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 .
This is a Final Office Action in response to the amendment, filed on 07/28/2025.
Claims 1, 3, 7-9, 11, 13, 16-21 and 25 are amended. Claims 4, 6, 14-15 and 24 are canceled. Claims 1-3, 5, 7-13 and 16-23 and 25 are presented for examination, with claims 1, 11 and 18 being independent.
Response to Arguments
Claim Rejections - 35 USC § 101
Applicant’s argument with regard to rejection of claims 1-3, 5, 7-13 and 16-23 and 25 under 35 U.S.C. 101 is acknowledged. However, Examiner is not persuaded. Based upon the consideration of claim 1 and all of the relevant factors with respect to the claim as a whole, it is directed to a judicial exception (i.e., abstract idea) without significantly more. There are no additional limitations recited beyond the judicial exception itself that integrate the exception into a practical application. More particularly, the claim does not recite: (i) an improvement to the functionality of a computer or other technology or technical field (see MPEP §2106.05(a)); (ii) a “particular machine” to apply or use the judicial exception (see MPEP 2106.05(b)); (iii) a particular transformation of an article to a different thing or state (see MPEP §2106.05(c)); or (iv) any other meaningful limitation (see MPEP §2106.05(e)). See also Guidance, 84 FED. Reg. at 55.
The claim is broadly written. Claim 1, as an exemplary claims is directed to a method for transmitting and storing data. Wherein, a transmitting updated data for storing is made as to whether the updated data are merging from first and second datasets. The claim does not include limitations that are “significantly more” than the abstract idea because the claims do not include an improvement to another technology or technical field, an improvement to the functioning of the computer itself, or meaningful limitations beyond generally linking the use of an abstract idea to a particular technological environment. The claim fails to recite specific limitations (or a combination of limitations) that are NOT well-understood, routine, and conventional. The steps of: allocat(ing) a first dataset…, allocat(ing) the second data set …; are conventional steps describe an abstract idea, they do not impose any meaningful limits on practicing the abstract idea and thus do not add significantly more to the claimed invention. The claim does not include any structure and/or a series of steps as how to merge the first and second data sets within the partition; how to replacing the iteration of the value. Claims 11 and 18 include features that are similar to claim 1. The claims further recite: a processor, a communication interface, a memory, a non-transitory computer-readable medium; they are generic computer elements. The generically recited computer elements do not add a meaningful limitation to the abstract idea. Thus, the limitations do not impose any meaningful limits on practicing the abstract idea and thus do not add significantly more to the claimed invention.
Viewed as a whole, the claim elements do not provide meaningful limitations to transform the abstract idea into a patent eligible application of the abstract idea such that the claim amounts to significantly more than the abstract idea itself. Therefore, the claim is rejected under 35 U.S.C. 101 as being directed to non-statutory subject matter.
See also, MPEP 2106.04(a)(2).III.C
“Performing a mental process on a generic computer. An example of a case identifying a mental process performed on a generic computer as an abstract idea is Voter Verified, Inc. v. Election Systems & Software, LLC, 887 F.3d 1376, 1385, 126 USPQ2d 1498, 1504 (Fed. Cir. 2018). In this case, the Federal Circuit relied upon the specification in explaining that the claimed steps of voting, verifying the vote, and submitting the vote for tabulation are "human cognitive actions" that humans have performed for hundreds of years. The claims therefore recited an abstract idea, despite the fact that the claimed voting steps were performed on a computer. 887 F.3d at 1385, 126 USPQ2d at 1504. Another example is Versata, in which the patentee claimed a system and method for determining a price of a product offered to a purchasing organization that was implemented using general purpose computer hardware. 793 F.3d at 1312-13, 1331, 115 USPQ2d at 1685, 1699. The Federal Circuit acknowledged that the claims were performed on a generic computer, but still described the claims as "directed to the abstract idea of determining a price, using organizational and product group hierarchies, in the same way that the claims in Alice were directed to the abstract idea of intermediated settlement, and the claims in Bilski were directed to the abstract idea of risk hedging." 793 F.3d at 1333; 115 USPQ2d at 1700-01..”
See MPEP 2111 for when and to what extent the specification can be read into claims.
For the above reasons, the Examiner maintains the rejections to claims under 35 U.S.C 101.
Claim Rejections - 35 USC § 103
Applicant's arguments filed 07/28/2025 have been fully considered (i.e. Johnas/Iyer does not disclose that the data records include values that indicate pricing data or discount data). New ground of rejection is provided in view of the argument/amendment.
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 1-3, 5, 7-13 and 16-23 and 25 are rejected under 35 U.S.C. §101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more. Claims 1-3, 5, 7-13 and 16-23 and 25 are directed to the abstract idea for transmitting and storing data. The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Independent claims 1, 11 and 18
Step 1: Claim 1 recites “A computerized method …”; the claim recites a series of steps and therefore is process. Claim 11 recites “A system …”; therefore the claim is a machines. Claim 18 recites “A computer-readable medium …”; therefore, the claim is a manufacture.
Independent claims 1 and 11 recite limitations of:
allocat(ing) (a mental step that using a generic tool) a first dataset in a database into a partition of an intermediate storage, wherein the intermediate storage stores data in the partition based on a first identifier of the partition, and wherein allocating the first dataset into the partition comprises identifying a second identifier of the first dataset as matching the first identifier of the partition, the first dataset including a first iteration of a value, wherein the value indicates at least one of pricing data, or discount data associated with the first identifier;
allocat(ing) (a mental step that using a generic tool) a second dataset in the database into the partition, including identifying a third identifier of the second dataset as matching the first identifier of the partition, the second dataset including a second iteration of the value, that is different than the first iteration of the value;
merg(ing) (insignificant extra-solution data gathering) the first and second datasets within the partition, including update(ing) (insignificant extra-solution activity) the partition by converting (insignificant extra-solution activity) the first iteration of the value with the second iteration of the value; and
transmit(ting) (insignificant extra-solution activity) the updated partition to an external storage.
Step 2A Prong One: The limitations of allocat(ing) a first dataset …; allocat(ing) a second dataset; are processes that, under its broadest reasonable interpretation, covers performance of the limitation in the mind, but for the recitation of generic computer components. That is , other than reciting, a processor, a communication interface, a memory (in claim 11); nothing in the claim elements preclude the step from practically being performed in a human mind or with the aid of pen and paper. Note that the limitations are done by the generically recited computer components under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas (concepts performed in the human mind including an observation, evaluation, judgment, and opinion).
Step 2A Prong Two: The judicial exception is not integrated into a practical application.
Claims 1 and 11 recite the additional elements, merg(ing) …, update(ing) …, convert(ing) …, and transmit(ting); the limitations amount to gathering and analyzing data (MPEP 2106.05(g); …; the limitations are a mere generic gathering/collecting, analyzing and transmission data for storing (MPEP 2106.05(g)). Further, these additional limitations are recited as being performed by “a processor” , “a communication interface” , and “a memory”, recited in claim 11, provide nothing more than mere instructions to implement an abstract idea on a generic computer. See MPEP 2106.05(f). MPEP 2106.05(f) provides the following considerations for determining whether a claim simply recites a judicial exception with the words “apply it” (or an equivalent), such as mere instructions to implement an abstract idea on a computer: (1) whether the claim recites only the idea of a solution or outcome i.e., the claim fails to recite details of how a solution to a problem is accomplished; (2) whether the claim invokes computers or other machinery merely as a tool to perform an existing process; and (3) the particularity or generality of the application of the judicial exception.
Step 2B: The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. The limitations merg(ing) …, update(ing) …, convert(ing) …, and transmit(ting); are recognized by the courts as well-understood, routine , and conventional activities when they are claimed in a merely generic manner (see MPEP 2106.05(d)(II)(iv) gathering, analyzing and transmission data for storing, Versata Dev. Group Inc....
As explained with respect to Step 2A, Prong Two, the additional elements performing by “a processor” , “a communication interface”, and “a memory” are at best mere instructions to “apply” the abstract ideas, which cannot provide an inventive concept. See MPEP 2106.05(f).
Generally linking the use of the judicial exception to a particular technological environment or field of use, e.g., a claim describing how the abstract idea of hedging could be used in the commodities and energy markets, as discussed in Bilski v. Kappos, 561 U.S. 593, 595, 95 USPQ2d 1001, 1010 (2010) or a claim limiting the use of a mathematical formula to the petrochemical and oil-refining fields, as discussed in Parker v. Flook, 437 U.S. 584, 588-90, 198 USPQ 193, 197-98 (1978) (MPEP § 2106.05(h)).
Since, claims 1 and 11 are directed to abstract ideas; thus, the claims are not patent eligible.
Independent claim 18 recites limitations of:
allocate (a mental step that using a generic tool) a first dataset in a database into a partition of an intermediate storage, wherein the intermediate storage stores data in the partition based on a first identifier of the partition, and wherein allocating the first dataset into the partition comprises identifying a second identifier of the first dataset as matching the first identifier of the partition, the first dataset including a first iteration of a value, that indicates discount data associated with the first identifier of the partition;
allocate (a mental step that using a generic tool) a second dataset in the database into the partition, including identifying a third identifier of the second dataset as matching the first identifier of the partition utilizing an algorithm configured to derive a mapping of identifiers to partitions, the second dataset including a second iteration of the value that is different than the first iteration of the value;
merge (insignificant extra-solution data gathering) the first and second datasets within the partition, including performing (insignificant extra-solution activity) an UPSERT operation that updates the partition by converting the value from the first iteration of the value to the second iteration of the value;
receive (insignificant extra-solution activity) a query request, the query request including a request to return data and the first identifier of the partition;
allocate (a mental step that using a generic tool) the updated partition by the first identifier of the partition; and
transmit (insignificant extra-solution activity), via a communication interface, a portion of the updated partition related to the received query request.
Step 2A Prong One: The limitations of: allocate a first dataset …; allocate a second dataset; and bucket the updated partition …; are processes that, under its broadest reasonable interpretation, covers performance of the limitation in the mind, but for the recitation of generic computer components. That is , other than reciting, a processor, a computer-readable medium; nothing in the claim elements preclude the step from practically being performed in a human mind or with the aid of pen and paper. Note that the limitations are done by the generically recited computer components under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas (concepts performed in the human mind including an observation, evaluation, judgment, and opinion).
Step 2A Prong Two: The judicial exception is not integrated into a practical application.
Claim 18 recites the additional elements, merge …, receive a request …, and transmit …; the limitations amount to gathering/collecting and transmission data (MPEP 2106.05(g)). Further, these additional limitations are recited as being performed by a processor, a computer-readable medium, provide nothing more than mere instructions to implement an abstract idea on a generic computer. See MPEP 2106.05(f). MPEP 2106.05(f) provides the following considerations for determining whether a claim simply recites a judicial exception with the words “apply it” (or an equivalent), such as mere instructions to implement an abstract idea on a computer: (1) whether the claim recites only the idea of a solution or outcome i.e., the claim fails to recite details of how a solution to a problem is accomplished; (2) whether the claim invokes computers or other machinery merely as a tool to perform an existing process; and (3) the particularity or generality of the application of the judicial exception.
Step 2B: The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. The limitations: merge …, receive a request …, and transmit … the updated …, are recognized by the courts as well-understood, routine , and conventional activities when they are claimed in a merely generic manner (see MPEP 2106.05(d)(II)(iv) gathering/collecting and transmission data, Versata Dev. Group Inc....
As explained with respect to Step 2A, Prong Two, the additional elements performing by a processor, a computer-readable medium are at best mere instructions to “apply” the abstract ideas, which cannot provide an inventive concept. See MPEP 2106.05(f).
Generally linking the use of the judicial exception to a particular technological environment or field of use, e.g., a claim describing how the abstract idea of hedging could be used in the commodities and energy markets, as discussed in Bilski v. Kappos, 561 U.S. 593, 595, 95 USPQ2d 1001, 1010 (2010) or a claim limiting the use of a mathematical formula to the petrochemical and oil-refining fields, as discussed in Parker v. Flook, 437 U.S. 584, 588-90, 198 USPQ 193, 197-98 (1978) (MPEP § 2106.05(h)).
Since, claim 18 is directed to abstract ideas; thus, the claim is not patent eligible.
Claims 2-3, 5, 7-10, 12-13, 16-17 and 19-23 and 25
The limitations as recited in claims 2-3, 5, 7-10, 12-13, 16-17 and 19-23 and 25 are simply describe the concepts of transmitting and storing data. The claims do not include additional element(s) that is sufficient to amount to significantly more than the judicial exceptions. The claims cannot provide an inventive concept. Therefore, claims 2-3, 5, 7-10, 12-13, 16-17 and 19-23 and 25 are directed to abstract ideas and are not patent eligible. Analysis of the dependent claims are shown below.
Dependent claim 2 recites the limitations, receiving a query request; and outputting a portion of the updated partition based on the received query request; the limitation is insignificant extra-solution activity of mere generic gathering and analyzing data (MPEP 2106.05(g)) and which is well understood routine conventional (see MPEP 2106.05(d)).
Dependent claim 3 recites the limitations, wherein outputting the portion of the updated partition further comprises: allocating the updated partition by the value; is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind, then it falls within the “Mental Processes” grouping of abstract ideas (concepts performed in the human mind including an observation, evaluation, judgment, and opinion); and outputting a portion of the updated partition related to the received query request; the limitation amounts to transmitting analyzed data (MPEP 2106.05(g)) which is well understood routine conventional (see MPEP 2106.05(d)).
Dependent claim 5 recites the limitation, wherein updating the partition comprises performing an UPSERT operation; is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind, then it falls within the “Mental Processes” grouping of abstract ideas (concepts performed in the human mind including an observation, evaluation, judgment, and opinion).
Dependent claim 7 recites the limitations, wherein the partition is a fist partition; is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind, then it falls within the “Mental Processes” grouping of abstract ideas (concepts performed in the human mind including an observation, evaluation, judgment, and opinion), the method further comprising:
receiving a third dataset, the third dataset including a third iteration of the value; the limitation amounts to data gathering (MPEP 2106.05(g) which is well understood routine conventional (see MPEP 2106.05(d)); determining a fourth identifier or the third dataset does not match the first identifier of the partition; and allocating the third dataset into a second partition; are processes that, under its broadest reasonable interpretation, covers performance of the limitation in the mind, then it falls within the “Mental Processes” grouping of abstract ideas (concepts performed in the human mind including an observation, evaluation, judgment, and opinion).
Dependent claim 8 recites the limitations, receiving a third dataset, the third dataset including a third iteration of the value; the limitation amounts to data gathering (MPEP 2106.05(g) which is well understood routine conventional (see MPEP 2106.05(d)); determining a fourth identifier of the third dataset matches the first identifier of the partition; and allocating the third dataset into the first partition; are processes that, under its broadest reasonable interpretation, covers performance of the limitation in the mind, then it falls within the “Mental Processes” grouping of abstract ideas (concepts performed in the human mind including an observation, evaluation, judgment, and opinion); and updating the partition to include the third iteration of the value; the limitation amounts to data analyzing (MPEP 2106.05(g) which is well understood routine conventional (see MPEP 2106.05(d)).
Dependent claim 9 recites the limitation, wherein the value indicates address data associated with the first identifier; this limitation is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind, then it falls within the “Mental Processes” grouping of abstract ideas (concepts performed in the human mind including an observation, evaluation, judgment, and opinion).
Dependent claim 10 recites the limitations, wherein the partition is implemented in a distributed database storage system; this limitation is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind, then it falls within the “Mental Processes” grouping of abstract ideas (concepts performed in the human mind including an observation, evaluation, judgment, and opinion).
Claims 12-13 and 16-17 recite a system comprising steps are similar to claims 2-3, 5 and 7-8. Therefore, claims 12-13 and 16-17 are rejected by the same reasons as discussed in claims 2-3, 5 and 7-8.
Claims 19-20 recite a computer-readable medium comprising steps are similar to claims 7-8. Therefore, claims 19-20 are rejected by the same reasons as discussed in claims 7-8.
Dependent claims 21 recites the limitation, wherein identifying the third identifier of the second dataset as matching the first identifier of the partition comprises accessing a record of a mapping of identifiers to partitions; is a Mathematical concept. The courts have found that mathematical relationships fall within the judicial exceptions, grouping of abstract ideas, see MPEP § 2106.04(a)).
Dependent claim 22 recites the limitation, wherein transmitting the updated partition to the external storage comprises materializing a state of the merged first and second datasets of the updated partition; the limitation amounts to data analyzing (MPEP 2106.05(g) which is well understood routine conventional (see MPEP 2106.05(d)).
Dependent claim 23 recites the limitation, converting the updated partition to a read-optimized copy; the limitation amounts to data analyzing (MPEP 2106.05(g) which is well understood routine conventional (see MPEP 2106.05(d)).
Dependent claim 25 recites the limitation, wherein the value indicates shipping data associated with the first identifier; this limitation is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind, then it falls within the “Mental Processes” grouping of abstract ideas (concepts performed in the human mind including an observation, evaluation, judgment, and opinion).
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.
Claims 1-3, 5, 7-13 and 16-23 and 25 are rejected under 35 U.S.C. 103 as being unpatentable over Johnas et al., US 2023/0205743 (hereinafter Johnas), in view of Carter et al., US 2009/0307153 (hereinafter Carter), and further in view of Iyer et al., US 2007/0192304 (hereinafter Iyer) .
Regarding claim 1, Johnas discloses, A computerized method, comprising:
allocating a first dataset in a database into a partition of an intermediate storage, wherein the intermediate storage stores data in the partition based on a first identifier of the partition (e.g., For each entity (e.g., the organization or an agency), the data management system may create a corresponding enterprise data model instance based on the enterprise data model schema for storing data received from that entity [as allocating a first dataset in a database]. Each enterprise data model instance may include data structures (e.g., tables, lists, etc.). For example, Table 402 {as first dataset] includes columns [as partitions], e.g. Name, Account Number and date [as first identifiers of the partition], Johnas: [0016], [0022] and Fig. 4), and wherein allocating the first dataset into the partition comprises identifying a second identifier of the first dataset as matching the first identifier of the partition (e.g. The data management system may analyze the data types to identify common data types (e.g., even though the names may be different for different entities). The data management system may then generate a set of data types for the enterprise data model schema that encompasses all of the data types associated with the different entities. The set of data types may have a uniform naming convention such that a data type having a data type name [as a second identifier] (e.g., “user_name”) may correspond to related data types [as matching the first identifier] associated with the different entities, which may have different names (e.g., “username,” “u_name,” “u_n,” etc.), Johnas: [0020]. The data management system may determine whether the data obtained is consistent with the data stored within the data management system by comparing the data against a corresponding data record in the corresponding enterprise data model instance, Johnas: [0023] and [0047]), the first dataset including a first iteration of a value, (e.g. Each data record [as a data set] may include a name of a person, a bank account number, and a date. Wherein, the columns’ names [as a first iteration], and values of the columns , e.g. a name of a person, a bank account number, and a date [as values of the columns’ name], Johnas: Fig. 4);
allocating a second dataset in the database into the partition, including identifying a third identifier of the second dataset as matching the first identifier of the partition, the second dataset including a second iteration of the value that is different than the first iteration of the value (e.g. data records 404 [as a second data set], from the data storage 114 [as a database], includes columns [as partitions], e.g. Name, Account Number and date [as identifiers of the partition]. Since the bank account number of data record 404 are different with the back account number of data record 402; therefore, the bank account number of record 404 has been interpreted as the second iteration of the second value is different than the first iteration of the value, e.g. account number, Johnas: [0084] and Fig. 4); and
merging the first and second datasets within the partition (e.g. Instead of viewing only the data from a single enterprise data model instance according to the data organization, a consolidated data view may generate the virtual and temporary data structure for presenting data that is merged from multiple enterprise data model instances, Johnas: [0032]).
transmitting the updated partition to an external storage (e.g. When an API request call for accessing data within the data storage is detected, the client application may monitor any API response generated by the entity server in response to the API request call. The client application may obtain the data included in the API request call and may transmit the data to the server [as external storage] of the data management system, Johnas: [0046]);
Johnas does not directly or explicitly disclose:
wherein the value indicates at least one of pricing data or discount data associated with the first identifier.
Carter teaches:
wherein the value indicates at least one of pricing data or discount data associated with the first identifier (e.g. In the example of FIG. 1, a 486/33 CPU is sold to Adam at a price of $40, a 486/50 CPU is sold to Adam at a price of $60 and a 486/66 CPU is sold to Adam at a price of $80. A 486/33 CPU is sold to Bob at a price of $42, a 486/50 CPU is sold to Bob at $58, and a 486/66 CPU is sold to Bob at $72. FIG. 2 shows a volume discount table that corresponds to the basic price table of FIG. 1. Thus, the price $40 would be reduced by a discount of 10% if Adam purchases 486/33 CPU's in volume. Thus, Adam can purchase each 486/33 CPU at a volume-discounted price of $40*(1-(10/100)), i.e. at $36, as compared with the original price $40. Similarly, a volume discount of 12% corresponds to the original price $60, and a volume discount of 14% corresponds to the original price of $80, and so forth, Carter: Figs. 1-2. Wherein, the customer Name, e.g. Adam, Bob, Charlei are interpreted as identifiers, and pricing data or discount data associated with the customer’s name as first identifier).
Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the invention to modify methods and systems for providing a data management system for storing, managing, and providing access to data from different sources as disclosed by Johnas to include A method and apparatus for pricing products in multi-level product and organizational groups as taught by Carter to provide flexibility in formulating a desired pricing system to store, maintain, and retrieve huge amounts of data.
Johnas in view of Carter does not directly or explicitly disclose:
updating the partition by converting the first iteration of the value to the second iteration of the value.
Iyer teaches:
updating the partition by converting the first iteration of the value to the second iteration of the value (e.g. An upsert of a data set corresponds to updating a destination data set [as updating the partition] with data from a source data set. Data present in the source data set that is not present in the destination data set is inserted into the destination data set. Values in the destination data set are replaced [as converting] with values from the source data set, Iyer: [0211]-[0212], abstract, Figs. 11-12).
Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the invention to modify methods and systems for providing a data management system for storing, managing, and providing access to data from different sources as disclosed by Johnas in view of Carter to include upsert operation as taught by Iyer to provide quickly and efficiently data retrieval.
Regarding claim 2, Johnas further discloses:
receiving a query request (e.g. entity servers of the entities may receive API calls (e.g., from their users such as merchants or individual users, etc.) for accessing data, Johnas: [0046]); and
outputting a portion of the updated partition based on the received query request (e.g. data views can be generated to provide different virtual views of the data for different data consumers. A data view, which may also be referred to as a data set or a data mart is a virtual and temporary data structure for visualization of at least a portion of the data stored in a data repository. Data views can be useful for providing unique views or presentations of data based on different focuses, Johnas: [0031] and [0046]).
Regarding claim 3, Johnas further discloses, wherein outputting the portion of the updated partition further comprises:
allocating the updated partition by the value (e.g. For example, an account-focused data view may be generated that compiles data in an accounting-focused organization (e.g., having a focus on monetary amounts being transacted instead of other attributes, etc.) while a risk-focused data view may be generated that compiles data in a risk-focused organization (e.g., having a focus on risk attributes, such as transaction locations, transaction frequencies, etc. instead of other attributes, etc.). Thus, each data view may include a different subset of data types from the enterprise data model schema and/or a different organization of the data types than the actual organization in the enterprise data model instances, Johnas: [0031]); and
outputting a portion of the updated partition related to the received query request (e.g. data views can be generated to provide different virtual views of the data for different data consumers. A data view, which may also be referred to as a data set or a data mart is a virtual and temporary data structure for visualization of at least a portion of the data stored in a data repository. Data views can be useful for providing unique views or presentations of data based on different focuses, Johnas: [0031] and [0046]).
Regarding claim 5, Iyer further teaches, wherein updating the partition comprises performing an UPSERT operation (e.g. Fig. 11 is a flowchart of the upsert record operation. In Query Destination Data Set for Record Matching Instance step 1110, the data set to be updated is queried using user keys to determine whether the record is present in the data set. In Record Present decision point 1120, a determination is made whether the record is present. If the record is present, the record is updated in Update Record step 1130. If the record is not present, in Insert Record step 1140, a record is inserted containing data corresponding to the input instance data, Iyer: [0211], and Fig. 11).
Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the invention to modify methods and systems for providing a data management system for storing, managing, and providing access to data from different sources as disclosed by Johnas to include upsert operation as taught by Iyer to provide quickly and efficiently data retrieval.
Regarding claim 7, Johnas further discloses, wherein the partition is a first partition, the method further comprising:
receiving a third dataset, the third dataset including a third iteration of the second value (e.g. Whenever information indicates new data or changes of data [as third dataset] from an entity is received by the server, Johnas: [0027]);
determining a fourth identifier of the third dataset does not match the first identifier of the partition (e.g. the server may be configured to transform the data [as determining a fourth identifier] (or the changes of data) and map the data (or the changes of data) to a data record [as match the first identifier] in a corresponding enterprise data model instance corresponding to the entity, Johnas: [0027]); and
allocating the third dataset into a second partition (e.g. when a new entity joins the data management system (e.g., a new adjacency becomes affiliated to the organization, etc.), the data management system may create a new enterprise data model instance [as second partition]for the new entity, Johnas: [0028]).
Regarding claim 8, Johnas further discloses:
receiving a third dataset, the third dataset including a third iteration of the value (e.g. Whenever information indicates new data or changes of data [as third dataset] from an entity is received by the server, Johnas: [0027]);
determining a fourth identifier of the third dataset matches the first identifier of the partition (e.g. the server may be configured to transform the data [as determining a fourth identifier] (or the changes of data) and map the data (or the changes of data) to a data record [as match the first identifier] in a corresponding enterprise data model instance corresponding to the entity, Johnas: [0027]); and
allocating the third dataset into the first partition (e.g. The data management application may obtain any data changes, and add the data changes to a batch data structure, Johnas: [0070]); and
updating the partition to include the third value and the third iteration of the value (e.g. After the initial ingestion of data from the entities, the data management system may continue to update the data in the enterprise data model instances based on updates to the data stored in the data storages associated with the entities, Johnas: [0023]).
Regarding claim 9, Carter further teaches, wherein the key of the partition is an identifier and the value indicates at least one of pricing data, discount data, shipping data, or address data associated with the first identifier (e.g. In the example of FIG. 1, a 486/33 CPU is sold to Adam at a price of $40, a 486/50 CPU is sold to Adam at a price of $60 and a 486/66 CPU is sold to Adam at a price of $80. A 486/33 CPU is sold to Bob at a price of $42, a 486/50 CPU is sold to Bob at $58, and a 486/66 CPU is sold to Bob at $72. FIG. 2 shows a volume discount table that corresponds to the basic price table of FIG. 1. Thus, the price $40 would be reduced by a discount of 10% if Adam purchases 486/33 CPU's in volume. Thus, Adam can purchase each 486/33 CPU at a volume-discounted price of $40*(1-(10/100)), i.e. at $36, as compared with the original price $40. Similarly, a volume discount of 12% corresponds to the original price $60, and a volume discount of 14% corresponds to the original price of $80, and so forth, Carter: Figs. 1-2. Wherein, the customer Name, e.g. Adam, Bob, Charlei are interpreted as identifiers, and pricing data or discount data associated with the customer’s name as first identifier).
Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the invention to modify methods and systems for providing a data management system for storing, managing, and providing access to data from different sources as disclosed by Johnas to include A method and apparatus for pricing products in multi-level product and organizational groups as taught by Carter to provide flexibility in formulating a desired pricing system to store, maintain, and retrieve huge amounts of data.
Regarding claim 10, Johnas further discloses, wherein the partition is implemented in a distributed database storage system (e.g. data storage system in Fig. 2, Johnas: [0051] and Fig. 2).
Regarding claim 11, Johnas discloses, A system, comprising:
a processor (e.g. A processor 1014, which can be a micro-controller, digital signal processor (DSP), or other processing component, Johnas: [0117] and Fig. 10);
a communication interface (e.g. network interface 1020 transmits and receives signals between the computer system 1000 and other devices, Johnas: [0117] and Fig. 10); and
a memory (e.g. a system memory component 1010, Johnas: [0118] and Fig. 10) storing instructions that, when executed by the processor, cause the processor to:
allocate a first dataset in a database into a partition of an intermediate storage, wherein the intermediate storage stores data in the partition based on a first identifier of the partition (e.g. For each entity (e.g., the organization or an adjacency), the data management system may create a corresponding enterprise data model instance based on the enterprise data model schema for storing data received from that entity [as allocate a first dataset in a database]. Each enterprise data model instance may include data structures (e.g., tables, lists, etc.). For example, Table 402 {as first dataset] includes columns [as partitions], e.g. Name, Account Number and date [as identifiers of the partitions], Johnas: [0016], [0022] and Fig. 4), and wherein allocating the first dataset into the partition comprises identifying a second identifier of the first dataset as matching the first identifier of the partition (e.g. The data management system may analyze the data types to identify common data types (e.g., even though the names may be different for different entities). The data management system may then generate a set of data types for the enterprise data model schema that encompasses all of the data types associated with the different entities. The set of data types may have a uniform naming convention such that a data type having a data type name [as a key] (e.g., “user_name”) may correspond to related data types [as matching the identifier] associated with the different entities, which may have different names (e.g., “username,” “u_name,” “u_n,” etc.), Johnas: [0020]. The data management system may determine whether the data obtained is consistent with the data stored within the data management system by comparing the data against a corresponding data record in the corresponding enterprise data model instance, Johnas: [0023] and [0047]),
allocate a second dataset in the database into the partition, including identifying a third identifier of the second dataset as matching the first identifier of the partition utilizing a record of a mapping of identifiers to partitions, the second dataset including a second iteration of the value that is different than the first iteration of the value (e.g. data records 404 [as a second data set], from the data storage 114 [as a database], includes columns [as partitions], e.g. Name, Account Number and date [as identifiers of the partition]. Since the bank account number of data record 404 are different with the back account number of data record 402; therefore, the bank account number of record 404 has been interpreted as the second iteration of the second value is different than the first iteration of the value, e.g. account number, Johnas: [0084] and Fig. 4);
merging the first and second datasets within the partition (e.g. Instead of viewing only the data from a single enterprise data model instance according to the data organization, a consolidated data view may generate the virtual and temporary data structure for presenting data that is merged from multiple enterprise data model instances, Johnas: [0032]).
transmit, via a communication interface, the updated partition to an external storage (e.g. When an API request call for accessing data within the data storage is detected, the client application may monitor any API response generated by the entity server in response to the API request call. The client application may obtain the data included in the API request call and may transmit the data to the server [as external storage] of the data management system, Johnas: [0046]);
Johnas does not directly or explicitly disclose:
the first dataset including a first iteration of a value that indicates pricing data associated with the first identifier of the partition.
Carter teaches:
the first dataset including a first iteration of a value that indicates pricing data associated with the first identifier of the partition (e.g. In the example of FIG. 1, a 486/33 CPU is sold to Adam at a price of $40, a 486/50 CPU is sold to Adam at a price of $60 and a 486/66 CPU is sold to Adam at a price of $80. A 486/33 CPU is sold to Bob at a price of $42, a 486/50 CPU is sold to Bob at $58, and a 486/66 CPU is sold to Bob at $72, Carter: Fig. 1. Wherein, the Who are interpreted as identifiers, and pricing data associated with the customer’s names as first, second or third identifier).
Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the invention to modify methods and systems for providing a data management system for storing, managing, and providing access to data from different sources as disclosed by Johnas to include A method and apparatus for pricing products in multi-level product and organizational groups as taught by Carter to provide flexibility in formulating a desired pricing system to store, maintain, and retrieve huge amounts of data.
Johnas in view of Carter does not directly or explicitly disclose:
performing an UPSERT operation that updates the partition by changing the value from the first iteration of the value to the second iteration of the value.
Iyer teaches:
performing an UPSERT operation that updates the partition by changing the value from the first iteration of the value to the second iteration of the value (e.g. The SQL adapter business service optimizes operations to update data in the data sets by combining operations when possible, e.g. upsert record operation to update/insert record in the data set [as UPSERT operation](Fig. 11),and by using result sets from executing previous SQL statements to construct subsequent SQL statements. Values in the destination data set are replaced with values from the source data set. FIG. 11 is a flowchart of the upsert record operation to update or insert record in the data set; thus, the upsert record operation in Fig. 11 has been understood as An UPSERT operation. An upsert of a data set corresponds to updating a destination data set [as updating the partition] with data from a source data set. Data present in the source data set that is not present in the destination data set is inserted into the destination data set. Values in the destination data set are replaced with values from the source data set, Iyer: [0211]-[0212], abstract, Figs. 11-12, Iyer: [0211], abstract, Figs. 11-12).
Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the invention to modify methods and systems for providing a data management system for storing, managing, and providing access to data from different sources as disclosed by Johnas to include upsert operation as taught by Iyer to provide quickly and efficiently data retrieval.
Claims 12-14 and 16-17 recite a system comprising steps are similar to claims 2-3, 5 and 7-8. Therefore, claims 12-14 and 16-17 are rejected by the same reasons as discussed in claims 2-3, 5 and 7-8.
Regarding claim 18, Johnas discloses, A non-transitory computer-readable medium (e.g. a computer readable medium, Johnas: [0119]-[0120] ) storing instructions that, when executed by a processor, cause the processor to:
allocate a first dataset in a database into a partition of an intermediate storage, wherein the intermediate storage stores data in the partition based on a first identifier of the partition (e.g. For each entity (e.g., the organization or an adjacency), the data management system may create a corresponding enterprise data model instance based on the enterprise data model schema for storing data received from that entity [as bucketing a first dataset in a database]. Each enterprise data model instance may include data structures (e.g., tables, lists, etc.). For example, Table 402 {as first dataset] includes columns [as partitions], e.g. Name, Account Number and date [as keys of the partition], Johnas: [0016], [0022] and Fig. 4), and wherein allocating the first dataset into the partition comprises identifying a second identifier of the first dataset as matching the first identifier of the partition (e.g. The data management system may analyze the data types to identify common data types (e.g., even though the names may be different for different entities). The data management system may then generate a set of data types for the enterprise data model schema that encompasses all of the data types associated with the different entities. The set of data types may have a uniform naming convention such that a data type having a data type name [as a first identifier] (e.g., “user_name”) may correspond to related data types [as matching the first identifier] associated with the different entities, which may have different names (e.g., “username,” “u_name,” “u_n,” etc.), Johnas: [0020]. The data management system may determine whether the data obtained is consistent with the data stored within the data management system by comparing the data against a corresponding data record in the corresponding enterprise data model instance, Johnas: [0023] and [0047]),
allocate a second dataset in the database into the partition, including identifying a third identifier of the second dataset as matching the first identifier of the partition utilizing an algorithm configured to derive a mapping of identifiers to partitions, the second data set including a second iteration of the value that is different than the first iteration of the value (e.g. data records 404 [as a second data set], from the data storage 114 [as a database], includes columns [as partitions], e.g. Name, Account Number and date [as identifiers of the partition]. Since the bank account number of data record 404 are different with the back account number of data record 402; therefore, the bank account number of record 404 has been interpreted as the second iteration of the second value is different than the first iteration of the value, e.g. account number, Johnas: [0084] and Fig. 4);
merge the first and second datasets within the partition (e.g. Instead of viewing only the data from a single enterprise data model instance according to the data organization, a consolidated data view may generate the virtual and temporary data structure for presenting data that is merged from multiple enterprise data model instances, Johnas: [0032]).
receive a query request, the query request including a request to return data and the key of the partition (e.g. entity servers of the entities may receive API calls (e.g., from their users such as merchants or individual users, etc.) for accessing data, Johnas: [0046]);
allocate the updated partition by the first identifier of the partition (e.g. the data management system may compile, for an accounting-focused [as the key] consolidated data view, transaction data having an accounting focus from different enterprise data model instances (e.g., transactions conducted with different entities). The data management system may then present the data organized according to the accounting-focused consolidated data view on a user device, Johnas: [0032]); and
transmit, via a communication interface, a portion of the updated partition related to the received query request (e.g. When an API request call for accessing data within the data storage is detected, the client application may monitor any API response generated by the entity server in response to the API request call. The client application may obtain the data included in the API request call and may transmit the data to the server of the data management system, Johnas: [0084] and Fig. 4).
Johnas does not directly or explicitly disclose:
the first dataset including a first iteration of a value that indicates discount data associated with the first identifier of the partition;
Carter teaches:
the first dataset including a first iteration of a value that indicates discount data associated with the first identifier of the partition (e.g. In the example of FIG. 1, a 486/33 CPU is sold to Adam at a price of $40, a 486/50 CPU is sold to Adam at a price of $60 and a 486/66 CPU is sold to Adam at a price of $80. A 486/33 CPU is sold to Bob at a price of $42, a 486/50 CPU is sold to Bob at $58, and a 486/66 CPU is sold to Bob at $72, Carter: Fig. 1. Wherein, the Who are interpreted as identifiers, and pricing data associated with the customer’s names as first, second or third identifier).
Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the invention to modify methods and systems for providing a data management system for storing, managing, and providing access to data from different sources as disclosed by Johnas to include A method and apparatus for pricing products in multi-level product and organizational groups as taught by Carter to provide flexibility in formulating a desired pricing system to store, maintain, and retrieve huge amounts of data.
Johnas in view of Carter does not directly or explicitly disclose:
performing an UPSERT operation that updates the partition by changing the value from the first iteration of the value to the second iteration of the value.
Iyer teaches:
performing an UPSERT operation that updates the partition by changing the value from the first iteration of the value to the second iteration of the value (e.g. The SQL adapter business service optimizes operations to update data in the data sets by combining operations when possible, e.g. upsert record operation to update/insert record in the data set [as UPSERT operation](Fig. 11), and by using result sets from executing previous SQL statements to construct subsequent SQL statements. Values in the destination data set are replaced with values from the source data set. FIG. 11 is a flowchart of the upsert record operation to update or insert record in the data set; thus, the upsert record operation in Fig. 11 has been understood as An UPSERT operation. An upsert of a data set corresponds to updating a destination data set [as updating the partition] with data from a source data set. Data present in the source data set that is not present in the destination data set is inserted into the destination data set. Values in the destination data set are replaced with values from the source data set, Iyer: [0211]-[0212], abstract, Figs. 11-12).
Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the invention to modify methods and systems for providing a data management system for storing, managing, and providing access to data from different sources as disclosed by Johnas in view of Carter to include upsert operation as taught by Iyer to provide quickly and efficiently data retrieval.
Claims 19-20 recite a computer-readable medium comprising steps are similar to claims 7-8. Therefore, claims 19-20 are rejected by the same reasons as discussed in claims 7-8.
Regarding claim 21, Johnas further discloses, wherein identifying the third identifier of the second dataset as matching the first identifier of the partition comprises accessing a record of a mapping of identifiers to partitions (e.g. After the initial ingestion of data from the entities, the data management system may continue to update the data in the enterprise data model instances based on updates to the data stored in the data storages associated with the entities. The data management system may determine whether the data obtained is consistent with the data stored within the data management system by comparing the data against a corresponding data record in the corresponding enterprise data model instance, Johnas: [0023] and [0047]).
Regarding claim 22, Johnas further discloses, wherein transmitting the updated partition to the external storage comprises materializing a state of the merged first and second datasets of the updated partition (e.g. Instead of viewing only the data from a single enterprise data model instance according to the data organization, a consolidated data view may generate the virtual and temporary data structure for presenting data that is merged from multiple enterprise data model instances, Johnas: [0032]. The data management application 112 may then encrypt the entire data records 402 or re-encrypt only the bank account numbers in the data records 402 before transmitting the data records 402 to the data management module 116, , Johnas: [0084] and Fig. 4).
Regarding claim 23, Johnas further discloses, converting the updated partition to a read-optimized copy (e.g. The transformation may also include converting the data to a common unit, Johnas: [0065]).
Regarding claim, Carter further teaches, wherein the value indicates shipping data associated with the first identifier (e.g. one or more tables to apply various adjustments to the basic price of a particular product for a prospective purchaser [as identifiers], e.g. customer/who. These adjustments can be, for example, applicable state and local taxes, actual shipping charges, currency conversions, and various discounts, Carter: [0053] ).
Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the invention to modify methods and systems for providing a data management system for storing, managing, and providing access to data from different sources as disclosed by Johnas to include A method and apparatus for pricing products in multi-level product and organizational groups as taught by Carter to provide flexibility in formulating a desired pricing system to store, maintain, and retrieve huge amounts of data.
Conclusion
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 CECILE H VO whose telephone number is (571)270-3031. The examiner can normally be reached Mon-Fri (9AM-5PM).
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/CECILE H VO/Examiner, Art Unit 2153 9/29/2025
/KAVITA STANLEY/Supervisory Patent Examiner, Art Unit 2153
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