DETAILED ACTION
This action is in response to the arguments and RCE and amendments filed on June 16, 2025.
Claims 21-40 have been examined. Claims 21-36 and 38-40 have been amended. Claims 1-20 have been cancelled in Amendment dated 1/9/2023.
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on June 16, 2025 has been entered.
Information Disclosure Statement
The information disclosure statements (IDS) submitted on June 18, 2025 and December 22, 2025 have been considered by the examiner.
Claim Objections
Claim 26 is objected to because of the following informalities: amended claim 26 reads: “… the metadata, is associated with the the plurality of memory locations…” Appropriate correction is required.
Claim 28 is objected to because of the following informalities: amended claim 28 reads: “… the metadata, is associated with the the plurality of memory locations…” Appropriate correction is required.
Claim Rejections - 35 USC § 112
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention.
Claims 21-40 rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention.
Independent claims 21, 38, and 39 recite “each subset of the plurality of subsets corresponding to a particular data type of a particular data types, wherein each data type of the plurality of data types is stored at different memory location of the plurality of memory locations…” Applicant’s specification does not detail subsets corresponding to particular data types stored at different memory locations of the plurality of memory locations. Any claim not specifically addressed above is being rejected as incorporating the deficiencies of a claim upon which it depends.
The previous rejection of claims 21-40 under 35 U.S.C. 112(a) due to the “metadata comprising one or more attributes indicating where two or more subsets of the first information are stored” is withdrawn in view of Applicant’s amendments to the claims.
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 21-40 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
When considering subject matter eligibility under 35 U.S.C. § 101, it must be determined whether the claim is directed to one of the four statutory categories of invention, i.e., process, machine, manufacture, or composition of matter (Step 1). If the claim does fall within one of the statutory categories, the second step in the analysis is to determine whether the claim is directed to a judicial exception (Step 2A). The step 2A analysis is broken into two prongs. In the first prong (Step 2A, Prong 1), it is determined whether or not the claims recite a judicial exception (e.g. mathematical concepts, mental processes, certain methods of organizing human activity). If it is determined in step 2A, Prong 1 that the claims recite a judicial exception, the analysis proceeds to the second prong (Step 2A, Prong 2), where it is determined whether or not the claims integrate the judicial exception into a practical application. If it is determined that step 2A, Prong that the claims do not integrate the judicial exception into a practical application, the analysis proceeds to determining whether the claim is a patent-eligible application of the exception (Step 2B). If an abstract idea is present in the claim, any element or combination of elements in the claim must be sufficient to ensure that the claim integrates the judicial exception into a practical application, or else amounts to significantly more than the abstract idea itself.
According to Step 1 of the analysis, in the instant case claims 21-36 are directed to a method, claim 38 is directed to a non-transitory computer-readable medium, and claims 39 and 40 are directed to a system. Thus, each of the claims falls within one of the four statutory categories (i.e., process, machine, manufacture, or composition of matter).
Considering claim 21 and Step 2A, Prong One, the limitations including: “determining using metadata comprising one or more attributes corresponding to a set of input data, a plurality of memory locations corresponding to storage of the set of input data, wherein the set of input data comprises a plurality of subsets of input data, each subset of the plurality of subsets corresponding to a particular data type of a plurality of data types, wherein each data type of the plurality of data types is stored at a different memory location of the plurality of memory locations” and “generating a set of inferences using the set of input data,” cover performance of the mind. That is, nothing in the claim element precludes the step from practically being performed in the mind.
MPEP 2106.04(a)(2)(III) notes “the "mental processes" abstract idea grouping is defined as concepts performed in the human mind, and examples of mental processes include observations, evaluations, judgments, and opinions.” The “determining” and “generating” limitations are an evaluation/judgment of data, and a human is capable of determining correspondence of input data and metadata, and generating a set of inferences using the data mentally, or with pen and paper.
Considering Step 2A, Prong Two, the judicial exception in claim 21 is not integrated into a practical application. Claim 21 includes the additional elements: “providing, as input to a neural network, each subset of input data from a memory location of the plurality of memory locations corresponding to a data type of the particular subset of input data.” The “providing” limitation is insignificant extra-solution activity, mere data gathering; see MPEP 2106.05(g).
Further, considering Step 2B, the claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. The claim recites additional elements: providing, as input to a neural network, each subset of input data from a memory location of the plurality of memory locations corresponding to a data type of the particular subset of input data.” The “providing” limitation is insignificant extra-solution activity, mere data gathering, and does not amount to significantly more; see MPEP 2106.05(g). Therefore, the limitation does not amount to significantly more and the claim is not eligible in view of 101.
Claim 22, dependent on claim 21, is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without integration into a practical application or significantly more.
The claimed “set of input data comprises one or more required inputs for the neural network” is an additional element that is not integrated into a practical application, nor does it amount to significantly more. The input data is insignificant pre-solution extra-solution activity, data gathering; see MPEP 2106.05(g). Further, the neural network is extra-solution activity because it is a tangential addition to the claim; see MPEP 2106.05(g). Additionally, the limitations are well-understood, routine, and conventional computer functions including receiving or transmitting data over a network and/or storing and retrieving information in memory; see MPEP 2106.05(d)(II).
Claim 23, dependent on claim 21, is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without integration into a practical application or significantly more.
The claimed “plurality of data types indicates one or more types of information contained within the respective data type” is an additional element that is not integrated into a practical application, nor does it amount to significantly more. The data type details are insignificant pre-solution extra-solution activity, data gathering; see MPEP 2106.05(g). Additionally, the limitations are well-understood, routine, and conventional computer functions including receiving or transmitting data over a network and/or storing and retrieving information in memory; see MPEP 2106.05(d)(II).
Claim 24, dependent on claim 21, is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without integration into a practical application or significantly more.
The claimed “set of input data comprises data stored in a memory device by a software application and the one mor more attributes indicates on or more memory locations storing the set of input data” is an additional element that is not integrated into a practical application, nor does it amount to significantly more. The data type details are insignificant pre-solution extra-solution activity, data gathering; see MPEP 2106.05(g). Additionally, the limitations are well-understood, routine, and conventional computer functions including receiving or transmitting data over a network and/or storing and retrieving information in memory; see MPEP 2106.05(d)(II).
Claim 25, dependent on claim 21, is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without integration into a practical application or significantly more.
The claimed “receiving the metadata, comprising data for a software application, and storing the metadata in association with the software application” is an additional element that is not integrated into a practical application, nor does it amount to significantly more. The data gathering details are insignificant pre-solution extra-solution activity, data gathering; see MPEP 2106.05(g). Additionally, the limitations are well-understood, routine, and conventional computer functions including receiving or transmitting data over a network and/or storing and retrieving information in memory; see MPEP 2106.05(d)(II).
Claim 26, dependent on claim 21, is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without integration into a practical application or significantly more.
The claimed “the metadata, is associated with the the plurality of memory locations, comprising data from a software application, by being inserted in a portion of code of the software application that defines locations in a memory device in which the data is stored” is an additional element that is not integrated into a practical application, nor does it amount to significantly more. The data details are insignificant pre-solution extra-solution activity, data gathering; see MPEP 2106.05(g). Additionally, the limitations are well-understood, routine, and conventional computer functions including receiving or transmitting data over a network and/or storing and retrieving information in memory; see MPEP 2106.05(d)(II).
Claim 27, dependent on claim 21, is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without integration into a practical application or significantly more.
The claimed “configuration files indicate one or more types of data to be used as input to the neural network” is an additional element that is not integrated into a practical application, nor does it amount to significantly more. The file details are insignificant pre-solution extra-solution activity, data gathering; see MPEP 2106.05(g). Additionally, the limitations are well-understood, routine, and conventional computer functions including receiving or transmitting data over a network and/or storing and retrieving information in memory; see MPEP 2106.05(d)(II).
Claim 28, dependent on claim 21, is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without integration into a practical application or significantly more.
The claimed “the metadata, is associated with the the plurality of memory locations, comprising data of a software application, by being stored in a reference table that maps each metadata to a corresponding portion of the data or location in a memory device in which the corresponding portion of the data is stored” is an additional element that is not integrated into a practical application, nor does it amount to significantly more. The file details are insignificant pre-solution extra-solution activity, data gathering; see MPEP 2106.05(g). Additionally, the limitations are well-understood, routine, and conventional computer functions including receiving or transmitting data over a network and/or storing and retrieving information in memory; see MPEP 2106.05(d)(II).
Claim 29, dependent on claim 21, is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without integration into a practical application or significantly more.
The claimed “software application loads the set of input data, comprising data, in a memory device and applies a third set of information to the data in the memory device” is an additional element that is not integrated into a practical application, nor does it amount to significantly more. The software and device details are well-understood, routine, and conventional computer functions including receiving or transmitting data over a network and/or storing and retrieving information in memory; see MPEP 2106.05(d)(II).
Claim 30, dependent on claim 21, is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without integration into a practical application or significantly more.
Considering claim 30 and Step 2A, Prong One, the limitations including: “using the metadata from a software application to infer one or more required inputs to be used by the neural network includes: matching an identifier to each of the one or more required inputs to be use as inputs by the neural network to metadata defined for certain data of the software application” cover performance of the mind. That is, nothing in the claim element precludes the step from practically being performed in the mind. MPEP 2106.04(a)(2)(III) notes “the "mental processes" abstract idea grouping is defined as concepts performed in the human mind, and examples of mental processes include observations, evaluations, judgments, and opinions.” The “infer” and “matching” limitations are an evaluation/judgment of data, and a human is capable of determining correspondence of input data and metadata, and generating a set of inferences using the data mentally, or with pen and paper.
Considering Step 2A, Prong Two, the judicial exception in claim 31 is not integrated into a practical application. Claim 31 includes the additional elements: “and retrieving the certain data of the software application.” The “retrieving” limitation is insignificant extra-solution activity, mere data gathering; see MPEP 2106.05(g).
Further, considering Step 2B, the claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. The claim recites additional elements: providing, as input to a neural network, each subset of input data from a memory location of the plurality of memory locations corresponding to a data type of the particular subset of input data.” The “retrieving” limitation is insignificant extra-solution activity, mere data gathering, and does not amount to significantly more; see MPEP 2106.05(g). Therefore, the limitation does not amount to significantly more and the claim is not eligible in view of 101.
Claim 31, dependent on claim 21, is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without integration into a practical application or significantly more.
The claimed “one or more subsets of the plurality of subsets of input data, comprising data of a software application, are used as inputs by the neural network to train a deep learning model” is an additional element that is not integrated into a practical application, nor does it amount to significantly more. The software and device details are well-understood, routine, and conventional computer functions including receiving or transmitting data over a network and/or storing and retrieving information in memory; see MPEP 2106.05(d)(II).
Claim 32, dependent on claim 21, is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without integration into a practical application or significantly more.
The claimed “generating the set of inferences is performed responsive to a call to the neural network by a software application” is a mental process. MPEP 2106.04(a)(2)(III) notes “the "mental processes" abstract idea grouping is defined as concepts performed in the human mind, and examples of mental processes include observations, evaluations, judgments, and opinions.” The “generating the set of inferences” limitation is an evaluation/judgment of data, and a human is capable of determining correspondence of input data and metadata, and generating a set of inferences using the data mentally, or with pen and paper.
Claim 33, dependent on claim 21, is not rejected under 35 U.S.C. 101, A human is not capable of performing a deep learning model executor that interfaces a software application.
Claim 34, dependent on claim 33, is not rejected under 35 U.S.C. 101 for the reasons stated above.
Claim 35, dependent on claim 21, is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without integration into a practical application or significantly more.
The claimed “receiving inferenced data as output of the neural network; and providing the inferenced data to a software application” is an additional element that is not integrated into a practical application, nor does it amount to significantly more. The file details are insignificant pre-solution extra-solution activity, data gathering; see MPEP 2106.05(g). Additionally, the limitations are well-understood, routine, and conventional computer functions including receiving or transmitting data over a network and/or storing and retrieving information in memory; see MPEP 2106.05(d)(II).
Claim 36, dependent on claim 21, is not rejected under 35 U.S.C. 101 because a human is not capable of updating a neural network
Claim 37, dependent on claim 36, is not rejected under 35 U.S.C. 101 for the reasons stated above.
Considering claim 38 and Step 2A, Prong One, the limitations including: “determining using metadata comprising one or more attributes corresponding to a set of input data, a plurality of memory locations corresponding to storage of the set of input data, wherein the set of input data comprises a plurality of subsets of input data, each subset of the plurality of subsets corresponding to a particular data type of a plurality of data types, wherein each data type of the plurality of data types is stored at a different memory location of the plurality of memory locations” and “generating a set of inferences using the set of input data,” cover performance of the mind. That is, nothing in the claim element precludes the step from practically being performed in the mind.
MPEP 2106.04(a)(2)(III) notes “the "mental processes" abstract idea grouping is defined as concepts performed in the human mind, and examples of mental processes include observations, evaluations, judgments, and opinions.” The “determining” and “generating” limitations are an evaluation/judgment of data, and a human is capable of determining correspondence of input data and metadata, and generating a set of inferences using the data mentally, or with pen and paper.
Considering Step 2A, Prong Two, the judicial exception in claim 38 is not integrated into a practical application. Claim 38 includes the additional elements: “[a] non-transitory computer-readable medium storing computer instructions”, “one or more processors”, and “providing, as input to a neural network, each subset of input data from a memory location of the plurality of memory locations corresponding to a data type of the particular subset of input data.” The non-transitory computer-readable medium and processors are generic computer components and do not integrate the judicial exception into a practical application; see MPEP 2106.05(f). Additionally, the “providing” limitation is insignificant extra-solution activity, mere data gathering; see MPEP 2106.05(g).
Further, considering Step 2B, the claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. The claim recites additional elements: “[a] non-transitory computer-readable medium storing computer instructions”, “one or more processors”, and “providing, as input to a neural network, each subset of input data from a memory location of the plurality of memory locations corresponding to a data type of the particular subset of input data.” The non-transitory computer-readable medium and processors are generic computer components and do not amount to significantly more; see MPEP2106.05(I)(B). Additionally, the “providing” limitation is insignificant extra-solution activity, mere data gathering, and does not amount to significantly more; see MPEP 2106.05(g). Therefore, the limitation does not amount to significantly more and the claim is not eligible in view of 101.
Considering claim 39 and Step 2A, Prong One, the limitations including: “determining using metadata comprising one or more attributes corresponding to a set of input data, a plurality of memory locations corresponding to storage of the set of input data, wherein the set of input data comprises a plurality of subsets of input data, each subset of the plurality of subsets corresponding to a particular data type of a plurality of data types, wherein each data type of the plurality of data types is stored at a different memory location of the plurality of memory locations” and “generating a set of inferences using the set of input data,” cover performance of the mind. That is, nothing in the claim element precludes the step from practically being performed in the mind.
MPEP 2106.04(a)(2)(III) notes “the "mental processes" abstract idea grouping is defined as concepts performed in the human mind, and examples of mental processes include observations, evaluations, judgments, and opinions.” The “determining” and “generating” limitations are an evaluation/judgment of data, and a human is capable of determining correspondence of input data and metadata, and generating a set of inferences using the data mentally, or with pen and paper.
Considering Step 2A, Prong Two, the judicial exception in claim 39 is not integrated into a practical application. Claim 39 includes the additional elements: “a memory storing computer instructions”, “one or more processors”, and “providing, as input to a neural network, each subset of input data from a memory location of the plurality of memory locations corresponding to a data type of the particular subset of input data.” The memory and processors are generic computer components and do not integrate the judicial exception into a practical application; see MPEP 2106.05(f). Additionally, the “providing” limitation is insignificant extra-solution activity, mere data gathering; see MPEP 2106.05(g).
Further, considering Step 2B, the claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. The claim recites additional elements: “a memory storing computer instructions”, “one or more processors”, and “providing, as input to a neural network, each subset of input data from a memory location of the plurality of memory locations corresponding to a data type of the particular subset of input data.” The memory and processors are generic computer components and do not amount to significantly more; see MPEP2106.05(I)(B). Additionally, the “providing” limitation is insignificant extra-solution activity, mere data gathering, and does not amount to significantly more; see MPEP 2106.05(g). Therefore, the limitation does not amount to significantly more and the claim is not eligible in view of 101.
Claim 40, dependent on claim 39, is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without integration into a practical application or significantly more.
The claimed “memory further stores the metadata, and associated set of input data, comprising data of a software application” is an additional element that is not integrated into a practical application, nor does it amount to significantly more. The data type details are insignificant pre-solution extra-solution activity, data gathering; see MPEP 2106.05(g). Additionally, the limitations are well-understood, routine, and conventional computer functions including receiving or transmitting data over a network and/or storing and retrieving information in memory; see MPEP 2106.05(d)(II).
Claim Rejections - 35 USC § 102
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)(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 21, 23-25, 27, and 29-40 are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Biesemann et al. (US 2019/0180189, hereinafter Biesemann).
As per claim 21, Biesemann teaches:
A method comprising:
determining using metadata comprising one or more attributes corresponding to a set of input data (Biesemann teaches an input data identifier in [0041] that identifies where the necessary information is stored on the client.), a plurality of memory locations corresponding to storage of the set of input data (Biesemann teaches various memories for storing input data in figure 1 and [0029]; see memories 114 and 180 in figure 1, and the various input data stored in the memories.), wherein the set of input data comprises a plurality of subsets of input data (Biesemann teaches an input definition which describes the inputs required to allow the neural network to be able to generate a corresponding output and that the input definition may be associated with particular sets of data (i.e. subsets) that are associated with a particular user, entity, business, or organization; see [0031].), each subset of the plurality of subsets corresponding to a particular data type of a plurality of data types (Biesemann teaches an input definition may be associated with particular sets of data that are associated with a particular user, entity, business, or organization; see [0031].), wherein each data type of the plurality of data types is stored at a different memory location of the plurality of memory locations (Biesemann teaches a memory (114) that stores a variety of data types in figure 1 and [0029]. The claimed “memory location” is not detailed in the claims or specification; [0035] merely indicates the memory location is the locations in memory in which the data is to be stored. A given memory location (which could be interpreted as the physical transistors used to store bits) is capable of only storing one type of data. Therefore, any additional data being stored would have to be stored at a different memory location. Given the broad description of “memory location” Biesemann’s teachings are sufficient to teach the claimed features.);
providing, as input to a neural network, each subset of input data from a memory location of the plurality of memory locations corresponding to a data type of the particular subset of input data (Biesemann teaches that each neural network can be associated with an input definition which describes the particular inputs required to allow the neural network to be able to generate a corresponding response in [0031].); and
generating a set of inferences using the set of input data (Biesemann teaches that each neural network can be associated with an input definition which describes the particular inputs required to allow the neural network to be able to generate a corresponding response in [0031]. The output of a neural network, described by Biesemann in [0031], is an inference.).
As per claim 23, Biesemann teaches wherein each of the plurality of data types indicates one or more types of information contained within the respective data type (i.e., the input definition can specifically link to particular information, can identify a set of predefined queries or data access, or can include one or more user inputs that need to be received in order to execute, input data may be associated with particular sets of data as they are associated with a particular user, entity, business, or organization, backend data definition, see at least [0023], [0031]).
As per claim 24, Biesemann teaches wherein the set of input data comprises data stored in a memory device by a software application and the one more attributes indicates one or more memory locations storing the set of input data (i.e., backend data may be any suitable data used by the business application, backend data may be accessible via one or more queries executed upon the backend data and its storage components, the input definition can identify a set of predefined queries or data accesses, [0030], [0031]).
As per claim 25, Biesemann teaches receiving the metadata, comprising data for a software application (Biesemann teaches software implementation throughout the disclosure; see, in particular, [0011]. Biesemann also teaches input data identifier that may identify where the necessary information is stored on the client; see [0041]. Location data is metadata according to [0033] of the instant specification; “the metadata is any descriptive information that can be associated with the data of the software application.”), and storing the metadata in association with the software application (i.e., client applicant may include neural network sync manager the neural network sync manager may identify the particular input data needed from the input data identifier and/or a copy of the input definition, see at least [0050]).
As per claim 27, Biesemann teaches wherein one or more configuration files indicate one or more types of data to be used as input to the neural network (i.e., the input definition can specifically link to particular information, can identify a set of predefined queries or data access, or can include one or more user inputs that need to be received in order to execute, input data may be associated with particular sets of data as they are associated with a particular user, entity, business, or organization, backend data definition, see at least [0031]).
As per claim 29, Biesemann teaches wherein a software application loads the set of input data, comprising data, in a memory device (i.e., backend data may be accessible via one or more queries executed upon the backend data and its storage components, see at least [003]) and applies a third set of information, to the data in the memory device (i.e., input definition can identify a set of predefined queries or data accesses, see at least [0031]).
As per claim 30, Biesemann teaches wherein using the metadata from a
software application to infer one or more required inputs to be used by the neural network, includes:
matching an identifier to each of the one or more required inputs to be used as inputs by the neural network to metadata defined for certain data of the software application (i.e., input definition can identify a set of predefined queries or data accesses, see at least [0007], [0030], [0031]), and
retrieving the certain data of the software application (i.e., obtain the set of data responsive to the at least one predefined query, see at least [0007], [0030], [0031]).
As per claim 31, Biesemann teaches wherein one or more subsets of the plurality of subsets of input data, comprising data of a software application, are used as inputs by the neural network to train a deep learning model (i.e., end users at the mobile device may create new business data during execution in a business mode for the application, where the new business data is considered by the calculations in the neural network, the business data can be used in additional training of the neural network, see at least [0021]).
As per claim 32, Biesemann teaches wherein generating the set of inferences is performed responsive to a call to the neural network by a software application (i.e., client application sending requests for execution or execute the offline neural network, see at least [0049]).
As per claim 33, Biesemann teaches wherein generating the set of inferences is performed by a deep learning model executor that interfaces a software application (i.e., execution type module manages execution of the neural network in the offline mode, route any attempted client application operations to the offline neural network, see at least [0051]).
As per claim 34, Biesemann teaches wherein the deep learning model executor, the software application, and the neural network is installed on a client system (see at least Fig. 1, [0049]).
As per claim 35, Biesemann teaches receiving inferenced data as output of the neural network; and providing the inferenced data to a software application (i.e., receiving the output of the operation at the client application, see at least [0049]).
As per claim 36, Biesemann teaches updating the neural network, wherein the updated neural network is configured to be performed using one or more new second information different from an inferred one (i.e., update neural network using new data, see at least [0021], [0022]);
determining one or more new inputs for an updated deep learning model (i.e., the executed queries may be dynamically derived based on the information required for a particular neural network execution, see at least [0031], [0057], [0058]);
using a second metadata associated with data of a software application to retrieve one or more new portions of the data from the software application that satisfy the one or more new inputs to be used by the neural network (i.e., the executed queries may be dynamically derived based on the information required for a particular neural network execution, see at least [0031], [0057], [0058]); and
providing the retrieved one or more new portions of the data to the updated deep learning model to perform inferencing operations to generate new inferenced data (see at least [0023], [0031]).
As per claim 37, Biesemann teaches receiving the new inferenced data as output of the updated deep learning model (i.e., outputs associated with execution of the neural network can continue to evolve, see at least [0033], [0049]); and
providing the new inferenced data to the software application (i.e., receiving the output of the operation at the client application, see at least [0049]).
As per claim 38, this is the non-transitory computer-readable medium claim of claim 21. Therefore, claim 38 is rejected using the same reasons as claim 21.
As per claim 39, this is the system claim of claim 21. Therefore, claim 39 is rejected using the same reasons as claim 21.
As per claim 40, Biesemann teaches wherein the memory further stores the metadata, and associated set of input data, comprising data of a software application (see at least Fig. 1, [0029]-[0031]).
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 22 is rejected under 35 U.S.C. 103 as being unpatentable over Biesemann, in view of Biswas et al. (US 2019/0102098, hereinafter Biswas).
As per claim 22, Biesemann does not explicitly teach wherein the set of input data comprises one or more required inputs for the neural network.
Biswas teaches wherein the set of input data comprises one or more required inputs for the neural network (i.e., use verified outputs from the particular machine learning system to update training dataset, see at least [0114]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Biesemann such that the second set of information comprises one or more required inputs for the one or more neural networks as similarly taught by Biswas in order to update training dataset using verified outputs (see at least [0114] of Biswas).
Claims 26 and 28 are rejected under 35 U.S.C. 103 as being unpatentable over Biesemann, in view of Bruckhaus et al. (US 8,417,715, hereinafter Bruckhaus)
As per claim 26, Biesemann teaches wherein the metadata, is associated with the plurality of memory locations, comprising data of a software application (see at least [0021], [0030], [0031]).
Biesemann does not explicitly teach by being inserted in a portion of code of the software application that defines locations in a memory device in which the data is stored.
Bruckhaus teaches metadata is associated with the data of the software application by being inserted in a portion of code of the software application that defines the locations in the memory device in which the data is stored (i.e., invention is a plug in to software application, invention use a common data model for data mining, integration of user’s data sources, see at least column 13, lines 28-48, column 14, lines 56-67, column 18, lines 16-65).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Biesemann such that the metadata is associated with the data of the software application by being inserted in a portion of code of the software application that defines the locations in the memory device in which the data is stored as similarly taught by Bruckhaus because providing functionality using a plug-in to another software unit is a well-known technique in the art and allows the plug-in to extend an existing function (see at least column 83, lines 21-29 of Bruckhaus).
As per claim 28, Biesemann teaches wherein the metadata, is associated with the plurality of memory locations, comprising data of a software application (see at least [0021], [0030], [0031]).
Biesemann does not explicitly teach by being stored in a reference table that maps each metadata to a corresponding portion of the data or location in a memory device in which the corresponding portion of the data is stored.
Bruckhaus teaches wherein the metadata is associated with the data of the software application by being stored in a reference table that maps each metadata to a corresponding portion of the data or location in the memory device in which the corresponding portion of the data is stored (i.e., create mapping user’s data source and table defined by the CDMDM, see at least column 18, lines 16-65).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Biesemann such that the metadata is associated with the data of the software application by being stored in a reference table that maps each metadata to a corresponding portion of the data or location in the memory device in which the corresponding portion of the data is stored as similarly taught by Bruckhaus to use known techniques in the art to integrate user’s data sources using a data structure that maps the user’s data sources (see at least column 14, lines 56-67 of Bruckhaus).
Response to Arguments
Considering Applicant’s remarks, filed on June 16, 2025:
Rejection of claims under §112(a):
Beginning on page 12 of remarks, Applicant argues that the previous rejection of claims under §112(a) has been overcome due to the amendments made to claims 21, 38, and 39. The offending language has been removed and the rejection is withdrawn. However, the amended claims are rejected under §112(a), as detailed above.
Rejection of claims under §102 in view of Biesemann:
Also on page 12 of remarks, Applicant argues that Biesemann does not teach the newly amended features found in independent claims 21, 38, and 39. Claims 21, 38, and 39 have been rejected by Biesemann, as indicated above. Applicant’s arguments that Biesemann does not teach the newly claimed features are not persuasive. For example, on page 13 of remarks, Applicant argues that Biesemann relates to a GAN. However, Biesemann does not disclose a GAN model anywhere in the disclosure. Paragraph 0023 of Biesemann, referenced in Applicant’s arguments, discloses neural networks and not GANs. It is believed Applicant has confused Biesemann with another reference and, as such, the argument is not persuasive.
Conclusion
Claims 21-40 are rejected.
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/DANIEL T PELLETT/Primary Examiner, Art Unit 2121