DETAILED ACTION
1. Claims 1-20 are pending in this application.
Notice of Pre-AIA or AIA Status
2. The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
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 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.
Information Disclosure Statement
3. The information disclosure statement filed 01/06/2026 is in compliance with the provisions of 37 CFR 1.97, 1.98 and MPEP § 609. It has been placed in the application file and the information referred to therein has been considered as to the merits.
Response to Arguments
4. Applicant's arguments, filed on 03/05/2026, with respect to the rejection of claims 1-20 under 35 U.S.C. §101 an abstract idea (mental process) (Applicant’s arguments, pages 9-12), have been fully considered but are not persuasive. Respectfully, the examiner disagrees, see the clarification below.
Applicant argues that “This is more than generic, and solves the cloud storage specific problem of preventing an excessively long wait and improving the accuracy of data query results fed back to the client (See e.g. paragraph 88).” However, the claims do not describe any steps showing how such an accomplishment is performed in detail. The Applicant also did not clearly define what the cloud storage is. Simply reciting in the specification that the invention solves the cloud storage specific problem of preventing an excessively long wait and improving the accuracy of data query results fed back to the client is insufficient. A simple idea for a solution does not integrate an abstract idea into practical application or improve any technology or technological field. See § MPEP 2106.05(f)(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.”.
Applicant argues that “Applicant respectfully submits that the Office fails to consider Applicant's claims as a whole and that the Office is using an improper standard for determining patent eligibility.” The Examiner disagrees. The Examiner uses the 2019 Revised Patent Subject Matter Eligibility Guidance (2019 PEG) to perform the rejection analysis.
Applicant argues that “Obtaining, by one or more computing clusters of the cloud system and based on N second messages, data query results specifically relates to computer data storage-humans cannot mentally perform computer storage.” It is noted that nor do the courts distinguish between claims that recite mental processes performed by humans and claims that recite mental processes performed on a computer, see MPEP § 2106.04(a)(2)(III).
Applicant argues that “As discussed in MPEP § 2106.04(d), enumerated improvements in the operation of a machine, including improvements in computing systems or a technical field are not directed to a judicial exception.” However, the claims and the specification do not reflect the similar improvements enumerated in MPEP § 2106.04(d). The claims should reflect the improvements the applicant claims to have accomplished, see MPEP § 2106.05(a) – “During examination, the examiner should analyze the "improvements" consideration by evaluating the specification and the claims to ensure that a technical explanation of the asserted improvement is present in the specification, and that the claim reflects the asserted improvement.”.
Applicant argues that “The claims are directed to a practical application designed to improve cloud data storage by reducing delay in obtaining data query results and improving accuracy.” However, the claims do not describe such improvement, see MPEP § 2106.05(a).
Applicant argues that “The claimed process performs unconventional operations, i.e. obtaining, by one or more computing clusters of the cloud service system and based on the N second messages, data query results, wherein obtaining the data query results comprises respectively sending the N second messages to N computing clusters. The N computing clusters are in one-to-one correspondence with the N second messages, and the N computing clusters obtain data from a storage node based on the N second messages to generate the data query results.” However, the limitations mentioned herein are either simple instructions to accomplish the abstract ideas or considered by the courts as a method of simple gathering data, see the rejection below for more details.
For all the reasons above, the 35 U.S.C. § 101 abstract idea (mental process) rejection is upheld.
Applicant's arguments, filed on 03/05/2026, with respect to the rejection of claims 1-20 under 35 U.S.C. §103 (Applicant' s arguments, pages 13-17), have been fully considered and are but are moot because the independent claims are amended and introduce new limitations that were not previously presented newly found prior art has been applied.
Claim Objections
5. Claims 1, 10 are 20 objected to because of the following informalities:
The claims recite the following limitation “obtaining, by one or more computing clusters of the cloud service system and based on the N second messages, data query results” later in the claims is also recited the following limitation “wherein the N computing clusters obtain data from a storage node based on the N second messages to generate the data query results” It is not clear if “generate the data query results” is the same or is referring to “obtaining, by one or more computing clusters of the cloud service system and based on the N second messages, data query results”. If they are the same or is referring to “obtaining, by one or more computing clusters of the cloud service system and based on the N second messages, data query results” should be recited as “obtaining the data query results”;” to keep the terms consistent throughout the claims.
The Examiner gave the best reasonable interpretation to the claims.
Appropriate correction is required.
Claim Rejections - 35 USC § 101
6. 35 U.S.C. §101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 are rejected under 35 U.S.C. §101 because the claimed invention is directed to an abstract idea (Mental Process) without significantly more. The claims describe steps for data query.
The following is an analysis based on 2019 Revised Patent Subject Matter Eligibility Guidance (2019 PEG).
Step 1, Statutory Category?
Claims 1-9 are directed to a method.
Claims 10-19 are directed to a computing device cluster (system).
Claims 20 is directed to a computer program product.
Therefore, claims 1-20 fall into at least one of the four statutory categories.
Step 2A, Prong I: Judicial Exception Recited?
The examiner submits that the foregoing claim limitations constitute a “Mental Process”, as the claims cover performance of the limitations in the human mind, given the broadest reasonable interpretation.
As per independent claims 1, 10 and 20, the claims similarly recite the limitations of:
“generating, by a cloud service control plane of the cloud service system, N second messages based on the first data query request message” A human can mentally create a message based on data that the human has observed. For example, human sees data about dark clouds in the sky and creates the message: ‘It is going to rain’ mentally. There is nothing so complex in the limitation that could not be doing in the human mind.
“obtaining, by one or more computing clusters of the cloud service system and based on the N second messages, data query results” A human can mentally observe data and select, from the observed data, a piece that corresponds to one of a few possible messages as a result. For example, a human observes a traffic light data and selects the message 'Go' from the options of 'Stop,' 'Caution,' and 'Go.'" mentally. There is nothing so complex in the limitation that could not be doing in the human mind.
As per dependent claims 7 and 17, the claims similarly recite the limitation of:
“generating the first data query request message based on the third message, wherein the first data query request message is a structured query language (SQL) message.” A human sees something (observes data) and that observation prompts them to mentally formulate a question or a request for more information (elaborate a request for a message). For example, a human observes a closed store door and mentally elaborates the request: 'Is this business closed permanently or just for the holiday’?. There is nothing so complex in the limitation that could not be doing in the human mind.
Accordingly, claims 1-20 recite at least one abstract idea.
Step 2A, Prong II: Integrated into a Practical Application?
The claims recite the following additional limitations/elements:
As per independent claims 1, 10 and 20, the claims similarly recite the limitations of:
The claims also recite the additional limitations of “receiving, by a user interface of a cloud service system and from a client, a first data query request message; individually sending, by the user interface to the client and in an order of obtaining the data query results”, and the courts have recognized that receiving or transmitting data over a network, e.g., using the Internet to gather data, as well‐understood, routine, and conventional functions when they are claimed in a merely generic manner (e.g., at a high level of generality) or as insignificant extra-solution activity. See MPEP 2106.05(d)(II)(i).
The claims also recite the additional limitations of “by a cloud service control plane of the cloud service system; by one or more computing clusters of the cloud service system and based on the N second messages; wherein each of the N second messages is an approximate query processing (AQP) message, wherein each of the N second messages corresponds to one of a plurality of sampling parameters, wherein the sampling parameters have different values, and wherein N is an integer greater than 1; wherein obtaining the data query results comprises respectively sending the N second messages to N computing clusters, wherein the N computing clusters are in one-to-one correspondence with the N second messages, wherein the N computing clusters obtain data from a storage node based on the N second messages to generate the data query results, and wherein each of the data query results corresponds to one of the N second messages; wherein each of the M data query response messages carries a corresponding data query result of the data query results, wherein the M data query response messages are in one-to-one correspondence with a subset of the N second messages, and wherein M is less than or equal to N.” that is mere instructions to apply an exception. A recitation of the words "apply it" (or an equivalent) are mere instructions to implement an abstract idea or other exception on a computer. (See MPEP 2106.05(f)).
As per dependent claim 2, the claim recites the limitation of:
The claim also recites the additional limitation of “wherein obtaining the data query results comprises simultaneously starting the N computing clusters to obtain the data from the storage node.” that is mere instructions to apply an exception. A recitation of the words "apply it" (or an equivalent) are mere instructions to implement an abstract idea or other exception on a computer. (See MPEP 2106.05(f)).
As per dependent claim 2, the claim recites the limitation of:
The claim also recites the additional limitation of “wherein obtaining the data query results comprises simultaneously starting the N computing clusters to obtain the data from the storage node.” that is mere instructions to apply an exception. A recitation of the words "apply it" (or an equivalent) are mere instructions to implement an abstract idea or other exception on a computer. (See MPEP 2106.05(f)).
As per dependent claims 3 and 11, the claims recite the limitation of:
The claim also recites the additional limitation of “wherein the subset comprises the N second messages having smaller sampling parameter values.” that is mere instructions to apply an exception. A recitation of the words "apply it" (or an equivalent) are mere instructions to implement an abstract idea or other exception on a computer. (See MPEP 2106.05(f)).
As per dependent claims 4 and 12, the claims recite the limitations of:
The claim also recites the additional limitation of “establishing, by the cloud service system, a connection to the client; and disconnecting, by the cloud service system, from the client when the M data query response messages meet a first condition that a corresponding data query result carried in each of the M data query response messages meets a first accuracy.” that is mere instructions to apply an exception. A recitation of the words "apply it" (or an equivalent) are mere instructions to implement an abstract idea or other exception on a computer. (See MPEP 2106.05(f)).
As per dependent claims 5 and 15, the claims recite the limitations of:
The claim also recites the additional limitation of “wherein the first data query request message is a structured query language (SQL) message.” that is mere instructions to apply an exception. A recitation of the words "apply it" (or an equivalent) are mere instructions to implement an abstract idea or other exception on a computer. (See MPEP 2106.05(f)).
As per dependent claims 6 and 16, the claims recite the limitations of:
The claim also recites the additional limitation of “wherein the first data query request message carries indication information instructing to feed back the M data query response messages.” that is mere instructions to apply an exception. A recitation of the words "apply it" (or an equivalent) are mere instructions to implement an abstract idea or other exception on a computer. (See MPEP 2106.05(f)).
As per dependent claims 7 and 17, the claims recite the limitations of:
The claims also recite the additional limitations of “receiving a third message, wherein the third message is a second data query request message, and wherein the third message is in a language different from that of the first data query request message;”, and the courts have recognized that receiving or transmitting data over a network, e.g., using the Internet to gather data, as well‐understood, routine, and conventional functions when they are claimed in a merely generic manner (e.g., at a high level of generality) or as insignificant extra-solution activity. See MPEP 2106.05(d)(II)(i).
The claims also recite the additional element of “a structured query language (SQL) message” that is mere instructions to apply an exception. A recitation of the words "apply it" (or an equivalent) are mere instructions to implement an abstract idea or other exception on a computer. (See MPEP 2106.05(f)).
As per dependent claims 8 and 18, the claims recite the limitations of:
The claim also recites the additional limitation of “wherein the third message carries indication information instructing to feed back the M data query response messages.” that is mere instructions to apply an exception. A recitation of the words "apply it" (or an equivalent) are mere instructions to implement an abstract idea or other exception on a computer. (See MPEP 2106.05(f)).
As per dependent claims 9 and 19, the claims recite the limitations of:
The claim also recites the additional limitation of “wherein the third message is a Python message.” that is mere instructions to apply an exception. A recitation of the words "apply it" (or an equivalent) are mere instructions to implement an abstract idea or other exception on a computer. (See MPEP 2106.05(f)).
As per independent claim 10, the claim recites the limitation of:
“a memory configured to store instructions; and one or more processors.” This element is example of mere instruction to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea (see MPEP § 2106.05(f)). Specifically, the additional elements of the limitations invoke computers or other machinery merely as a tool to perform an existing process. Use of a computer or other machinery in its ordinary capacity for economic or other tasks (e.g., to receive, store, or transmit data) or simply adding a general-purpose computer or computer components after the fact to an abstract idea (e.g., a fundamental economic practice or mathematical equation) do not provide improvements to the functioning of a computer or to any other technology or technical field; and do not integrate a judicial exception into a practical application.
As per independent claim 20, the claim recites the limitation of:
“a non-transitory computer-readable medium; and computing device cluster.” This element is example of mere instruction to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea (see MPEP § 2106.05(f)). Specifically, the additional elements of the limitations invoke computers or other machinery merely as a tool to perform an existing process. Use of a computer or other machinery in its ordinary capacity for economic or other tasks (e.g., to receive, store, or transmit data) or simply adding a general-purpose computer or computer components after the fact to an abstract idea (e.g., a fundamental economic practice or mathematical equation) do not provide improvements to the functioning of a computer or to any other technology or technical field; and do not integrate a judicial exception into a practical application.
Therefore, claims 1-20 do not integrate the recited abstract ideas into a practical application.
Step 2B: Claim provides an Inventive Concept?
With respect to the limitations identified as insignificant extra-solution activity above the conclusions are carried over, and both the “receiving ….; sending …; obtaining …” are well-understood, routine, and conventional operations.
For support as being well-understood, routine, and conventional for “receiving ….; sending …; and obtaining …” as noted by the courts is well understood routine and conventional, see MPEP 2106.05(d)(ii) “i. Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information); … buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network);” and/or MPEP 2106.05(d)(ii) “iv. Storing and retrieving information in memory, Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015); OIP Techs., 788 F.3d at 1363, 115 USPQ2d at 1092-93;”, and/or MPEP 2106.05(d)(II) “iii. Ultramercial, 772 F.3d at 716, 112 USPQ2d at 1755 (updating an activity log);” Marsh (US 20070058807 A1) – para. [0051] “The graphics chip or codec chip would then typically send the result back to the CPU for evaluation.” and para. [0053] “the CPU typically receives the results of the query from the graphics chip or audio codec chip.”; Elad et al. (US 20230350889 A1) – para. [0040] “run the standard SQL script to obtain a processing result of the data query request.”; and Schmiedehausen – para. [0047] “services typically needed to receive information (e.g., queries and/or other service requests) in a format native to the particular service and/or associated datastores.”.
Looking at the limitations in combination and the claim as a whole does not change this conclusion and the claim is ineligible.
Therefore, the claims 1-20 are not patent eligible.
Claim Rejections - 35 USC § 103
7. 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 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 of this title, 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.
The factual inquiries set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied for establishing a background for determining obviousness under pre-AIA 35 U.S.C. § 103(a) are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
8. Claims 1-6 and 10-16 are rejected under 35 U.S.C. § 103 as being unpatentable over Kumar et al. (US 20190065556 A1) in view of Cao et al. (US 20150370854 A1) in further view of Wang et al. (US 20180260450 A1).
As per claim 1, Kumar teaches a method (i.e. “methods to provide an interactive query processing system”; para. [0032]) comprising:
receiving (i.e. “receives”; fig.11, para. [0077]),
by a user interface (i.e. “a user interface”; Abstract)
of a cloud service system (i.e. “The user interface circuitry 114 may be implemented in a user interface server 138. In certain embodiments, the user interface server 138, which may also be implemented in a cloud-based service or other hosted service”; para. [0041]) and
from a client (i.e. “from the user device’’; para. [0077]),
a first data query request message (i.e. “an example screenshot 1300 of the dynamic graphical user interface provided by the user interface circuitry 114, the user may type (or speak) the first query into the user device 118 within the dynamic graphical user interface (GUI) presented to the user. The GUI presents a “chat bot” or other interactive element that enables the user to enter the query or question, shown at 1302 (“What is the overview of Apple?”).”; figs. 13:1300, 14, para. [0061], [0079]; Examiner note the first data query request message is interpreted as the (“What is the overview of Apple?”));
generating, by a cloud service control plane of the cloud service system (i.e. “a dynamic graphical user interface”; figs. 13-26, para. [0018], [0061], [0077]; Examiner note: the cloud service control plane of the cloud service system is interpreted as the dynamic graphical user interface), N second messages based on the first data query request message (i.e. “the GUI provides an overview of an account, including one or more interactive graphical elements 1402 as well as a list of actionable insights at 1404.”; figs. 14, para. [0077]; Examiner note: the generating the N second messages is interpreted as one or more interactive graphical elements as well as a list of actionable insights);
obtaining, by one or more computing clusters of the cloud service system (i.e. “Each server 132-142 may represent a different distributed computing service (e.g., in the “cloud”) that performs particular tasks or portions of the overall functionality of the interactive query processing system 100”; fig.1, para. [0040]; Examiner note: the one or more computing clusters is interpreted as the Each server 132-142) and based on the N second messages (i.e. “the GUI provides an overview of an account, including one or more interactive graphical elements 1402 as well as a list of actionable insights at 1404.”; figs. 14, para. [0077]; Examiner note: the generating the N second messages is interpreted as one or more interactive graphical elements as well as a list of actionable insights), data query results (i.e. “For example, with brief reference to FIG. 15, upon selection of the “Get More” button 1504, the user interface circuitry 114 may provide the user with additional information 1506 (here, shown as key buyers along with selectable contact information links).”; fig. 15, para. [0071]; Examiner note: the obtaining the data query results is interpreted as the provide the user with additional information 1506 (here, shown as key buyers along with selectable contact information links)),
wherein obtaining the data query results (i.e. “For example, with brief reference to FIG. 15, upon selection of the “Get More” button 1504, the user interface circuitry 114 may provide the user with additional information 1506 (here, shown as key buyers along with selectable contact information links).”; fig. 15, para. [0071]; Examiner note: the obtaining the data query results is interpreted as the provide the user with additional information 1506 (here, shown as key buyers along with selectable contact information links)) comprises respectively sending the N second messages to N computing clusters (i.e. “At 318, the query processing hardware 102 communicates the received first result information to the user device. This may include, for example, communicating the received first result information to the user interface circuitry 114,”; fig.3, para. [0064]). Further, i.e. “The communication interfaces 246 may support communication with the user devices 118, either directly or through one or more other servers (e.g., user interface server 138)”; fig. 1, para. [0056]; Examiner note: The N second messages is interpreted as the first result information. The N Computing clusters is interpreted as the one or more other servers (e.g., user interface server 138)),
wherein the N computing clusters are in one-to-one correspondence with the N second messages (i.e. “The communication interfaces 246 may support communication with the user devices 118, either directly or through one or more other servers (e.g., user interface server 138.”; fig.1, para. [0056]),
wherein the N computing clusters obtain data from a storage node (i.e “At 610, the query processing hardware 102 receives the second result information from the second application responsive to the second application query and, at 612, communicates the second result information to the user device 118.””; figs. 6, 15, para. [0071]; Examiner note: the N computing clusters obtain data is interpreted as the query processing hardware 102 receives the second result information; the storage node is interpreted as the second application. Further, “a storage device 268 (e.g., a hard drive, solid-state drive, or other memory system) to enable local storage of system software, user interfaces, or system instructions.”; fig. 1, para. [0058]; Examiner note: the second application is understood to retrieve the data from the storage device where it was stored) based on the N second messages (i.e. “the GUI provides an overview of an account, including one or more interactive graphical elements 1402 as well as a list of actionable insights at 1404.”; figs. 14, para. [0077]; Examiner note: the generating the N second messages is interpreted as one or more interactive graphical elements as well as a list of actionable insights) to generate the data query results (i.e. “For example, with brief reference to FIG. 15, upon selection of the “Get More” button 1504, the user interface circuitry 114 may provide the user with additional information 1506 (here, shown as key buyers along with selectable contact information links).”; fig. 15, para. [0071]; Examiner note: the obtaining/generating the data query results is interpreted as the provide the user with additional information 1506 (here, shown as key buyers along with selectable contact information links)), and
wherein each of the data query results corresponds to one of the N second messages (i.e. “A user's selection of this button initiates the user device 118 to communicate the request for additional data corresponding to the first result information. … the query processing hardware 102 may be preprogrammed according to control logic 208 to extract additional data from the applications that is related to the previous information provided to the user or to a particular entity or account as a whole.”; figs. 6, 5, para. [0071]-[0072]; Examiner note; the each of the data query results corresponds to one of the N second messages is interpreted as the additional data corresponding to the first result information);
However, it is noted that the prior art of Kumar does not explicitly teach “wherein each of the N second messages is an approximate query processing (AQP) message, wherein each of the N second messages corresponds to one of a plurality of sampling parameters, wherein the sampling parameters have different values, and wherein N is an integer greater than 1; individually sending, by the user interface to the client and in an order of obtaining the data query results, M data query response messages, wherein each of the M data query response messages carries a corresponding data query result of the data query results; wherein the M data query response messages are in one-to-one correspondence with a subset of the N second messages, and wherein M is less than or equal to N.”
On the other hand, in the same field of endeavor, Cao teaches wherein each of the N second messages is an approximate query processing (AQP) message (i.e. “The sub-query processing unit 126 may generate an approximate answer for each of the sub queries”; figs. 1-4, para. [0022], [0025], [0027]-[0029]; Examiner note: the approximate answer of each of the sub queries is the approximate query processing (AQP) message),
wherein each of the N second messages corresponds to one of a plurality of sampling parameters (i.e. “the result of the sub-query can be scaled up based on the sampling ratio”; para. [0025]; Examiner note: the sampling parameters is interpreted as the sampling ratio),
wherein the sampling parameters have different values (i.e. “answer Y=process(S,q) can be obtained using samples, and then this answer can be modified by using the statistics of the rest values”; para. [0032]; Examiner note: the different values in interpreted as the statistics of the rest values), and
wherein N is an integer greater than 1 (i.e. “set of sub queries”; para. [0022]. Further, i.e. “(A1Ωv1 AND A3Ωv3 AND NOT A4Ωv4) OR (A2Ωv2 AND A3Ωv3 AND NOT A4Ωv4), wherein “(A1Ωv1 AND A3Ωv3 AND NOT A4Ωv4)” and “(A2Ωv2 AND A3Ωv3 AND NOT A4Ωv4)” are the converted sub-queries.”; para. [0024]; Examiner note: N refer to second messages which are sub queries in Cao and there are many sub queries which in number are greater than 1);
wherein the M data query response messages are in one-to-one correspondence with a subset of the N second messages (i.e. “a sub-query processing module may generate an approximate answer for each of the sub queries”: para. [0074]; Examiner note: the subset of the N second message is interpreted as the each of the sub queries. M data query response messages is interpreted as the an approximate answer), and
wherein M is less than or equal to N (i.e. “a result Z=process(S,q) can be obtained by using samples”; para. [0043]; Examiner note: M is the a result which is less than N which is samples. Further, i.e. “For example, a query name=“failed login” AND message!=“success” searches for event logs with a “name” field set to “failed login” and a message field not set to “success”.”; para. [0014], [0021]).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the teachings of Cao that teaches an approximate answer for a query on a database into the prior art of Kumar that teaches machines and complex system architectures that process queries using natural language processing, access one or more applications in relation to the query, and return information responsive to the query. Additionally, this can keep a short query response time and provide insights about the data.
The motivation for doing so would be to divide a query into multiple sub-queries and obtain approximate answers for each, because it can then be combined to reach an approximate result for the original query (Cao, para. [0008]).
However, it is noted that the combination of the prior arts of Kumar and Cao do not explicitly teach “individually sending, by the user interface to the client and in an order of obtaining the data query results, M data query response messages, wherein each of the M data query response messages carries a corresponding data query result of the data query results;”
On the other hand, in the same field of endeavor, Wang teaches individually sending, by the user interface to the client (i.e. “communicated via the user interface component (106) to the client application”; para. [0025]) and in an order of obtaining the data query results (“generates from the approximate query results an approximate visualization”; para. [0038]), M data query response messages (i.e. “communicates the approximate visualization (304) back to the client device”; para. [0038]; Examiner note: the M data query response message is the approximate visualization. The individually sending to the client is interpreted as the communicates the approximate visualization (304) back to the client device),
wherein each of the M data query response messages carries a corresponding data query result of the data query results (i.e. “The data visualization generating component (114) derives data visualizations based on query results.”; para. [0025]);
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the teachings of Wang that teaches techniques for presenting data visualizations into the combination of prior arts of Kumar that teaches machines and complex system architectures that process queries using natural language processing, access one or more applications in relation to the query, and return information responsive to the query, and Cao that teaches an approximate answer for a query on a database. Additionally, this can keep a short query response time and provide insights about the data.
The motivation for doing so would be to use Approximate Query Processing (AQP) to facilitate data visualization because it can quickly provide approximate answers (Wang, para. [0005]).
As per claim 2, Kumar, Cao and Wang teach all the limitations as discussed in claim 1 above.
Additionally, Kumar teaches wherein obtaining the data query results comprises: simultaneously starting the N computing clusters (i.e. “may represent a plurality of server devices that work together to implement the interactive query processing system 100.”; fig.2, para. [0042]; Examiner note: the N computing clusters is interpreted as the plurality of server devices. The simultaneously is interpreted as the work together which is inherent feature of a cluster system) to obtain the data from the storage node (i.e. “At 416, the query processing hardware 102 determines a second application query associated with the second intent and the first entity. At 418, the query processing hardware 102 communicates the second application query to the second application. At 420, the query processing hardware 102 receives second result information from the second application responsive to the second application query. At 422, the query processing hardware 102 communicates the second result information to the user device 118.”; fig. 4, para. [0067]).
As per claim 3, Kumar, Cao and Wang teach all the limitations as discussed in claim 1 above.
However, it is noted that the prior art of Kumar and Wang do not explicitly teach “wherein the subset comprises the N second messages having smaller sampling parameter values.”
On the other hand, in the same field of endeavor, Cao teaches wherein the subset comprises the N second messages having smaller sampling parameter values (i.e. “another answer Y=process(S,q) can be obtained using samples, and then this answer can be modified by using the statistics of the rest values other than top-k items in the histogram h.”; para. [0032]; Examiner note: the having smaller sampling parameter values is interpreted as the rest values other than top-k items).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the teachings of Cao that teaches an approximate answer for a query on a database into the prior art of Kumar that teaches machines and complex system architectures that process queries using natural language processing, access one or more applications in relation to the query, and return information responsive to the query, and Wang that teaches techniques for presenting data visualizations. Additionally, this can keep a short query response time and provide insights about the data.
The motivation for doing so would be to divide a query into multiple sub-queries and obtain approximate answers for each, because it can then be combined to reach an approximate result for the original query (Cao, para. [0008]).
As per claim 4, Kumar, Cao and Wang teach all the limitations as discussed in claim 1 above.
However, it is noted that the combination of prior arts of Kumar and Cao do not explicitly teach “further comprising: establishing, by a cloud service system, a connection to the client; and disconnecting, by the cloud service system, from the client when the M data query response messages meet a first condition that a corresponding data query result carried in each of the M data query response messages meets a first accuracy.”
On the other hand, in the same field of endeavor, Wang teaches further comprising: establishing, by a cloud service system, a connection to the client (i.e. “This data visualization is then communicated via the user interface component (106) to the client application (104) for presentation to the data analyst”; para. [0025]); and
disconnecting, by the cloud service system, from the client when the M data query response messages meet a first condition that a corresponding data query result carried in each of the M data query response messages meets a first accuracy (i.e. “To ensure query responsiveness using an error bound technique, the query processing may be terminated at some maximum query processing time, before the error bound condition is satisfied.”; para. [0028]).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the teachings of Wang that teaches techniques for presenting data visualizations into the combination of prior arts of Kumar that teaches machines and complex system architectures that process queries using natural language processing, access one or more applications in relation to the query, and return information responsive to the query, and Cao that teaches an approximate answer for a query on a database. Additionally, this can keep a short query response time and provide insights about the data.
The motivation for doing so would be to use Approximate Query Processing (AQP) to facilitate data visualization because it can quickly provide approximate answers (Wang, para. [0005]).
As per claim 5, Kumar, Cao and Wang teach all the limitations as discussed in claim 1 above.
However, it is noted that the prior art of Kumar and Wang do not explicitly teach “wherein the first data query request message is a structured query language (SQL) message.”
On the other hand, in the same field of endeavor, Cao teaches wherein the first data query request message is a structured query language (SQL) message (i.e. “The query q can be expressed using SQL”; Para. [0015]).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the teachings of Cao that teaches an approximate answer for a query on a database into the prior art of Kumar that teaches machines and complex system architectures that process queries using natural language processing, access one or more applications in relation to the query, and return information responsive to the query, and Wang that teaches techniques for presenting data visualizations. Additionally, this can keep a short query response time and provide insights about the data.
The motivation for doing so would be to divide a query into multiple sub-queries and obtain approximate answers for each, because it can then be combined to reach an approximate result for the original query (Cao, para. [0008]).
As per claim 6, Kumar, Cao and Wang teach all the limitations as discussed in claim 5 above.
However, it is noted that the combination of prior arts of Kumar and Cao do not explicitly teach “wherein the first data query request message carries indication information instructing to feed back the M data query response messages.”
On the other hand, in the same field of endeavor, Wang teaches wherein the first data query request message carries indication information instructing to feed back the M data query response messages (i.e. “a “query result” represents the raw data or information returned from executing a query against a dataset.”; para. [0020]).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the teachings of Wang that teaches techniques for presenting data visualizations into the combination of prior arts of Kumar that teaches machines and complex system architectures that process queries using natural language processing, access one or more applications in relation to the query, and return information responsive to the query, and Cao that teaches an approximate answer for a query on a database. Additionally, this can keep a short query response time and provide insights about the data.
The motivation for doing so would be to use Approximate Query Processing (AQP) to facilitate data visualization because it can quickly provide approximate answers (Wang, para. [0005]).
As per claim 10, Kumar teaches a computing device cluster (i.e. “a set of networked servers”; para. [0040]), comprising:
a memory configured to store instructions (i.e. “The memory 206 may store data”; para. [0044]); and
one or more processors coupled to the memory (i.e. “The processors 204 may be connected to the memory 206 “; para. [0043]) and
configured to execute the instructions to cause the computing device cluster to (i.e. “The control instructions may be executed by the processor 204 to implement any of the processing”; para. [0043]):
receive (i.e. “receives”; fig.11, para. [0077]),
from a client (i.e. “from the user device’’; para. [0077]),
a first data query request message (i.e. “an example screenshot 1300 of the dynamic graphical user interface provided by the user interface circuitry 114, the user may type (or speak) the first query into the user device 118 within the dynamic graphical user interface (GUI) presented to the user. The GUI presents a “chat bot” or other interactive element that enables the user to enter the query or question, shown at 1302 (“What is the overview of Apple?”).”; figs. 13:1300, 14, para. [0061], [0079]; Examiner note the first data query request message is interpreted as the (“What is the overview of Apple?”));
generate N second messages based on the first data query request message (i.e. “the GUI provides an overview of an account, including one or more interactive graphical elements 1402 as well as a list of actionable insights at 1404.”; figs. 14, para. [0077]; Examiner note: the generating the N second messages is interpreted as one or more interactive graphical elements as well as a list of actionable insights);
obtain, based on the N second messages (i.e. “the GUI provides an overview of an account, including one or more interactive graphical elements 1402 as well as a list of actionable insights at 1404.”; figs. 14, para. [0077]; Examiner note: the generating the N second messages is interpreted as one or more interactive graphical elements as well as a list of actionable insights), data query results (i.e. “For example, with brief reference to FIG. 15, upon selection of the “Get More” button 1504, the user interface circuitry 114 may provide the user with additional information 1506 (here, shown as key buyers along with selectable contact information links).”; fig. 15, para. [0071]; Examiner note: the obtaining the data query results is interpreted as the provide the user with additional information 1506 (here, shown as key buyers along with selectable contact information links)),
wherein obtaining the data query results (i.e. “For example, with brief reference to FIG. 15, upon selection of the “Get More” button 1504, the user interface circuitry 114 may provide the user with additional information 1506 (here, shown as key buyers along with selectable contact information links).”; fig. 15, para. [0071]; Examiner note: the obtaining the data query results is interpreted as the provide the user with additional information 1506 (here, shown as key buyers along with selectable contact information links)) comprises respectively sending the N second messages to N computing clusters (i.e. “At 318, the query processing hardware 102 communicates the received first result information to the user device. This may include, for example, communicating the received first result information to the user interface circuitry 114,”; fig.3, para. [0064]). Further, i.e. “The communication interfaces 246 may support communication with the user devices 118, either directly or through one or more other servers (e.g., user interface server 138)”; fig. 1, para. [0056]; Examiner note: The N second messages is interpreted as the first result information. The N Computing clusters is interpreted as the one or more other servers (e.g., user interface server 138)),
wherein the N computing clusters are in one-to-one correspondence with the N second messages (i.e. “The communication interfaces 246 may support communication with the user devices 118, either directly or through one or more other servers (e.g., user interface server 138.”; fig.1, para. [0056]),
wherein the N computing clusters obtain data from a storage node (i.e “At 610, the query processing hardware 102 receives the second result information from the second application responsive to the second application query and, at 612, communicates the second result information to the user device 118.””; figs. 6, 15, para. [0071]; Examiner note: the N computing clusters obtain data is interpreted as the query processing hardware 102 receives the second result information; the storage node is interpreted as the second application. Further, “a storage device 268 (e.g., a hard drive, solid-state drive, or other memory system) to enable local storage of system software, user interfaces, or system instructions.”; fig. 1, para. [0058]; Examiner note: the second application is understood to retrieve the data from the storage device where it was stored) based on the N second messages (i.e. “the GUI provides an overview of an account, including one or more interactive graphical elements 1402 as well as a list of actionable insights at 1404.”; figs. 14, para. [0077]; Examiner note: the generating the N second messages is interpreted as one or more interactive graphical elements as well as a list of actionable insights) to generate the data query results (i.e. “For example, with brief reference to FIG. 15, upon selection of the “Get More” button 1504, the user interface circuitry 114 may provide the user with additional information 1506 (here, shown as key buyers along with selectable contact information links).”; fig. 15, para. [0071]; Examiner note: the obtaining/generating the data query results is interpreted as the provide the user with additional information 1506 (here, shown as key buyers along with selectable contact information links)), and
wherein each of the data query results corresponds to one of the N second messages (i.e. “A user's selection of this button initiates the user device 118 to communicate the request for additional data corresponding to the first result information. … the query processing hardware 102 may be preprogrammed according to control logic 208 to extract additional data from the applications that is related to the previous information provided to the user or to a particular entity or account as a whole.”; figs. 6, 5, para. [0071]-[0072]; Examiner note; the each of the data query results corresponds to one of the N second messages is interpreted as the additional data corresponding to the first result information);
However, it is noted that the prior art of Kumar does not explicitly teach “wherein each of the N second messages is an approximate query processing (AQP) message, wherein each of the N second messages corresponds to one of a plurality of sampling parameters, wherein the sampling parameters have different values, and wherein N is an integer greater than 1; send, to the client, M data query response messages, wherein each of the M data query response messages carries a corresponding data query result of the data query results, wherein the M data query response messages are in one-to-one correspondence with a subset of the N second messages, and wherein M is less than or equal to N.”
On the other hand, in the same field of endeavor, Cao teaches wherein each of the N second messages is an approximate query processing (AQP) message (i.e. “The sub-query processing unit 126 may generate an approximate answer for each of the sub queries”; figs. 1-4, para. [0022], [0025], [0027]-[0029]; Examiner note: the approximate answer of each of the sub queries is the approximate query processing (AQP) message),
wherein each of the N second messages corresponds to one of a plurality of sampling parameters (i.e. “the result of the sub-query can be scaled up based on the sampling ratio”; para. [0025]; Examiner note: the sampling parameters is interpreted as the sampling ratio),
wherein the sampling parameters have different values (i.e. “answer Y=process(S,q) can be obtained using samples, and then this answer can be modified by using the statistics of the rest values”; para. [0032]; Examiner note: the different values in interpreted as the statistics of the rest values), and
wherein N is an integer greater than 1 (i.e. “set of sub queries”; para. [0022]. Further, i.e. “(A1Ωv1 AND A3Ωv3 AND NOT A4Ωv4) OR (A2Ωv2 AND A3Ωv3 AND NOT A4Ωv4), wherein “(A1Ωv1 AND A3Ωv3 AND NOT A4Ωv4)” and “(A2Ωv2 AND A3Ωv3 AND NOT A4Ωv4)” are the converted sub-queries.”; para. [0024]; Examiner note: the N refer to second messages which are sub queries in Cao and there are many sub queries which in number are greater than 1);
wherein the M data query response messages are in one-to-one correspondence with a subset of the N second messages (i.e. “a sub-query processing module may generate an approximate answer for each of the sub queries”: para. [0074]; Examiner note: the subset of the N second message is interpreted as the each of the sub queries. M data query response messages is interpreted as the an approximate answer), and
wherein M is less than or equal to N (i.e. “a result Z=process(S,q) can be obtained by using samples”; para. [0043]; Examiner note: M is the a result which is less than N which is samples. Further, i.e. “For example, a query name=“failed login” AND message!=“success” searches for event logs with a “name” field set to “failed login” and a message field not set to “success”.”; para. [0014], [0021]).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the teachings of Cao that teaches an approximate answer for a query on a database into the prior art of Kumar that teaches machines and complex system architectures that process queries using natural language processing, access one or more applications in relation to the query, and return information responsive to the query. Additionally, this can keep a short query response time and provide insights about the data.
The motivation for doing so would be to divide a query into multiple sub-queries and obtain approximate answers for each, because it can then be combined to reach an approximate result for the original query (Cao, para. [0008]).
However, it is noted that the combination of the prior arts of Kumar and Cao do not explicitly teach “send, to the client, M data query response messages, wherein each of the M data query response messages carries a corresponding data query result of the data query results;”
On the other hand, in the same field of endeavor, Wang teaches send, to the client, M data query response messages (i.e. “communicates the approximate visualization (304) back to the client device”; para. [0038]; Examiner note: the M data query response message is the approximate visualization. The send, to the client is interpreted as the communicates the approximate visualization (304) back to the client device),
wherein each of the M data query response messages carries a corresponding data query result of the data query results (i.e. “The data visualization generating component (114) derives data visualizations based on query results.”; para. [0025]);
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the teachings of Wang that teaches techniques for presenting data visualizations into the combination of prior arts of Kumar that teaches machines and complex system architectures that process queries using natural language processing, access one or more applications in relation to the query, and return information responsive to the query, and Cao that teaches an approximate answer for a query on a database. Additionally, this can keep a short query response time and provide insights about the data.
The motivation for doing so would be to use Approximate Query Processing (AQP) to facilitate data visualization because it can quickly provide approximate answers (Wang, para. [0005]).
As per claim 11, Kumar, Cao and Wang teach all the limitations as discussed in claim 10 above.
However, it is noted that the prior art of Kumar and Wang do not explicitly teach “wherein the M data query response messages correspond to the M second messages, and wherein the M second messages are the N second messages having smaller sampling parameter values.”
On the other hand, in the same field of endeavor, Cao teaches wherein the M data query response messages correspond to the M second messages (i.e. “a sub-query processing module may generate an approximate answer for each of the sub queries”: para. [0074]; Examiner note: the second message is interpreted as the each of the sub queries. M data query response messages is interpreted as the approximate answer), and
wherein the M second messages are the N second messages having smaller sampling parameter values (i.e. “another answer Y=process(S,q) can be obtained using samples, and then this answer can be modified by using the statistics of the rest values other than top-k items in the histogram h.”; para. [0032]; Examiner note: the having smaller sampling parameter values is interpreted as the rest values other than top-k items).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the teachings of Cao that teaches an approximate answer for a query on a database into the prior art of Kumar that teaches machines and complex system architectures that process queries using natural language processing, access one or more applications in relation to the query, and return information responsive to the query, and Wang that teaches techniques for presenting data visualizations. Additionally, this can keep a short query response time and provide insights about the data.
The motivation for doing so would be to divide a query into multiple sub-queries and obtain approximate answers for each, because it can then be combined to reach an approximate result for the original query (Cao, para. [0008]).
As per claim 12, Kumar, Cao and Wang teach all the limitations as discussed in claim 10 above.
However, it is noted that the prior art of Kumar and Wang do not explicitly teach “wherein the one or more processors are further configured to execute the instructions to cause the computing device cluster to: establish a connection to the client; and disconnect from the client when the M data query response messages meet a first condition that a corresponding data query result carried in each of the M data query response messages meets first accuracy.”
On the other hand, in the same field of endeavor, Cao teaches wherein the one or more processors are further configured to execute the instructions to cause the computing device cluster to: establish a connection to the client (i.e. “This data visualization is then communicated via the user interface component (106) to the client application (104) for presentation to the data analyst”; para. [0025]); and
disconnect from the client when the M data query response messages meet a first condition that a corresponding data query result carried in each of the M data query response messages meets first accuracy (i.e. “To ensure query responsiveness using an error bound technique, the query processing may be terminated at some maximum query processing time, before the error bound condition is satisfied.”; para. [0028]).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the teachings of Cao that teaches an approximate answer for a query on a database into the prior art of Kumar that teaches machines and complex system architectures that process queries using natural language processing, access one or more applications in relation to the query, and return information responsive to the query, and Wang that teaches techniques for presenting data visualizations. Additionally, this can keep a short query response time and provide insights about the data.
The motivation for doing so would be to divide a query into multiple sub-queries and obtain approximate answers for each, because it can then be combined to reach an approximate result for the original query (Cao, para. [0008]).
As per claim 13, Kumar, Cao and Wang teach all the limitations as discussed in claim 10 above.
However, it is noted that the prior art of Kumar and Wang do not explicitly teach “wherein N and M are configured by the client.”
On the other hand, in the same field of endeavor, Cao teaches wherein N and M are configured by the client (i.e. “the query can be submitted from one of the client computers 104”; para. [0022]).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the teachings of Cao that teaches an approximate answer for a query on a database into the prior art of Kumar that teaches machines and complex system architectures that process queries using natural language processing, access one or more applications in relation to the query, and return information responsive to the query, and Wang that teaches techniques for presenting data visualizations. Additionally, this can keep a short query response time and provide insights about the data.
The motivation for doing so would be to divide a query into multiple sub-queries and obtain approximate answers for each, because it can then be combined to reach an approximate result for the original query (Cao, para. [0008]).
As per claim 14, Kumar, Cao and Wang teach all the limitations as discussed in claim 10 above.
However, it is noted that the prior art of Kumar and Wang do not explicitly teach “wherein the one or more processors are further configured to execute the instructions to cause the computing device cluster to configure N and M.”
On the other hand, in the same field of endeavor, Cao teaches wherein the one or more processors are further configured to execute the instructions to cause the computing device cluster to configure N and M (i.e. “A processor 501 generally retrieves and executes the computer-implemented instructions stored in the non-transitory, computer-readable medium 500 for obtaining an approximate answer for a query on a database.”; para. [0011]).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the teachings of Cao that teaches an approximate answer for a query on a database into the prior art of Kumar that teaches machines and complex system architectures that process queries using natural language processing, access one or more applications in relation to the query, and return information responsive to the query, and Wang that teaches techniques for presenting data visualizations. Additionally, this can keep a short query response time and provide insights about the data.
The motivation for doing so would be to divide a query into multiple sub-queries and obtain approximate answers for each, because it can then be combined to reach an approximate result for the original query (Cao, para. [0008]).
As per claim 15, Kumar, Cao and Wang teach all the limitations as discussed in claim 10 above.
However, it is noted that the prior art of Kumar and Wang do not explicitly teach “wherein the first data query request message is a structured query language (SQL) message.”
On the other hand, in the same field of endeavor, Cao teaches wherein the first data query request message is a structured query language (SQL) message (i.e. “The query q can be expressed using SQL”; Para. [0015]).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the teachings of Cao that teaches an approximate answer for a query on a database into the prior art of Kumar that teaches machines and complex system architectures that process queries using natural language processing, access one or more applications in relation to the query, and return information responsive to the query, and Wang that teaches techniques for presenting data visualizations. Additionally, this can keep a short query response time and provide insights about the data.
The motivation for doing so would be to divide a query into multiple sub-queries and obtain approximate answers for each, because it can then be combined to reach an approximate result for the original query (Cao, para. [0008]).
As per claim 16, Kumar, Cao and Wang teach all the limitations as discussed in claim 15 above.
However, it is noted that the prior art of Kumar and Wang do not explicitly teach “wherein the first data query request message carries indication information instructing to feed back the M data query response messages.”
On the other hand, in the same field of endeavor, Wang teaches wherein the first data query request message carries indication information instructing to feed back the M data query response messages (i.e. “a “query result” represents the raw data or information returned from executing a query against a dataset.”; para. [0020]).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the teachings of Wang that teaches techniques for presenting data visualizations into the combination of prior arts of Kumar that teaches machines and complex system architectures that process queries using natural language processing, access one or more applications in relation to the query, and return information responsive to the query, and Cao that teaches an approximate answer for a query on a database. Additionally, this can keep a short query response time and provide insights about the data.
The motivation for doing so would be to use Approximate Query Processing (AQP) to facilitate data visualization because it can quickly provide approximate answers (Wang, para. [0005]).
As per claim 20, Kumar teaches a computer program product comprising instructions that are stored on a non-transitory computer-readable medium and that, when executed by one or more processors, cause a computing device cluster to (i.e. “computer program product, may include a storage medium and instructions stored in or on the medium, and the instructions when executed by the circuitry in a device”; para. [0087]):
receiving (i.e. “receives”; fig.11, para. [0077]),
from a client (i.e. “from the user device’’; para. [0077]),
a first data query request message (i.e. “an example screenshot 1300 of the dynamic graphical user interface provided by the user interface circuitry 114, the user may type (or speak) the first query into the user device 118 within the dynamic graphical user interface (GUI) presented to the user. The GUI presents a “chat bot” or other interactive element that enables the user to enter the query or question, shown at 1302 (“What is the overview of Apple?”).”; figs. 13:1300, 14, para. [0061], [0079]; Examiner note the first data query request message is interpreted as the (“What is the overview of Apple?”));
generating N second messages based on the first data query request message (i.e. “the GUI provides an overview of an account, including one or more interactive graphical elements 1402 as well as a list of actionable insights at 1404.”; figs. 14, para. [0077]; Examiner note: the generating the N second messages is interpreted as one or more interactive graphical elements as well as a list of actionable insights);
obtaining, based on the N second messages (i.e. “the GUI provides an overview of an account, including one or more interactive graphical elements 1402 as well as a list of actionable insights at 1404.”; figs. 14, para. [0077]; Examiner note: the generating the N second messages is interpreted as one or more interactive graphical elements as well as a list of actionable insights), data query results (i.e. “For example, with brief reference to FIG. 15, upon selection of the “Get More” button 1504, the user interface circuitry 114 may provide the user with additional information 1506 (here, shown as key buyers along with selectable contact information links).”; fig. 15, para. [0071]; Examiner note: the obtaining the data query results is interpreted as the provide the user with additional information 1506 (here, shown as key buyers along with selectable contact information links)),
wherein obtaining the data query results (i.e. “For example, with brief reference to FIG. 15, upon selection of the “Get More” button 1504, the user interface circuitry 114 may provide the user with additional information 1506 (here, shown as key buyers along with selectable contact information links).”; fig. 15, para. [0071]; Examiner note: the obtaining the data query results is interpreted as the provide the user with additional information 1506 (here, shown as key buyers along with selectable contact information links)) comprises respectively sending the N second messages to N computing clusters (i.e. “At 318, the query processing hardware 102 communicates the received first result information to the user device. This may include, for example, communicating the received first result information to the user interface circuitry 114,”; fig.3, para. [0064]). Further, i.e. “The communication interfaces 246 may support communication with the user devices 118, either directly or through one or more other servers (e.g., user interface server 138)”; fig. 1, para. [0056]; Examiner note: The N second messages is interpreted as the first result information. The N Computing clusters is interpreted as the one or more other servers (e.g., user interface server 138)),
wherein the N computing clusters are in one-to-one correspondence with the N second messages (i.e. “The communication interfaces 246 may support communication with the user devices 118, either directly or through one or more other servers (e.g., user interface server 138.”; fig.1, para. [0056]),
wherein the N computing clusters obtain data from a storage node (i.e “At 610, the query processing hardware 102 receives the second result information from the second application responsive to the second application query and, at 612, communicates the second result information to the user device 118.””; figs. 6, 15, para. [0071]; Examiner note: the N computing clusters obtain data is interpreted as the query processing hardware 102 receives the second result information; the storage node is interpreted as the second application. Further, “a storage device 268 (e.g., a hard drive, solid-state drive, or other memory system) to enable local storage of system software, user interfaces, or system instructions.”; fig. 1, para. [0058]; Examiner note: the second application is understood to retrieve the data from the storage device where it was stored) based on the N second messages (i.e. “the GUI provides an overview of an account, including one or more interactive graphical elements 1402 as well as a list of actionable insights at 1404.”; figs. 14, para. [0077]; Examiner note: the generating the N second messages is interpreted as one or more interactive graphical elements as well as a list of actionable insights) to generate the data query results (i.e. “For example, with brief reference to FIG. 15, upon selection of the “Get More” button 1504, the user interface circuitry 114 may provide the user with additional information 1506 (here, shown as key buyers along with selectable contact information links).”; fig. 15, para. [0071]; Examiner note: the obtaining/generating the data query results is interpreted as the provide the user with additional information 1506 (here, shown as key buyers along with selectable contact information links)), and
wherein each of the data query results corresponds to one of the N second messages (i.e. “A user's selection of this button initiates the user device 118 to communicate the request for additional data corresponding to the first result information. … the query processing hardware 102 may be preprogrammed according to control logic 208 to extract additional data from the applications that is related to the previous information provided to the user or to a particular entity or account as a whole.”; figs. 6, 5, para. [0071]-[0072]; Examiner note; the each of the data query results corresponds to one of the N second messages is interpreted as the additional data corresponding to the first result information);
However, it is noted that the prior art of Kumar does not explicitly teach “wherein each of the N second messages is an approximate query processing (AQP) message, wherein each of the N second messages corresponds to one of a plurality of sampling parameters, wherein the sampling parameters have different values, and wherein N is an integer greater than 1; sending, to the client, M data query response messages, wherein each of the M data query response messages carries a corresponding data query result of the data query results; wherein the M data query response messages are in one-to-one correspondence with a subset of the N second messages, and wherein M is less than or equal to N.”
On the other hand, in the same field of endeavor, Cao teaches wherein each of the N second messages is an approximate query processing (AQP) message (i.e. “The sub-query processing unit 126 may generate an approximate answer for each of the sub queries”; figs. 1-4, para. [0022], [0025], [0027]-[0029]; Examiner note: the approximate answer of each of the sub queries is the approximate query processing (AQP) message),
wherein each of the N second messages corresponds to one of a plurality of sampling parameters (i.e. “the result of the sub-query can be scaled up based on the sampling ratio”; para. [0025]; Examiner note: the sampling parameters is interpreted as the sampling ratio),
wherein the sampling parameters have different values (i.e. “answer Y=process(S,q) can be obtained using samples, and then this answer can be modified by using the statistics of the rest values”; para. [0032]; Examiner note: the different values in interpreted as the statistics of the rest values), and
wherein N is an integer greater than 1 (i.e. “set of sub queries”; para. [0022]. Further, i.e. “(A1Ωv1 AND A3Ωv3 AND NOT A4Ωv4) OR (A2Ωv2 AND A3Ωv3 AND NOT A4Ωv4), wherein “(A1Ωv1 AND A3Ωv3 AND NOT A4Ωv4)” and “(A2Ωv2 AND A3Ωv3 AND NOT A4Ωv4)” are the converted sub-queries.”; para. [0024]; Examiner note: N refer to second messages which are sub queries in Cao and there are many sub queries which in number are greater than 1);
wherein the M data query response messages are in one-to-one correspondence with a subset of the N second messages (i.e. “a sub-query processing module may generate an approximate answer for each of the sub queries”: para. [0074]; Examiner note: the subset of the N second message is interpreted as the each of the sub queries. M data query response messages is interpreted as the an approximate answer), and
wherein M is less than or equal to N (i.e. “a result Z=process(S,q) can be obtained by using samples”; para. [0043]; Examiner note: M is the a result which is less than N which is samples. Further, i.e. “For example, a query name=“failed login” AND message!=“success” searches for event logs with a “name” field set to “failed login” and a message field not set to “success”.”; para. [0014], [0021]).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the teachings of Cao that teaches an approximate answer for a query on a database into the prior art of Kumar that teaches machines and complex system architectures that process queries using natural language processing, access one or more applications in relation to the query, and return information responsive to the query. Additionally, this can keep a short query response time and provide insights about the data.
The motivation for doing so would be to divide a query into multiple sub-queries and obtain approximate answers for each, because it can then be combined to reach an approximate result for the original query (Cao, para. [0008]).
However, it is noted that the combination of the prior arts of Kumar and Cao do not explicitly teach “sending, to the client, M data query response messages, wherein each of the M data query response messages carries a corresponding data query result of the data query results;”
On the other hand, in the same field of endeavor, Wang teaches sending, to the client, M data query response messages (i.e. “communicates the approximate visualization (304) back to the client device”; para. [0038]; Examiner note: the M data query response message is the approximate visualization. The sending to the client is interpreted as the communicates the approximate visualization (304) back to the client device),
wherein each of the M data query response messages carries a corresponding data query result of the data query results (i.e. “The data visualization generating component (114) derives data visualizations based on query results.”; para. [0025]);
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the teachings of Wang that teaches techniques for presenting data visualizations into the combination of prior arts of Kumar that teaches machines and complex system architectures that process queries using natural language processing, access one or more applications in relation to the query, and return information responsive to the query, and Cao that teaches an approximate answer for a query on a database. Additionally, this can keep a short query response time and provide insights about the data.
The motivation for doing so would be to use Approximate Query Processing (AQP) to facilitate data visualization because it can quickly provide approximate answers (Wang, para. [0005]).
9. Claims 7-9 and 17-19 are rejected under 35 U.S.C. § 103 as being unpatentable over Kumar et al. (US 20190065556 A1) in view of Cao et al. (US 20150370854 A1) in further view of Wang et al. (US 20180260450 A1) still in further view of Scholak et al. (US 20220358125 A1).
As per claim 7, Kumar, Cao and Wang teach all the limitations as discussed in claim 1 above.
However, it is noted that the prior art of Kumar and Wang do not explicitly teach “receiving a third message, wherein the third message is a second data query request message, and wherein the third message is in a language different from that of the first data query request message; and generating the first data query request message based on the third message, wherein the first data query request message is a structured query language (SQL) message.”
On the other hand, in the same field of endeavor, Cao teaches wherein the first data query request message is a structured query language (SQL) message (i.e. “The query q can be expressed using SQL”; Para. [0015]).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the teachings of Cao that teaches an approximate answer for a query on a database into the prior art of Kumar that teaches machines and complex system architectures that process queries using natural language processing, access one or more applications in relation to the query, and return information responsive to the query, and Wang that teaches techniques for presenting data visualizations. Additionally, this can keep a short query response time and provide insights about the data.
The motivation for doing so would be to divide a query into multiple sub-queries and obtain approximate answers for each, because it can then be combined to reach an approximate result for the original query (Cao, para. [0008]).
However, it is noted that the prior art of Kumar, Cao and Wang do not explicitly teach “further comprising: receiving a third message, wherein the third message is a second data query request message, and wherein the third message is in a language different from that of the first data query request message; and generating the first data query request message based on the third message;”
On the other hand, in the same field of endeavor, Scholak teaches further comprising: receiving a third message (i.e. “receive a natural language query (NLQ)”; para. [0037]),
wherein the third message is a second data query request message (i.e. “translating NLQs into SQL queries for different databases”; para. [0038]; Examiner note: the third message is NLQs and the second data query request message is SQL queries), and
wherein the third message is in a language different from that of the first data query request message (i.e. “natural language query to domain-specific language query (NLQ-to-DSLQ)”; PARA. [0011]); and
generating the first data query request message based on the third message (i.e. “generates one or more potential translations of the NLQ 308, and the partial translations are provided to the DSL parser 306 as they are generated”; para. [0040]);
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the teachings of Scholak that teaches constraining the output of neural language models to produce valid translations of natural language queries in a domain-specific language into the combination of the prior arts of Kumar that teaches machines and complex system architectures that process queries using natural language processing, access one or more applications in relation to the query, and return information responsive to the query, Cao that teaches an approximate answer for a query on a database, and Wang that teaches techniques for presenting data visualizations. Additionally, this can keep a short query response time and provide insights about the data.
The motivation for doing so would be to use a discovery (MID) server because it can facilitate communication of data between the network hosting the platform, other external applications, data sources, and services, and the client network (Scholak, para. [0024], [0029]).
As per claim 8, Kumar, Cao, Wang and Scholak teach all the limitations as discussed in claim 7 above.
However, it is noted that the prior art of Kumar, Cao and Scholak do not explicitly teach “wherein the third message carries indication information instructing to feed back the M data query response messages.”
On the other hand, in the same field of endeavor, Wang teaches wherein the third message carries indication information instructing to feed back the M data query response messages (i.e. “a “query result” represents the raw data or information returned from executing a query against a dataset.”; para. [0020]).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the teachings of Wang that teaches techniques for presenting data visualizations into the combination of the prior arts of Kumar that teaches machines and complex system architectures that process queries using natural language processing, access one or more applications in relation to the query, and return information responsive to the query, Cao that teaches an approximate answer for a query on a database, and Scholak that teaches constraining the output of neural language models to produce valid translations of natural language queries in a domain-specific language. Additionally, this can keep a short query response time and provide insights about the data.
The motivation for doing so would be to use Approximate Query Processing (AQP) to facilitate data visualization because it can quickly provide approximate answers (Wang, para. [0005]).
As per claim 9, Kumar, Cao, Wang and Scholak teach all the limitations as discussed in claim 7 above.
However, it is noted that the prior art of Kumar, Cao and Wang do not explicitly teach “wherein the third message is a Python message.”
On the other hand, in the same field of endeavor, Scholak teaches wherein the third message is a Python message (i.e. “a domain-specific language (DSL), such as Python”; para. [0011]).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the teachings of Scholak that teaches constraining the output of neural language models to produce valid translations of natural language queries in a domain-specific language into the combination of the prior arts of Kumar that teaches machines and complex system architectures that process queries using natural language processing, access one or more applications in relation to the query, and return information responsive to the query, Cao that teaches an approximate answer for a query on a database, and Wang that teaches techniques for presenting data visualizations. Additionally, this can keep a short query response time and provide insights about the data.
The motivation for doing so would be to use a discovery (MID) server because it can facilitate communication of data between the network hosting the platform, other external applications, data sources, and services, and the client network (Scholak, para. [0024], [0029]).
As per claim 17, Kumar, Cao and Wang teach all the limitations as discussed in claim 10 above.
However, it is noted that the prior art of Kumar and Wang do not explicitly teach “wherein the one or more processors are further configured to execute the instructions to cause the computing device cluster to: receive a third message, wherein the third message is a second data query request message, and wherein the third message is in a language different from that of the first data query request message; and generate the first data query request message based on the third message, wherein the first data query request message is a structured query language (SQL) message. wherein the first data query request message is a structured query language (SQL) message.”
On the other hand, in the same field of endeavor, Cao teaches wherein the first data query request message is a structured query language (SQL) message (i.e. “The query q can be expressed using SQL”; Para. [0015]).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the teachings of Cao that teaches an approximate answer for a query on a database into the prior art of Kumar that teaches machines and complex system architectures that process queries using natural language processing, access one or more applications in relation to the query, and return information responsive to the query, and Wang that teaches techniques for presenting data visualizations. Additionally, this can keep a short query response time and provide insights about the data.
The motivation for doing so would be to divide a query into multiple sub-queries and obtain approximate answers for each, because it can then be combined to reach an approximate result for the original query (Cao, para. [0008]).
However, it is noted that the prior art of Kumar, Cao and Wang do not explicitly teach “wherein the one or more processors are further configured to execute the instructions to cause the computing device cluster to: receive a third message, wherein the third message is a second data query request message, and wherein the third message is in a language different from that of the first data query request message; and generate the first data query request message based on the third message;”
On the other hand, in the same field of endeavor, Scholak teaches wherein the one or more processors are further configured to execute the instructions to cause the computing device cluster to: receive a third message (i.e. “receive a natural language query (NLQ)”; para. [0037]),
wherein the third message is a second data query request message (i.e. “translating NLQs into SQL queries for different databases”; para. [0038]; Examiner note: the third message is NLQs and the second data query request message is SQL queries), and
wherein the third message is in a language different from that of the first data query request message (i.e. “natural language query to domain-specific language query (NLQ-to-DSLQ)”; PARA. [0011]); and
generate the first data query request message based on the third message (i.e. “generates one or more potential translations of the NLQ 308, and the partial translations are provided to the DSL parser 306 as they are generated”; para. [0040]);
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the teachings of Scholak that teaches constraining the output of neural language models to produce valid translations of natural language queries in a domain-specific language into the combination of the prior arts of Kumar that teaches machines and complex system architectures that process queries using natural language processing, access one or more applications in relation to the query, and return information responsive to the query, Cao that teaches an approximate answer for a query on a database, and Wang that teaches techniques for presenting data visualizations. Additionally, this can keep a short query response time and provide insights about the data.
The motivation for doing so would be to use a discovery (MID) server because it can facilitate communication of data between the network hosting the platform, other external applications, data sources, and services, and the client network (Scholak, para. [0024], [0029]).
As per claim 18, Kumar, Cao and Wang teach all the limitations as discussed in claim 17 above.
However, it is noted that the prior art of Kumar, Cao and Scholak do not explicitly teach “wherein the third message carries indication information instructing to feed back the M data query response messages.”
On the other hand, in the same field of endeavor, Wang teaches wherein the third message carries indication information instructing to feed back the M data query response messages (i.e. “a “query result” represents the raw data or information returned from executing a query against a dataset.”; para. [0020]).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the teachings of Wang that teaches techniques for presenting data visualizations into the combination of the prior arts of Kumar that teaches machines and complex system architectures that process queries using natural language processing, access one or more applications in relation to the query, and return information responsive to the query, Cao that teaches an approximate answer for a query on a database, and Scholak that teaches constraining the output of neural language models to produce valid translations of natural language queries in a domain-specific language. Additionally, this can keep a short query response time and provide insights about the data.
The motivation for doing so would be to use Approximate Query Processing (AQP) to facilitate data visualization because it can quickly provide approximate answers (Wang, para. [0005]).
As per claim 19, Kumar, Cao, Wang and Scholak teach all the limitations as discussed in claim 17 above.
However, it is noted that the prior art of Kumar, Cao and Wang do not explicitly teach “wherein the third message is a Python message.”
On the other hand, in the same field of endeavor, Scholak teaches wherein the third message is a Python message (i.e. “a domain-specific language (DSL), such as Python”; para. [0011]).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the teachings of Scholak that teaches constraining the output of neural language models to produce valid translations of natural language queries in a domain-specific language into the combination of the prior arts of Kumar that teaches machines and complex system architectures that process queries using natural language processing, access one or more applications in relation to the query, and return information responsive to the query, Cao that teaches an approximate answer for a query on a database, and Wang that teaches techniques for presenting data visualizations. Additionally, this can keep a short query response time and provide insights about the data.
The motivation for doing so would be to use a discovery (MID) server because it can facilitate communication of data between the network hosting the platform, other external applications, data sources, and services, and the client network (Scholak, para. [0024], [0029]).
Prior Art of Record
10. The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Tian et al. (US 20250217397 A1), teaches processing a fuzzy query.
Conclusion
11. 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.
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/ANTONIO J CAIA DO/
Examiner, Art Unit 2164
/AMY NG/Supervisory Patent Examiner, Art Unit 2164
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