, abstract, figures 2A-2E and 3, [0024, 0039, 0052, 0054], note using machine learning models to identify relationships based on data type).
Regarding Claim 12:
Kurian as modified shows the method as disclosed above;
Kurian as modified further teaches:
wherein the one or more machine learning models comprise a recurrent neural network trained with known typical workload traces (Kurian, [0023, 0033-0035, 0061], note the use of artificial neural network algorithms, which include recurrent neural networks; note the machine learning models are trained on historical data, outputs or outcomes of actions, etc., which are interpreted as typical workload traces. It is also noted that this limitation is nonfunctional descriptive material as explained in section 2111.05 of the MPEP and does not hold patentable weight).
Regarding Claim 13:
Kurian as modified shows the method as disclosed above;
Kurian as modified further teaches:
wherein determining a view of the data object comprises prefetching the data object, caching the data object in a higher-level cache, or updating metadata associated with the data object (Kurian, abstract, figures 2A-2E and 3, [0024, 0039, 0054-0055, 0071], note storing the data element and connections/relationships is interpreted as updating metadata associated with the data object; note a user may receive the stored data and understand connections to the other data based on the stored connections, which is interpreted to mean the stored data element and connections is a view of the data object).
Regarding Claim 15:
Kurian as modified shows the method as disclosed above;
Kurian as modified further teaches:
wherein the one or more relationships comprise data type, data size, data attributes, or access protocol (Kurian, abstract, figures 2A-2E and 3, [0024, 0039, 0052, 0054], note identifying relationships based on data type. It is also noted that this limitation is nonfunctional descriptive material as explained in section 2111.05 of the MPEP and does not hold patentable weight).
Claim 16 discloses substantially the same limitations as claim 1 respectively, except claim 16 is directed to a non-transitory computer-readable storage medium (Kurian, figure 4, note processor and memory) while claim 1 is directed to a computing device. Therefore claim 16 is rejected under the same rationale set forth for claim 1.
Claim 17 discloses substantially the same limitations as claim 3 respectively, except claim 17 is directed to a non-transitory computer-readable storage medium (Kurian, figure 4, note processor and memory) while claim 3 is directed to a computing device. Therefore claim 17 is rejected under the same rationale set forth for claim 3.
Claim 18 discloses substantially the same limitations as claim 4 respectively, except claim 18 is directed to a non-transitory computer-readable storage medium (Kurian, figure 4, note processor and memory) while claim 4 is directed to a computing device. Therefore claim 18 is rejected under the same rationale set forth for claim 4.
Claim 19 discloses substantially the same limitations as claim 5 respectively, except claim 19 is directed to a non-transitory computer-readable storage medium (Kurian, figure 4, note processor and memory) while claim 5 is directed to a computing device. Therefore claim 19 is rejected under the same rationale set forth for claim 5.
Claim 20 discloses substantially the same limitations as claim 7 respectively, except claim 20 is directed to a non-transitory computer-readable storage medium (Kurian, figure 4, note processor and memory) while claim 7 is directed to a computing device. Therefore claim 20 is rejected under the same rationale set forth for claim 7.
Claim Rejections - 35 USC § 103
Claim(s) 6 and 14 is/are rejected under 35 U.S.C. 103 as being unpatentable over Kurian in view of Wilczynski and Subramanian et al. (US2007/0216674), hereinafter Subramanian.
Regarding Claim 6:
Kurian as modified shows the computing device as disclosed above;
While Kurian as modified teaches data object views, Kurian as modified doesn’t specifically teach prefetching data. However, Subramanian is in the same field of endeavor, data management, and Subramanian teaches:
wherein prefetching the data object comprises speculatively executing inline data or metadata operations through precision conversion, data filtering, or regular expression matching (Subramanian, abstract, figures 4-5, [0022-0023], note prefetching data in response to a request to display a first data, which is interpreted as speculatively executing inline data through data filtering).
It would have been obvious to one of ordinary skill in the art before the effective date of filing to modify the cited references to incorporate the teachings of Subramanian because all references are directed towards data management and because Subramanian would expand upon the teachings of the previously cited references in data retrieval which would improve the speed and performance of the system by prefetching data to reduce the time required to load the data.
Regarding Claim 14:
Kurian as modified shows the computing device as disclosed above;
While Kurian as modified teaches data object views, Kurian as modified doesn’t specifically teach prefetching data. However, Subramanian is in the same field of endeavor, data management, and Subramanian teaches:
wherein prefetching the data object comprises speculatively executing inline data or metadata operations through precision conversion, data filtering, or regular expression matching (Subramanian, abstract, figures 4-5, [0022-0023], note prefetching data in response to a request to display a first data, which is interpreted as speculatively executing inline data through data filtering).
It would have been obvious to one of ordinary skill in the art before the effective date of filing to modify the cited references to incorporate the teachings of Subramanian because all references are directed towards data management and because Subramanian would expand upon the teachings of the previously cited references in data retrieval which would improve the speed and performance of the system by prefetching data to reduce the time required to load the data.
Conclusion
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/JOHN J MORRIS/Examiner, Art Unit 2152 10/29/2025
/NEVEEN ABEL JALIL/Supervisory Patent Examiner, Art Unit 2152
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