DETAILED ACTION
Response to Amendments
Prosecution is re-opened based on the Pre-Brief Conference decision on 07/14/2026. This action is made non-final and new grounds of rejection are introduced. In this Office Action, claims 1-28 are pending.
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Response to Arguments
Regarding 103 rejection:
Applicants’ arguments with respect to independent claims 1, 8, 15 and 22 have been considered but are moot in view of the new ground(s) of rejection. After further search and a thorough examination of the present application, claims 1-28 are rejected.
Applicant’s arguments regarding the rejection of claim 1 under 35 U.S.C. § 101 have been fully considered but are not persuasive.
Applicant argues that claim 1 does not recite a mental process because the claim includes “one or more machine learning models” and requires “the query to be sent to a data store,” which Applicant contends are limitations that cannot practically be performed in the human mind or with pen and paper. Applicant further argues that the Office improperly separates the machine-learning language from the complete limitation rather than considering the limitation as a whole.
The Examiner respectfully disagrees.
The claim has been considered as a whole, including the complete limitation:
“determining, based on the base question and one or more machine learning models, data for generating a query.”
The Office does not contend that a human can mentally perform the internal mathematical computations of an actual machine-learning model or electronically transmit data to a data store. Rather, the identified mental process is the underlying claimed activity of determining a base question from information in a request and determining what data is needed for generating a corresponding query.
Under the broadest reasonable interpretation, claim 1 is sufficiently broad to encompass a person reviewing a request, identifying conditional or limiting terms, determining the underlying question being asked, and determining the information needed to formulate a corresponding query. These activities involve observation, evaluation, analysis, and judgment and can practically be performed mentally or with pen and paper.
For example, a database administrator could receive a request from a sales manager stating:
“I want to see the top five selling products by region.”
The database administrator could identify “top five” and “by region” as limiting or conditional information, determine that the underlying question concerns which products have the highest sales, and determine what information would be needed to formulate a query responsive to the request. The administrator could perform these evaluations mentally or by writing the request and corresponding query information on paper.
The breadth of claim 1 is significant. The claim recites “determining” a base question and “determining” data for generating a query, but does not recite a particular procedure for making either determination. The claim also does not expressly recite a particular procedure by which the determined data is converted into the query. Instead, the final limitation merely refers to “generating the query using the data for generating the query.”
Accordingly, the claim recites the desired results at a high level of generality rather than a specific technological procedure for achieving those results.
Applicant’s reliance on the phrase “one or more machine learning models” does not change this conclusion.
The complete limitation does not recite any specific manner in which the determining is performed using the machine-learning models. Claim 1 does not identify a particular model, model architecture, model input, model output, inference procedure, algorithm, or other specific machine-learning operation.
The claim merely states that the determining is “based on” the base question and one or more machine-learning models.
Thus, the limitation is results-oriented. The recitation of machine-learning models merely associates a computer-based tool with the claimed determination without specifying how the tool technically performs the determination.
This is consistent with the October 2019 Update relied upon by Applicant. As Applicant acknowledges, the Update explains that when a claim recites a computer, examiners consider whether the underlying claimed concept is one that may be performed in the human mind and the computer is merely used as a tool to perform that concept. Here, the underlying activity of interpreting a request, determining the question being asked, and determining information for a query is mentally performable; the broadly recited machine-learning models merely provide a tool for assisting or automating that activity.
Applicant also relies on the final limitation requiring:
“the query to be sent to a data store.”
This limitation has likewise been considered as part of the claim as a whole. However, claim 1 does not require that the query actually be executed against the data store.
The claim does not require the data store to search stored records, retrieve responsive data, modify data, generate results, or return information to the user. Under the broadest reasonable interpretation, the query could simply be transmitted to the data store and saved or stored.
Thus, the final limitation does not meaningfully change the character of the preceding mental process. Rather, it merely adds the conventional computer activity of transmitting or storing the informational result of the abstract analysis.
Accordingly, the fact that electronic transmission itself cannot occur in the human mind does not prevent the claim from reciting a mental process where the nonmental limitation merely implements, transmits, or stores the result of the abstract analysis.
Applicant’s Alleged Improvement in Translating Requests into Data-Store Queries
Applicant next argues that the claimed invention is directed to an improvement in translating requests into data-store queries and refers to paragraphs [0003]–[0004], [0022]–[0159], and Figures 1–13 of the specification. Applicant contends that one of ordinary skill in the art would recognize the disclosed invention as providing a technological improvement and that claim 1 reflects that improvement.
The Examiner has considered Applicant’s asserted improvement and the portions of the specification identified by Applicant. The argument is not persuasive.
The relevant question is not merely whether the disclosed technique improves the performance of the claimed abstract task. Rather, the claimed improvement must be an improvement to the functioning of a computer or another technology or technical field, and the claim must reflect the components or steps that provide that technological improvement.
Claim 1 does not recite a particular technological mechanism by which the translation of a natural-language request into a query is improved.
For example, claim 1 does not recite:
a particular machine-learning architecture;
a particular model-training procedure;
a particular inference procedure;
a particular representation of the base question;
a particular output produced by the machine-learning models;
a particular query-generation algorithm;
a particular query language or syntax conversion;
a new data-store architecture;
an improved indexing or retrieval technique; or
a change to how the data store executes queries.
Rather, claim 1 broadly recites determining a base question, determining data for generating a query based on the base question and unspecified machine-learning models, and sending the resulting query to a data store.
Therefore, even assuming that the disclosed system translates natural-language requests into queries more accurately, quickly, or efficiently than prior approaches, claim 1 recites at most an improvement in carrying out the abstract idea itself—translating a request into information suitable for formulating a query—not an improvement to computer functionality or to the operation of the data store.
The Federal Circuit has repeatedly distinguished an improvement in the performance of an abstract task from an improvement in technology.
In Intellectual Ventures I LLC v. Capital One Bank (USA), 792 F.3d 1363, 1367 (Fed. Cir. 2015), the court explained that improved speed or efficiency inherent in applying an abstract idea on a computer does not provide a sufficient inventive concept.
Similarly, in Bancorp Services, L.L.C. v. Sun Life Assurance Co. of Canada (U.S.), 687 F.3d 1266, 1278 (Fed. Cir. 2012), the court explained that the fact that required calculations may be performed more efficiently using a computer does not materially alter the patent-eligibility analysis.
In Synopsys, Inc. v. Mentor Graphics Corp., 839 F.3d 1138, 1151 (Fed. Cir. 2016), the court explained that a new abstract idea remains an abstract idea.
Likewise, Customedia Technologies, LLC v. Dish Network Corp., 951 F.3d 1359, 1365 (Fed. Cir. 2020), distinguishes claims that improve how a computer itself operates from claims that merely achieve generic speed or efficiency improvements through computer implementation.
Finally, OIP Technologies, Inc. v. Amazon.com, Inc., 788 F.3d 1359, 1363 (Fed. Cir. 2015), explains that relying on a computer to perform routine tasks more quickly or accurately is insufficient to make an otherwise abstract process patent eligible.
Here, claim 1 does not improve how the computer, machine-learning models, or data store themselves operate. The machine-learning models merely automate or assist the process of determining what information should be used to formulate a query. The data store continues to perform its ordinary function as a destination for the query.
Accordingly, any asserted improvement in the speed, efficiency, accuracy, or automation of translating a natural-language request into a query constitutes an improvement in the abstract process itself rather than a technological improvement that integrates the judicial exception into a practical application.
Applicant’s argument that the Office failed to conduct the required Step 2A, Prong Two analysis is also moot in view of the present rejection, which expressly considers the machine-learning models, generation of the query, transmission of the query to the data store, the claim as a whole, and Applicant’s asserted technological improvement.
For at least these reasons, Applicant’s arguments do not overcome the rejection. The rejection of claim 1 under 35 U.S.C. § 101 is maintained.
Claim Rejections - 35 USC §101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-28 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
Based upon consideration of all of the relevant factors with respect to the claims as a whole, claims 1-28 are determined to be directed to an abstract idea and not significantly more than the abstract idea itself. The rationale for this determination is explained below:
At Step 1: The claims 1,8, 15 and 22 recite determining, based on identifying conditional terms associated with data indicative of a request from a user, a base question; determining, based on the base question and one or more machine learning models, data for generating a query; and causing, based on generating the query using the data for generating the query, the query to be sent to a data store. Therefore, the claims are directed to a process which is a statutory category of invention.
At Step 2A, prong One:
Claims 1, 8, 15 and 22 recite the following limitations directed to an abstract idea:
“determining, based on identifying conditional terms associated with data indicative of a request from a user, a base question” and “determining, based on the base question, data for generating a query.”
These limitations involve observation, evaluation, analysis, and judgment. For example, a person could receive a request from a user, review the words or terms included in the request, identify conditional terms such as “if,” “when,” “where,” or “greater than,” determine the underlying question being asked, and determine the information needed to formulate a query.
A person could perform these steps by mentally reviewing the request or by writing the request and relevant conditional terms on paper. The person could then identify the base question and determine the data or characteristics needed to generate a corresponding query.
Thus, the claimed steps of interpreting the user’s request, identifying conditional terms, determining the underlying question, and determining information for generating a query are evaluations and judgments that can practically be performed in the human mind or with the aid of pen and paper.
Accordingly, claims 1, 8, 15, and 22 recite a mental process, which is an abstract idea. Mental processes include concepts performed in the human mind, such as observations, evaluations, judgments, and opinions.
At Step 2A, Prong Two:
Claims 1, 8, 15, 22 recite the additional elements of “one or more machine learning models,” generating a query, and causing the query to be sent to a data store.
However, the limitation of using “one or more machine learning models” merely amounts to using a computer-based tool to perform the recited evaluation and judgment. The claim does not recite a particular machine-learning architecture, a particular training technique, model parameters, a specific inference procedure, or another technological feature that improves the operation of the machine-learning model.
Instead, the machine-learning models are recited at a high level of generality and are used to perform the abstract idea of determining information for generating a query. The limitation therefore amounts to mere instructions to implement the abstract idea using a computer tool, as discussed in MPEP § 2106.05(f). Merely using a computer as a tool to perform an abstract idea does not integrate the judicial exception into a practical application.
The limitation of generating the query merely produces the result of the preceding mental evaluation. The claim does not recite a particular query language, query structure, query-generation algorithm, database architecture, or technical procedure for generating the query. Rather, the claim states the desired result of generating a query using the determined data.
The limitation of:
“causing, based on generating the query using the data for generating the query, the query to be sent to a data store”
merely transmits the result of the abstract analysis to a data store. Sending the generated query is insignificant extra-solution activity because it occurs after the base question and query-generation data have been determined. Transmitting or storing information at a high level of generality does not meaningfully limit the mental process. See MPEP § 2106.05(g).
The data store also merely limits the abstract idea to a particular technological environment or field of use. The claim does not recite an improvement to the operation, organization, storage structure, retrieval process, or processing capability of the data store.
The claim therefore does not recite an improvement to computer functionality or another technology. It does not explain how the computer or machine-learning model performs the claimed determinations in an improved manner. Merely adding generic computer components to perform a method does not establish a technological improvement.
Viewing the additional limitations together and claim as a whole, nothing integrates the judicial exception into a practical application.
At Step 2B:
Claims 1, 8, 15, 22 do not recite additional elements that amount to significantly more than the judicial exception.
The additional elements, including the one or more machine learning models, generation of the query, and transmission of the query to a data store, are recited at a high level of generality and perform ordinary computer functions such as processing information, generating information, transmitting information, and communicating with a data store.
The claims do not recite specialized hardware, an unconventional machine-learning architecture, a particular improvement to model training or inference, an improvement to database functionality, or another technological innovation.
The recited machine-learning models merely automate the mental steps of interpreting the user’s request and determining information for preparing a query. Merely performing an abstract idea more quickly or automatically through the use of a computer does not provide an inventive concept.
The step of sending the query to the data store also represents routine data transmission and/or storage. The claim does not require the data store to perform a particular nonconventional operation or specify how sending the query improves the operation of the data store or computing system.
As recognized in MPEP § 2106.05(d)(II), receiving or transmitting data over a network and storing or retrieving information in memory are well-understood, routine, and conventional computer functions. The Federal Circuit cases cited in the present Office Action support this conclusion.
For example, Intellectual Ventures I LLC v. Symantec Corp., 838 F.3d 1307, 1321 (Fed. Cir. 2016), recognizes receiving or transmitting data over a network as ordinary computer activity. Similarly, buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014), explains that a computer receiving and sending information over a network constitutes conventional computer functionality.
Likewise, Versata Development Group, Inc. v. SAP America, Inc., 793 F.3d 1306 (Fed. Cir. 2015), recognizes storing and retrieving information in memory as ordinary computer activity. Further, OIP Technologies, Inc. v. Amazon.com, Inc., 788 F.3d 1359, 1363 (Fed. Cir. 2015), explains that relying on a computer to perform routine tasks more quickly or more accurately is insufficient to transform an otherwise abstract idea into patent-eligible subject matter.
Here, claims 1, 8, 15, 22 do not require any unconventional manner of transmitting or storing the query. It merely requires that the query be “sent to a data store.” The claims do not require that the query actually be executed against the data store, that responsive data be retrieved, or that the data store perform any particular unconventional processing operation. Under the broadest reasonable interpretation, the query may simply be transmitted to or stored in the data store.
Thus, the recited transmission of the query amounts to ordinary computer functionality and does not supply an inventive concept.
Viewed individually and as an ordered combination, the additional elements merely apply the abstract idea using generic computer and machine-learning components. The ordered combination amounts to determining a base question from a user request, determining information for generating a query, generating the query in an unspecified manner, and transmitting the resulting query to a data store.
Nothing in this ordered combination recites an unconventional interaction between the machine-learning models and the data store, an improvement in the functioning of the computer, or another technological innovation. Rather, the additional elements merely automate or facilitate the mental process using ordinary computer functionality.
Accordingly, at Step 2B, the additional elements, both individually and in combination, do not amount to significantly more than the judicial exception. See MPEP § 2106.05.
Therefore, claims 1, 8, 15, 22 are not eligible subject matter under 35 U.S.C. § 101.
Claims 2, 9, 16, 23 further recites: “wherein the determining the data for generating the query comprises determining query data based on the one or more machine learning models and combining the query data with the base question.”
Claims 2, 9, 16, 23 inherit the mental process identified in the claims from which they depend on and further specifies determining query data and combining the query data with the base question. At the level of generality claimed, determining information relevant to a query and combining that information with the underlying question are themselves evaluations and organization of information that can practically be performed mentally or with pen and paper.
For example, after determining that the request concerns the top-selling products by region, a database administrator could identify the relevant information for the query and combine that information with the underlying question when formulating the query.
Claims do not specify how the machine-learning models determine or provide the query data. The machine-learning aspect is addressed as an additional element.
Claims 3, 10, 17, 24 further recites: “inputting one or more of the base question or the indication of the conditional terms to the one or more machine learning models to generate an output, and determining the data for generating the query is based on the output.” Claims 3, 10, 17, 24 inherit the mental process identified in the claims from which they depend on. The Office does not characterize the actual electronic act of inputting information into a machine-learning model or electronically generating model output as a mental process. Rather, those limitations are additional elements considered under Step 2A, Prong Two.
Claims 4, 11, 18, 25 further recites: “wherein determining the base question comprises removing the conditional terms from the data indicative of the request.”
Removing identified conditional terms from a user request to determine the underlying question is itself a mental process. A person could read the request, cross out or disregard the conditional words or phrases, and read the remaining text as the base question. This is a linguistic evaluation and editing operation that may practically be performed in the human mind or using pen and paper. Thus, claims further limits the abstract idea by another mental step.
Claims 5, 12, 19, 26 further recites: “wherein the one or more machine learning models are trained based on data from the data store.” Claims 5, 12, 19, 26 inherit the mental process identified in the claims from which they depend on. The Office does not characterize actual computerized training of a machine-learning model as a mental process. The claimed training feature is therefore considered as an additional element under Step 2A, Prong Two.
Claims 6, 13, 20, 27 further recites that the machine-learning models are trained to determine one or more of: “a specific type of query clause, a specific function of a query clause, or a specific query parameter of a query clause.” Claims 6, 13, 20, 27 inherit the mental process identified in the claims which they depend on. The actual computerized training of the models is not identified as the mental process. Rather, the training limitation and its specified subject matter are considered as additional elements below.
Claims 7, 14, 21, 28 further recites that the machine-learning models are configured to determine, for the request, one or more of: “columns of the data store, mathematical functions to apply to values of the data store, conditions to evaluate on the data store, columns to group results from the data store, ordering of results for values in a column of the data store, or a limit of a number of results from the data store.” Claims 7, 14, 21, 28 inherit the mental process identified in the claims from which they depend on. The actual operation of the machine-learning models is not identified as a mental act. However, the subject matter the models are configured to determine corresponds to information that a database administrator ordinarily evaluates when formulating a query, including what columns are needed, whether calculations are required, what conditions apply, whether records should be grouped or ordered, and how many results should be returned.
The machine-learning implementation of those determinations is considered under Prong Two. Accordingly, claims 1-28 recite the identified mental process and therefore recite an abstract idea.
Claim 15 is rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter. In view of applicant specification it is not clear if system include definitive hardware or physical components. Applicant is suggested to insert – “memory and processor” in the claim to obviate this rejection.
Claims 16-21 are also rejected under 35 U.S. C 101 because they fail to resolve the deficiencies of claim 15.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1-28 are rejected under 35 U.S.C. 103(a) as being unpatentable over Anderson et al (US 10,303,683 B2) in view of Shi et al (US 2020/0410011 A1).
As per claim 1, Anderson teaches a method comprising: determining, based on identifying conditional terms associated with data indicative of a request from a user, a base question (see col. 8, lines 42–65 and col. 9, lines 1–15; disclosing that an intelligent agent receives a natural-language query from a user, extracts entities from the query using relationship extraction, lookup tables, and regular expressions, correlates the extracted entities with generic variables, and replaces the entities with the generic variables to generate a generic query; the natural-language query corresponds to the data indicative of a request from a user, the extracted entity values correspond to the conditional terms because those values provide conditions of the resulting structured query, and the generic query corresponds to the base question; further see col. 9, lines 55–65 and FIG. 5, disclosing that the natural-language query “Can you please tell me the total spent on television in China by IBM in the last quarter of 2015?” is converted to generic query 92, “What was TV_MEDIA in REGION by COMPANY between DATE1 and DATE2?”); and causing, based on generating the query using the data for generating the query, the query to be sent to a data store (see col. 8, lines 29–35 and FIG. 2, items 54–56; disclosing translating the natural-language query into a structured data query and submitting the structured data query to a structured data system, such as a SQL database; further see col. 9, lines 37–50 and col. 10, lines 1–10, disclosing substituting specific data corresponding to the extracted entities into the structured data variables of a SQL shell to generate a final structured data query and submitting that query to the SQL system; the specific data and structured data variables correspond to the data for generating the query, and the SQL system corresponds to the data store).
Although Anderson teaches using a trained natural-language classifier to associate a generic query with a structured question type, Anderson does not explicitly teach determining, based on a base question and one or more machine-learning models, data for generating a query.
However, Shi teaches determining, based on a base question and one or more machine-learning models, data for generating a query (see paragraphs [0071]–[0074]; disclosing identifying one or more search intentions 222 in natural-language query text 220 and, after the text is matched to a search intention, applying one or more additional machine-learning models 208 to generate search parameters 224 for the vertical represented by that search intention, wherein each search parameter includes a field representing a named entity and a value for the field; the search intention 222 corresponds to the base question because it represents the underlying search objective, the machine-learning models 208 correspond to the one or more machine-learning models, and the fields and values of search parameters 224 correspond to the data for generating the query. For example, Shi maps “Who are my friends working at Company X?” to parameters including Company: Company X and Connection Type: first degree; and further see paragraphs [0015] and [0118]; disclosing converting the search parameters into a keyword query compatible with the search module for the corresponding vertical and transmitting the keyword query to obtain matching content from the data repository).
Thus, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to apply the teachings of Shi with the teachings of Anderson in order to determine Anderson’s query data using one or more machine learning models, thereby adapting extracted field-and-value parameters to the context of a particular data source, reducing ambiguity in the generated query, and reducing the computational overhead associated with reformulating and resubmitting queries (Anderson).
As per claim 2, Anderson teaches wherein that determining the data for generating the query comprises determining query data based on the one or more machine-learning models and combining the query data with the base question (col. 2, lines 5–15; col. 9, lines 15–50; and FIG. 5, items 92–96; disclosing that the natural-language classifier may use deep-learning techniques, receives generic query 92, and classifies the generic query according to structured question type 94, such as “SELECT SUM(COST) FROM SPEND_TABLE WHERE COMPANY AND COUNTRY AND DATE_RANGE”; specific data corresponding to the generic variables is then inserted into the selected structured question type to generate structured data query 96; the classifier-selected structured question type corresponds to the query data determined based on the machine-learning model, generic query 92 corresponds to the base question, and associating the generic query with the selected structured question type and inserting the specific data corresponds to combining the query data with the base question).
As per claim 3, Anderson teaches wherein inputting one or more of the base question or the indication of the conditional terms to the one or more machine-learning models to generate an output, and determining the data for generating the query based on the output (Anderson, col. 2, lines 5–15 and col. 9, lines 15–40; disclosing inputting the generic query obtained from the user’s natural-language query to a natural-language classifier that may apply deep-learning techniques, classifying the generic query according to one of multiple structured question types, and using the selected structured question type to generate the structured data query; the generic query corresponds to the base question, the selected structured question type corresponds to the model output, and the variables and SQL structure of the selected question type correspond to data for generating the query).
As per claim 4, Anderson teaches wherein determining the base question comprises removing the conditional terms from the data indicative of the request (col. 9, lines 1–15 and FIG. 3, item 68; disclosing replacing extracted entity values with generic variables so that the original specific conditional terms are no longer present in the resulting generic query; for example, “China” is removed from the resulting generic query and replaced by “REGION,” and the specific temporal condition “in the last quarter of <year>” is removed and replaced by “between DATE1 and DATE2”; the original entity values correspond to the conditional terms, and the resulting generic query corresponds to the base question). Anderson also teaches removing phrases that are extraneous to the query’s intent.
As per claim 5, Shi teaches wherein the one or more machine-learning models are trained based on data from the data store ( [0113]; disclosing that model-creation apparatus 210 creates or updates machine-learning models 208 using training data including text 212 and labels 214 obtained from data repository 134 and/or another data store; data repository 134 corresponds to the data store because it contains the entity and activity data searched using the generated search parameters).
As per claim 6, Anderson teaches wherein the one or more machine-learning models are trained to determine one or more of: a specific type of query clause, a specific function of a query clause, or a specific query parameter of a query clause (col. 9, lines 24–36 and FIG. 4, table 80; disclosing training the classifier using a ground-truth CSV file that maps example natural-language queries to structured question types or SQL shells, including shells containing expressions such as SELECT TOP 1 SUM(SPEND) GROUP BY COMPANY WHERE DATE_RANGE; the trained classifier selects the corresponding SQL shell, thereby determining at least the specific SUM function and the COMPANY and DATE_RANGE query parameters included in the selected query structure).
As per claim 7, Anderson teaches wherein the one or more machine-learning models are configured to determine, for the request, one or more of: columns of the data store, mathematical functions to apply to values of the data store, conditions to evaluate on the data store, columns by which to group results, ordering of results, or a limit on the number of results (col. 9, lines 24–36 and FIG. 4, table 80; disclosing that the trained classifier selects structured question types containing SQL expressions such as SELECT TOP 1 SUM(SPEND) GROUP BY COMPANY WHERE DATE_RANGE; SPEND corresponds to a data-store column, SUM corresponds to a mathematical function applied to values in that column, DATE_RANGE corresponds to a condition evaluated on the data store, COMPANY corresponds to a column by which results are grouped, and TOP 1 corresponds to a limit on the number of results).
Regarding claims 8, 15, 22, claims 8, 15, 22 are rejected for substantially the same reason as claim 1 above.
Regarding claims 9-14, 16-21, 23-28, claims 9-14, 16-21, 23-28 are rejected for substantially the same reason as claims 2-7 above.
It is noted that any citation [[s]] to specific, pages, columns, lines, or figures in the prior art references and any interpretation of the references should not be considered to be limiting in any wav. A reference is relevant for all it contains and may be relied upon for all that it would have reasonably suggested to one having ordinary skill in the art. [[See, MPEP 2123]].
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Mohammad A Sana whose telephone number is (571)270-1753. The examiner can normally be reached Monday-Friday 9-5.
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/Mohammad A Sana/Primary Examiner, Art Unit 2166
/SANJIV SHAH/Supervisory Patent Examiner, Art Unit 2166
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