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 . Claims 1-20 have been submitted for examination.
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 1-2 and 8-14 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.
Step 1: Claims 1-2 and 8-14 are drawn to a method, system and non-transitory computer-readable media each of which is within the four statutory categories (e.g., a process, a machine).
Step 2A - Prong One: In prong one of step 2A, the claims are analyzed to evaluate whether they recite a judicial exception.
Claim 1, 2 and 6-14.
recite(s) “obtaining… data”, sending data to language model “obtaining… large language model output”, “obtaining augmentation”, “obtaining… first resolved request data”, “obtaining…”, “obtaining… results data”, and “outputting data for presenting.”
The limitations directed towards ) “obtaining… data”, sending data to language model “obtaining… large language model output”, “obtaining augmentation”, “obtaining… first resolved request data”, “obtaining…”, “obtaining… results data”, and “outputting data for presenting.” are interpreted to be the observation or judgment a user may make and, therefore, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components. That is, other than reciting “apparatus”, “memories” and “processors” in claim 19, and “a computer readable medium” in claim 20, nothing in the claim element precludes the step from practically being performed in the mind.
The same rational applied to claims 6-14. Thus, the claims are directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
Under step 2A, Prong 2, of the 2019 Revised Guidance, 84 Fed. Reg., we determine whether any of the additional elements beyond the abstract idea integrate the abstract ideas into a practical application. 2019 Guidance, 84 Fed. Reg. 54; MPEP §§ 2106.04(d), 2106.05. The 2019 Guidance provides exemplary considerations that are indicative of an additional element or combination of elements integrating the judicial exception into a practical application, such as an additional element reflecting an improvement in the functioning of a computer or an improvement to other technology or technical field. Id. at 55; see also MPEP § 2106.05(a). This judicial exception is not integrated into a practical application by additional elements. In particular, the claim recites using a processor to perform the steps. The processor in both steps is recited at a high-level of generality such that it amounts no more than mere instructions to apply the exception using a generic computer component/ language model component.
Additionally, the claimed feature of “outputting data for presenting the results data” is merely insignificant extra-solution activity, i.e., necessary data outputting. See MPEP 2106.05(g). At step 2A, prong two, considering these limitations individually and the claim as a whole, the claim fails to integrate the abstract idea into a practical application. The elements directed to “storing” and “presenting” do not integrate the abstract idea into a practical application because they do not impose a meaningful limit on the judicial exception and provide only insignificant extra solution activity that is mere data gathering in conjunction with the abstract idea.
At Step 2B, all claim elements, with the exception of the the use of language model, correspond to concepts determined to be abstract ideas for the reasons discussed above in connection with Prong One of the analysis and/or merely constitute extra-solution activity under Prong Two. Applicant's lack of a detailed disclosure of language model or functional requirements and the lack of details describing a model-specific implementation of the recited functions (such as might have been indicated by inclusion of a detailed flow chart depicting unconventional model routines for performing each of the claimed steps), persuades us that the omitted details are well-understood, routine, and conventional. See, e.g., MPEP § 2106.07(a)(III)(A).
Consistent with the Berkheimer Memorandum, the claims merely recite generic computer components performing generic computing functions that are well-understood, routine, and conventional. 5 See Alice, 573 U.S. at 225 (The "obtaining data, analysing data and outputting data are are 'well-understood, routine, conventional activit[ies]' previously known to the industry.") ( quoting Mayo, 566 U.S. at 71-73); see also Benson, 409
MPEP § 2106.05(d)(II) (citing Alice and Mayo) accord Berkheimer Memo 3-4.
In this case, the “outputting” limitations are clearly well-understood, routine, and conventional; see MPEP 2106.05(d)(II), "receiving or transmitting data over a network." The claim(s) do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the computing of measures only add well-understood, routine and conventional activities previously known to the industry, specified at a high level of generality, to the judicial exception (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). The claims provide that the measures may be computed by program code that may be stored in memory. Therefore, the computing is nothing more than what can be handled by a conventional search engine and does not provide significantly more than the judicial exception. The claim(s) is/are not patent eligible.
The claim(s) do not include additional elements that are sufficient to amount to significantly more than the judicial exception. The claim(s) is/are not patent eligible.
The Examiner has therefore determined that the elements, or combination of additional elements, do not integrate the abstract idea into a practical application. Accordingly, the claims are directed to an abstract idea.
Double Patenting
The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the claims at issue are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); and In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on a nonstatutory double patenting ground provided the reference application or patent either is shown to be commonly owned with this application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP §§ 706.02(l)(1) - 706.02(l)(3) for applications not subject to examination under the first inventor to file provisions of the AIA . A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
The USPTO Internet website contains terminal disclaimer forms which may be used. Please visit www.uspto.gov/forms/. The filing date of the application in which the form is filed determines what form (e.g., PTO/SB/25, PTO/SB/26, PTO/AIA /25, or PTO/AIA /26) should be used. A web-based eTerminal Disclaimer may be filled out completely online using web-screens. An eTerminal Disclaimer that meets all requirements is auto-processed and approved immediately upon submission. For more information about eTerminal Disclaimers, refer to http://www.uspto.gov/patents/process/file/efs/guidance/eTD-info-I.jsp.
Claims 1, 2 and 8 are rejected on the ground of nonstatutory double patenting as being unpatentable over claim 1 of U.S. Patent No 12331906. Although the claims at issue are not identical, they are not patentably distinct from each other.
Claims 1-20 provisionally rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1, 5, 10, 14 and 15 of copending Application No. 18970752. This is a provisional nonstatutory double patenting rejection.
Claim 1 provisionally rejected on the ground of nonstatutory double patenting as being unpatentable over claim 1 of copending Application No. 18442193. This is a provisional nonstatutory double patenting rejection.
Claim 1 provisionally rejected on the ground of nonstatutory double patenting as being unpatentable over claim 1 of copending Application No. 19064943. This is a provisional nonstatutory double patenting rejection.
Claims 1, 2 and 8 provisionally rejected on the ground of nonstatutory double patenting as being unpatentable over claim 1 of copending Application No. 18587079. This is a provisional nonstatutory double patenting rejection.
Claim Rejections - 35 USC § 102
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claims 1-2 and 6-14 are rejected under 35 U.S.C. 102(a)(1) as being anticipant by Kulkarni et al (hereinafter Kulkarni) US Publication No 20240303235.
As per claim 1, Kulkarni teaches:
A method comprising:
obtaining, by a data access and analysis system, natural language input data expressing an input analytical request related to data stored in a database accessible by the data access and analysis system;
(Abstract and paragraphs [0004]-[0006], [0018]-[0021], [0021], [0121], [0154] and [0212]-[0213] and [0216]-[0217] and [0248])
obtaining first language model input data in accordance with the natural language input data;
(Abstract and paragraphs [0004]-[0006], [0018]-[0021], [0021], [0121], [0154] and [0212]-[0213] and [0216]-[0217] and [0248])
sending the first language model input data to a language model;
(Abstract and paragraphs [0004]-[0006], [0018]-[0021], [0021], [0121], [0154] and [0212]-[0213] and [0216]-[0217] and [0248])
obtaining, by the data access and analysis system, from the language model, responsive to the first language model input data, first language model generated output, wherein the first language model generated output includes first one or more language model generated natural language analytical requests;
(Abstract and paragraphs [0004]-[0006], [0018]-[0021], [0021], [0121], [0154] and [0212]-[0213] and [0216]-[0217] and [0248])
obtaining, by the data access and analysis system, in accordance with the first language model generated output, first augmentation data including:
first results data, wherein, for a respective language model generated natural language analytical request from the first one or more language model generated natural language analytical requests, the first results data includes first corresponding results- data-frame data;
(Paragraphs [0020], [0130], [0254] and [0257])
and first resolved-requests data, wherein, for a respective language model generated natural language analytical request from the first one or more language model generated natural language analytical requests, the first resolved-requests data includes first corresponding resolved-request data generated, by the data access and analysis system, in accordance with a defined data-analytics grammar implemented by the data access and analysis system;
(Abstract and paragraphs [0004]-[0006], [0112]-[0115], [0148] and [0253]-[0254])
obtaining second language model input data by including, by the data access and analysis system, in the second language model input data:
the first augmentation data;
(Paragraphs [0226], [0229], [0316], [0429])
the first language model input data;
(Paragraphs [0226], [0229], [0316], [0429])
and one or more second task description strings;
(Paragraphs [0226], [0229], [0316], [0429])
sending the second language model input data to the language model;
(Paragraphs [0037]-[0038], [0293])
obtaining, by the data access and analysis system, from the language model, responsive to the second language model input data, second language model generated output, wherein the second language model generated output includes second one or more language model generated natural language analytical requests;
(Paragraphs [0037]-[0038], [0293])
obtaining, by the data access and analysis system, in accordance with the second language model generated output, second augmentation data including:
second results data, wherein, for a respective language model generated natural language analytical request from the second one or more language model generated natural language analytical requests, the second results data includes second corresponding results-data-frame data;
(Paragraphs [0037]-[0038], [0293] and [0405]-[0407] and [0429])
and second resolved-requests data, wherein, for a respective language model generated natural language analytical request from the second one or more language model generated natural language analytical requests, the second resolved-requests data includes second corresponding resolved-request data generated, by the data access and analysis system, in accordance with the defined data-analytics grammar;
(Paragraphs [0004]-[0006] and [0019], [0251], [0255], [0272] and [0336])
obtaining third language model input data by including, by the data access and analysis system, in the third language model input data:
the second augmentation data;
(Paragraphs [0226], [0229], [0316], [0429])
the second language model input data;
(Paragraphs [0226], [0229], [0316], [0429])
and third task description data;
(Paragraphs [0226], [0229], [0316], [0429])
sending the third language model input data to the language model;
(Abstract and paragraphs [0004]-[0006], [0018]-[0021], [0021], [0121], [0154] and [0212]-[0213] and [0216]-[0217] and [0248])
obtaining, by the data access and analysis system, from the language model, responsive to the third language model input data, third language model generated output responsive to the natural language input data;
(Abstract and paragraphs [0004]-[0006], [0018]-[0021], [0021], [0121], [0154] and [0212]-[0213] and [0216]-[0217] and [0248])
and outputting data for presenting at least a portion of the third language model generated output.
(Abstract and paragraphs [0004]-[0006], [0383] and [0416])
As per claim 2, Kulkarni teaches:
A method comprising:
obtaining, by a data access and analysis system, natural language input data expressing an input analytical request related to data stored in a database accessible by the data access and analysis system;
(Abstract and paragraphs [0004]-[0006], [0018]-[0021], [0021], [0121], [0154] and [0212]-[0213] and [0216]-[0217] and [0248])
three or more iterations of agentic analysis, wherein agentic analysis includes:
obtaining first language model input data in accordance with the natural language input data, wherein obtaining the first language model input data comprises including, in the first language model input data, one or more iteration-specific task description strings;
(Paragraphs [0226], [0229], [0316], [0423], [0429] and [0431])
sending the first language model input data to a language model;
(Abstract and paragraphs [0004]-[0006], [0018]-[0021], [0021], [0121], [0154] and [0212]-[0213] and [0216]-[0217] and [0248])
and obtaining, by the data access and analysis system, from the language model, responsive to the first language model input data, first language model generated output, wherein, in iterations other than a sequentially last agentic analysis output iteration:
the first language model generated output includes first one or more language model generated natural language analytical requests;
(Paragraphs [0226], [0229], [0316], [0423], [0429] and [0431])
and agentic analysis includes:
obtaining, by the data access and analysis system, in accordance with the first language model generated output, augmentation data including:
first results data, wherein, for a respective language model generated natural language analytical request from the first one or more language model generated natural language analytical requests, the first results data includes first corresponding results- data-frame data;
(Paragraphs [0019]-[0021], [0109]-[0124], [0139]-[0141] and [0157]-[0162])
and first resolved-requests data, wherein, for a respective language model generated natural language analytical request from the first one or more language model generated natural language analytical requests, the first resolved-requests data includes first corresponding resolved-request data generated, by the data access and analysis system, in accordance with a defined data-analytics grammar implemented by the data access and analysis system;
(Paragraphs [0019]-[0021], [0109]-[0111], [0121]-[0124] and [0157]-[0162])
wherein, in iterations other than a sequentially first input iteration, obtaining the first language model input data comprises including, in the first language model input data: the first language model input data corresponding to an immediately preceding iteration;
(Paragraphs [0019]-[0021], [0109]-[0111], [0121]-[0124] and [0157]-[0162])
and the augmentation data corresponding to the immediately preceding iteration;
(Paragraphs [0019]-[0021], [0109]-[0111], [0121]-[0124] and [0157]-[0162])
and outputting data for presenting at least a portion of the first language model generated output corresponding to the sequentially last agentic analysis output iteration.
(Abstract and paragraphs [0004]-[0006], [0383] and [0416])
As per claim 6, Kulkarni teaches:
The method of claim 2, wherein outputting data includes:
generating, by the data access and analysis system, a first analytical object as an internal representation of a liveboard;
(Paragraphs [0079], [0085], [0093], [0130], [0176] and [0192])
including in the liveboard:
language model generated summarization data responsive to the input analytical request and obtained from the first language model generated output;
(Paragraphs [0079], [0085], [0093], [0130], [0176] and [0192])
and data for presenting one or more data visualizations, wherein the one or more data visualizations include one or more of:
a first data visualization representing language model generated results data responsive to the input analytical request, the language model generated results data generated by the language model using the first language model input data, wherein the language model generated summarization data summarizes the language model generated results data; or one or more second data visualizations, wherein the first results data includes a first plurality of results-data-frames, wherein a first results-data-frame from the first plurality of results-data-frames includes the first corresponding results-data-frame data, and wherein a respective second data visualization from the one or more second data visualizations represents a first respective results- data-frame from the first plurality of results-data-frames; and outputting data for presenting at least a portion of the liveboard.
(Paragraphs [0079], [0085], [0093], [0130], [0176] and [0192])
As per claim 7, Kulkarni teaches:
The method of claim 6, wherein outputting data includes:
generating, by the data access and analysis system, the data for presenting the one or more data visualizations, wherein generating the data for presenting the one or more data visualizations includes:
generating, by the data access and analysis system, a second analytical object as an internal representation of the language model generated results data;
(Paragraphs [0079], [0085], [0093], [0130], [0176] and [0192])
and generating, by the data access and analysis system, a second analytical object as an internal representation of a resolved request corresponding to a data frame.
(Paragraphs [0019], [0021], [0093] and [0164])
As per claim 8, Kulkarni teaches:
A method comprising:
obtaining, by a data access and analysis system, natural language input data expressing an input analytical request related to data stored in a database accessible by the data access and analysis system;
(Abstract and paragraphs [0004]-[0006], [0018]-[0021], [0021], [0121], [0154] and [0212]-[0213] and [0216]-[0217] and [0248])
performing iterations of agentic analysis, wherein agentic analysis includes obtaining, by the data access and analysis system, from a language model, responsive to first language model input data generated by the data access and analysis system in accordance with the natural language input data, first language model generated output, wherein, at least one iteration includes obtaining, by the data access and analysis system, in accordance with the first language model generated output, structured data generated by the database executing a data query automatically generated by the data access and analysis system in response to the first language model generated output;
(Abstract and paragraphs [0004]-[0006], [0018]-[0021], [0021], [0121], [0154] and [0212]-[0213] and [0216]-[0217] and [0248])
and outputting data for presenting at least a portion of the first language model generated output corresponding to a sequentially last iteration.
(Abstract and paragraphs [0004]-[0006], [0079], [0085], [0093], [0130], [0176], [0192], [0383] and [0416])
As per claim 9, Kulkarni teaches:
The method of claim 8, wherein performing iterations of agentic analysis includes performing three or more iterations of agentic analysis.
(Abstract and paragraphs [0004]-[0006], [0018]-[0021], [0021], [0121], [0154] and [0212]-[0213] and [0216]-[0217] and [0248])
As per claim 10, Kulkarni teaches:
The method of claim 8, wherein agentic analysis includes:
obtaining the first language model input data in accordance with the natural language input data, wherein obtaining the first language model input data comprises including, in the first language model input data, one or more iteration-specific task description strings.
(Abstract and paragraphs [0004]-[0006], [0018]-[0021], [0021], [0121], [0154] and [0212]-[0213] and [0216]-[0217] and [0248])
As per claim 11, Kulkarni teaches:
The method of claim 8, wherein agentic analysis includes:
obtaining the structured data in response to determining that the first language model generated output includes first one or more language model generated natural language analytical requests.
(Abstract and paragraphs [0004]-[0006], [0079], [0085], [0093], [0130], [0176], [0192], [0383] and [0416])
As per claim 12, Kulkarni teaches
The method of claim 11, wherein obtaining the structured data includes:
obtaining first results data, wherein, for a respective language model generated natural language analytical request from the first one or more language model generated natural language analytical requests, the first results data includes first corresponding results-data-frame data.
(Abstract and paragraphs [0004]-[0006], [0018]-[0021], [0021], [0121], [0154] and [0212]-[0213] and [0216]-[0217] and [0248])
As per claim 13, Kulkarni teaches
The method of claim 12, wherein obtaining the structured data includes:
obtaining first resolved-requests data, wherein, for a respective language model generated natural language analytical request from the first one or more language model generated natural language analytical requests, the first resolved-requests data includes first corresponding resolved-request data generated, by the data access and analysis system, in accordance with a defined data-analytics grammar implemented by the data access and analysis system.
(Abstract and paragraphs [0004]-[0006], [0018]-[0021], [0021], [0121], [0154] and [0212]-[0213] and [0216]-[0217] and [0248])
As per claim 14, Kulkarni teaches
The method of claim 13, wherein, in iterations other than a sequentially first iteration, obtaining the first language model input data comprises including, in the first language model input data: the first language model input data corresponding to an immediately preceding iteration;
(Abstract and paragraphs [0004]-[0006], [0018]-[0021], [0021], [0121], [0154] and [0212]-[0213] and [0216]-[0217] and [0248])
and the structured data corresponding to the immediately preceding iteration.
(Abstract and paragraphs [0004]-[0006], [0018]-[0021], [0021], [0121], [0154] and [0212]-[0213] and [0216]-[0217] and [0248])
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 3-5 and 15-20 are rejected under 35 U.S.C. 103(a) as being unpatentable over Kulkarni in view of Ghatage et al (hereinafter Ghatage) US Patent No. 12681942.
As per claim 3, Kulkarni teaches:
The method of claim 2, wherein, in the sequentially first input iteration, obtaining the first language model input data includes:
obtaining, by the data access and analysis system, first data for the natural language input data;
(Abstract and paragraphs [0004]-[0006], [0018]-[0021], [0021], [0121], [0154] and [0212]-[0213] and [0216]-[0217] and [0248])
obtaining, by the data access and analysis system, prompting template data;
(Abstract and paragraphs [0004]-[0006], [0018]-[0021], [0021], [0121], [0154] and [0212]-[0213] and [0216]-[0217] and [0248])
and obtaining the first language model input data by including, by the data access and analysis system, in the first language model input data:
a first plurality of column identifiers;
(Paragraphs [0059], [0110]-[0111] and [0147])
a first plurality of data values obtained in accordance with the first plurality of column identifiers;
(Paragraphs [0059], [0110]-[0111] and [0147])
contextual data;
(Paragraphs [0004]-[0006] and [0086])
ontological data;
(Paragraphs [0050], [0060], [0063]-[0064] and [0108])
a first task description string;
(Paragraphs [0226], [0229], [0316], [0429])
the natural language input data;
(Abstract and paragraphs [0004]-[0006], [0018]-[0021], [0021], [0121], [0154] and [0212]-[0213] and [0216]-[0217] and [0248])
and a defined cardinality of demonstrations from the prompting data.
(Paragraphs [0042], [0059]-[0060] and [0159])
Kulkarni does not explicitly teach obtaining, by the data access and analysis system, first embeddings data for the natural language input data, however in analogous art of data retrieval Ghatage teaches:
obtaining, by the data access and analysis system, first embeddings data for the natural language input data;
(Abstract and Column 10, lines 63-67 and Column 11, lines 1-22, Column 23, lines 64-67 and Column 24, lines 1-9 and Column 30, lines 9-58)
obtaining, by the data access and analysis system, in accordance with the first embeddings data, ranked list data;
(Abstract and Column 10, lines 63-67 and Column 11, lines 1-22, Column 23, lines 64-67 and Column 24, lines 1-9 and Column 30, lines 9-58)
obtaining, by the data access and analysis system, in accordance with the ranked list data and the prompting template data, prompting data;
(Abstract and Column 10, lines 63-67 and Column 11, lines 1-22, Column 23, lines 64-67 and Column 24, lines 1-9 and Column 30, lines 9-58)
obtaining, by the data access and analysis system, second embeddings data for the prompting data;
(Abstract and Column 10, lines 63-67 and Column 11, lines 1-22, Column 23, lines 64-67 and Column 24, lines 1-9 and Column 30, lines 9-58)
Therefore, it would have been obvious to a person in the ordinary skill in the art at the time of the filling of the invention to combine Kulkarni and Ghatage by incorporating the teaching of Ghatage into the method of Kulkarni. One having ordinary skill in the art would have found it motivated to use the content management of Ghatage into the system of Kulkarni for the purpose improving search efficiency.
As per claim 4, Kulkarni teaches:
The method of claim 2, wherein obtaining the augmentation data includes obtaining first augmentation data, wherein obtaining the first augmentation data includes:
on per-request basis with respect to the first one or more language model generated natural language analytical requests:
obtaining fourth language model input data by including, by the data access and analysis system, in the fourth language model input data, the current language model generated natural language analytical request and a defined cardinality of demonstrations from the candidate prompting data in descending score order with respect to the score data;
(Paragraphs [0141], [0152] and [0158], [0160], [0162], [0231] and [0239])
sending the fourth language model input data to the language model;
(Abstract and paragraphs [0004]-[0006], [0018]-[0021], [0021], [0121], [0154] and [0212]-[0213] and [0216]-[0217] and [0248])
obtaining, by the data access and analysis system, from the language model, second language model generated output;
(Abstract and paragraphs [0004]-[0006], [0018]-[0021], [0021], [0121], [0154] and [0212]-[0213] and [0216]-[0217] and [0248])
generating, by the data access and analysis system transforming the second language model generated output, the first corresponding resolved-request data expressing the current language model generated natural language analytical request in accordance with the defined data-analytics grammar;
(Paragraphs [0004]-[0006] and [0019], [0251], [0255], [0272] and [0336])
including the first corresponding resolved-request data in the first resolved- requests data; obtaining, by the data access and analysis system transforming the first corresponding resolved-request data, a first data query expressing the current language model generated natural language analytical request in accordance with a defined structured query language implemented by the database; obtaining, by the data access and analysis system, from the database, the first corresponding results-data-frame data responsive to the current language model generated natural language analytical request, the first corresponding results-data-frame data generated by execution of the first data query by the database;
(Paragraphs [0004]-[0006] and [0019], [0251], [0255], [0272] and [0336])
and including the first corresponding results-data-frame data in the first results data.
(Paragraphs [0004]-[0006] and [0019], [0251], [0255], [0272] and [0336])
Kulkarni does not explicitly teach obtaining, by the data access and analysis system, first embeddings data for the natural language input data, however in analogous art of data retrieval Ghatage teaches:
obtaining, by the data access and analysis system, first embeddings data for a current language model generated natural language analytical request from the first one or more language model generated natural language analytical requests;
(Abstract and Column 10, lines 63-67 and Column 11, lines 1-22, Column 23, lines 64-67 and Column 24, lines 1-9 and Column 30, lines 9-58)
obtaining, by the data access and analysis system, first data for a current language model generated natural language analytical request from the first one or more language model generated natural language analytical requests;
(Abstract and Column 10, lines 63-67 and Column 11, lines 1-22, Column 23, lines 64-67 and Column 24, lines 1-9 and Column 30, lines 9-58)
obtaining, by the data access and analysis system, prompting template data;
(Abstract and Column 10, lines 63-67 and Column 11, lines 1-22, Column 23, lines 64-67 and Column 24, lines 1-9 and Column 30, lines 9-58)
obtaining, by the data access and analysis system, in accordance with the first embeddings data, ranked list data;
(Abstract and Column 10, lines 63-67 and Column 11, lines 1-22, Column 23, lines 64-67 and Column 24, lines 1-9 and Column 30, lines 9-58)
obtaining, by the data access and analysis system, in accordance with the ranked list data and the prompting template data, candidate prompting data;
(Abstract and Column 10, lines 63-67 and Column 11, lines 1-22, Column 23, lines 64-67 and Column 24, lines 1-9 and Column 30, lines 9-58)
obtaining, by the data access and analysis system, second embeddings data for the candidate prompting data;
(Abstract and Column 10, lines 63-67 and Column 11, lines 1-22, Column 23, lines 64-67 and Column 24, lines 1-9 and Column 30, lines 9-58)
obtaining, by the data access and analysis system, score data indicating similarity between the first embeddings data and the second embeddings data;
(Abstract and Column 10, lines 63-67 and Column 11, lines 1-22, Column 23, lines 64-67 and Column 24, lines 1-9 and Column 30, lines 9-58)
Therefore, it would have been obvious to a person in the ordinary skill in the art at the time of the filling of the invention to combine Kulkarni and Ghatage by incorporating the teaching of Ghatage into the method of Kulkarni. One having ordinary skill in the art would have found it motivated to use the content management of Ghatage into the system of Kulkarni for the purpose improving search efficiency.
As per claim 5, Kulkarni teaches:
The method of claim 2, wherein obtaining the augmentation data includes obtaining second augmentation data, wherein obtaining the second augmentation data includes:
on per-request basis with respect to second one or more language model generated natural language analytical requests:
obtaining fourth language model input data by including, by the data access and analysis system, in the fourth language model input data, the current language model generated natural language analytical request and a defined cardinality of demonstrations from the candidate prompting data in descending score order with respect to the score data;
(Abstract and paragraphs [0004]-[0006], [0018]-[0021], [0021], [0121], [0154] and [0212]-[0213] and [0216]-[0217] and [0248])
sending the fourth language model input data to the language model;
(Abstract and paragraphs [0004]-[0006], [0018]-[0021], [0021], [0121], [0154] and [0212]-[0213] and [0216]-[0217] and [0248])
obtaining, by the data access and analysis system, from the language model,
second language model generated output; generating, by the data access and analysis system transforming the second language model generated output, second corresponding resolved-request data expressing the current language model generated natural language analytical request in accordance with the defined data-analytics grammar;
(Abstract and paragraphs [0004]-[0006], [0018]-[0021], [0021], [0121], [0154] and [0212]-[0213] and [0216]-[0217] and [0248])
obtaining, by the data access and analysis system transforming the second corresponding resolved-request data, a first data query expressing the current language model generated natural language analytical request in accordance with a defined structured query language implemented by the database;
(Abstract and paragraphs [0004]-[0006], [0018]-[0021], [0021], [0121], [0154] and [0212]-[0213] and [0216]-[0217] and [0248])
obtaining, by the data access and analysis system, from the database, second corresponding results-data-frame data responsive to the current language model generated natural language analytical request, the second corresponding results-data- frame data generated by execution of the first data query by the database;
(Abstract and paragraphs [0004]-[0006], [0018]-[0021], [0021], [0121], [0154] and [0212]-[0213] and [0216]-[0217] and [0248])
and including the second corresponding results-data-frame data in second results data.
(Abstract and paragraphs [0004]-[0006], [0018]-[0021], [0021], [0121], [0154] and [0212]-[0213] and [0216]-[0217] and [0248])
Kulkarni does not explicitly teach obtaining, by the data access and analysis system, first embeddings data for the natural language input data, however in analogous art of data retrieval Ghatage teaches:
obtaining, by the data access and analysis system, first embeddings data for a current language model generated natural language analytical request from the second one or more language model generated natural language analytical requests;
(Abstract and Column 10, lines 63-67 and Column 11, lines 1-22, Column 23, lines 64-67 and Column 24, lines 1-9 and Column 30, lines 9-58)
obtaining, by the data access and analysis system, prompting template data;
(Abstract and Column 10, lines 63-67 and Column 11, lines 1-22, Column 23, lines 64-67 and Column 24, lines 1-9 and Column 30, lines 9-58)
obtaining, by the data access and analysis system, in accordance with the first embeddings data, ranked list data;
(Abstract and Column 10, lines 63-67 and Column 11, lines 1-22, Column 23, lines 64-67 and Column 24, lines 1-9 and Column 30, lines 9-58)
obtaining, by the data access and analysis system, in accordance with the ranked list data and the prompting template data, candidate prompting data;
(Abstract and Column 10, lines 63-67 and Column 11, lines 1-22, Column 23, lines 64-67 and Column 24, lines 1-9 and Column 30, lines 9-58)
obtaining, by the data access and analysis system, second embeddings data for the candidate prompting data;
(Abstract and Column 10, lines 63-67 and Column 11, lines 1-22, Column 23, lines 64-67 and Column 24, lines 1-9 and Column 30, lines 9-58)
obtaining, by the data access and analysis system, score data indicating similarity between the first embeddings data and the second embeddings data;
(Abstract and Column 10, lines 63-67 and Column 11, lines 1-22, Column 23, lines 64-67 and Column 24, lines 1-9 and Column 30, lines 9-58)
Therefore, it would have been obvious to a person in the ordinary skill in the art at the time of the filling of the invention to combine Kulkarni and Ghatage by incorporating the teaching of Ghatage into the method of Kulkarni. One having ordinary skill in the art would have found it motivated to use the content management of Ghatage into the system of Kulkarni for the purpose improving search efficiency.
As per claim 15, Kulkarni teaches:
15. The method of claim 14, wherein, in the sequentially first iteration, obtaining the first language model input data includes:
and obtaining the first language model input data by including, by the data access and analysis system, in the first language model input data:
a first plurality of column identifiers;
(Paragraphs [0059], [0110]-[0111] and [0147])
a first plurality of data values obtained in accordance with the first plurality of column identifiers;
(Paragraphs [0059], [0110]-[0111] and [0147])
contextual data;
(Paragraphs [0004]-[0006] and [0086])
ontological data;
(Paragraphs [0050], [0060], [0063]-[0064] and [0108])
a first task description string;
(Paragraphs [0226], [0229], [0316], [0429])
the natural language input data;
(Abstract and paragraphs [0004]-[0006], [0018]-[0021], [0021], [0121], [0154] and [0212]-[0213] and [0216]-[0217] and [0248])
and a defined cardinality of demonstrations from the prompting data.
(Paragraphs [0042], [0059]-[0060] and [0159])
Kulkarni does not explicitly teach obtaining, by the data access and analysis system, first embeddings data for the natural language input data, however in analogous art of data retrieval Ghatage teaches:
obtaining, by the data access and analysis system, first embeddings data for the natural language input data;
(Abstract and Column 10, lines 63-67 and Column 11, lines 1-22, Column 23, lines 64-67 and Column 24, lines 1-9 and Column 30, lines 9-58)
obtaining, by the data access and analysis system, prompting template data;
(Abstract and Column 10, lines 63-67 and Column 11, lines 1-22, Column 23, lines 64-67 and Column 24, lines 1-9 and Column 30, lines 9-58)
obtaining, by the data access and analysis system, in accordance with the first embeddings data, ranked list data;
(Abstract and Column 10, lines 63-67 and Column 11, lines 1-22, Column 23, lines 64-67 and Column 24, lines 1-9 and Column 30, lines 9-58)
obtaining, by the data access and analysis system, in accordance with the ranked list data and the prompting template data, prompting data;
(Abstract and Column 10, lines 63-67 and Column 11, lines 1-22, Column 23, lines 64-67 and Column 24, lines 1-9 and Column 30, lines 9-58)
obtaining, by the data access and analysis system, second embeddings data for the prompting data;
(Abstract and Column 10, lines 63-67 and Column 11, lines 1-22, Column 23, lines 64-67 and Column 24, lines 1-9 and Column 30, lines 9-58)
Therefore, it would have been obvious to a person in the ordinary skill in the art at the time of the filling of the invention to combine Kulkarni and Ghatage by incorporating the teaching of Ghatage into the method of Kulkarni. One having ordinary skill in the art would have found it motivated to use the content management of Ghatage into the system of Kulkarni for the purpose improving search efficiency.
As per claim 16, Kulkarni teaches:
The method of claim 13, wherein obtaining the structured data includes obtaining first structured data, wherein obtaining the first structured data includes:
on per-request basis with respect to the first one or more language model generated natural language analytical requests:
obtaining fourth language model input data by including, by the data access and analysis system, in the fourth language model input data, the current language model generated natural language analytical request and a defined cardinality of demonstrations from the candidate prompting data in descending score order with respect to the score data;
(Abstract and paragraphs [0004]-[0006], [0018]-[0021], [0021], [0121], [0154] and [0212]-[0213] and [0216]-[0217] and [0248])
sending the fourth language model input data to the language model;
(Abstract and paragraphs [0004]-[0006], [0018]-[0021], [0021], [0121], [0154] and [0212]-[0213] and [0216]-[0217] and [0248])
obtaining, by the data access and analysis system, from the language model, second language model generated output;
(Abstract and paragraphs [0004]-[0006], [0018]-[0021], [0021], [0121], [0154] and [0212]-[0213] and [0216]-[0217] and [0248])
generating, by the data access and analysis system transforming the second language model generated output, first corresponding resolved-request data expressing the current language model generated natural language analytical request in accordance with the defined data-analytics grammar implemented by the data access and analysis system;
(Abstract and paragraphs [0004]-[0006], [0018]-[0021], [0021], [0121], [0154] and [0212]-[0213] and [0216]-[0217] and [0248])
including the first corresponding resolved-request data in the first resolved- requests data; obtaining, by the data access and analysis system transforming the first corresponding resolved-request data, a first data query expressing the current language model generated natural language analytical request in accordance with a defined structured query language implemented by the database;
(Abstract and paragraphs [0004]-[0006], [0018]-[0021], [0021], [0121], [0154] and [0212]-[0213] and [0216]-[0217] and [0248])
obtaining, by the data access and analysis system, from the database, the first corresponding results-data-frame data responsive to the current language model generated natural language analytical request, the first corresponding results-data-frame data generated by execution of the first data query by the database;
(Abstract and paragraphs [0004]-[0006], [0018]-[0021], [0021], [0121], [0154] and [0212]-[0213] and [0216]-[0217] and [0248])
and including the first corresponding results-data-frame data in the first results data.
(Abstract and paragraphs [0004]-[0006], [0018]-[0021], [0021], [0121], [0154] and [0212]-[0213] and [0216]-[0217] and [0248])
Kulkarni does not explicitly teach obtaining, by the data access and analysis system, first embeddings data for the natural language input data, however in analogous art of data retrieval Ghatage teaches:
obtaining, by the data access and analysis system, first embeddings data for a current language model generated natural language analytical request from the first one or more language model generated natural language analytical requests;
(Abstract and Column 10, lines 63-67 and Column 11, lines 1-22, Column 23, lines 64-67 and Column 24, lines 1-9 and Column 30, lines 9-58)
obtaining, by the data access and analysis system, prompting template data; obtaining, by the data access and analysis system, in accordance with the first embeddings data, ranked list data; obtaining, by the data access and analysis system, in accordance with the ranked list data and the prompting template data, candidate prompting data;
(Abstract and Column 10, lines 63-67 and Column 11, lines 1-22, Column 23, lines 64-67 and Column 24, lines 1-9 and Column 30, lines 9-58)
obtaining, by the data access and analysis system, second embeddings data for the candidate prompting data; obtaining, by the data access and analysis system, score data indicating similarity between the first embeddings data and the second embeddings data;
(Abstract and Column 10, lines 63-67 and Column 11, lines 1-22, Column 23, lines 64-67 and Column 24, lines 1-9 and Column 30, lines 9-58)
Therefore, it would have been obvious to a person in the ordinary skill in the art at the time of the filling of the invention to combine Kulkarni and Ghatage by incorporating the teaching of Ghatage into the method of Kulkarni. One having ordinary skill in the art would have found it motivated to use the content management of Ghatage into the system of Kulkarni for the purpose improving search efficiency.
As per claim 17, Kulkarni teaches:
The method of claim 13, wherein obtaining the structured data includes obtaining second structured data, wherein obtaining the second structured data includes:
on per-request basis with respect to second one or more language model generated natural language analytical requests:
(Abstract and paragraphs [0004]-[0006], [0018]-[0021], [0021], [0121], [0154] and [0212]-[0213] and [0216]-[0217] and [0248])
obtaining fourth language model input data by including, by the data access and analysis system, in the fourth language model input data, the current language model generated natural language analytical request and a defined cardinality of demonstrations from the candidate prompting data in descending score order with respect to the score data;
(Abstract and paragraphs [0004]-[0006], [0018]-[0021], [0021], [0121], [0154] and [0212]-[0213] and [0216]-[0217] and [0248])
sending the fourth language model input data to the language model;
(Abstract and paragraphs [0004]-[0006], [0018]-[0021], [0021], [0121], [0154] and [0212]-[0213] and [0216]-[0217] and [0248])
obtaining, by the data access and analysis system, from the language model,
second language model generated output;
(Abstract and paragraphs [0004]-[0006], [0018]-[0021], [0021], [0121], [0154] and [0212]-[0213] and [0216]-[0217] and [0248])
generating, by the data access and analysis system transforming the second language model generated output, second corresponding resolved-request data expressing the current language model generated natural language analytical request in accordance with the defined data-analytics grammar; including the second corresponding resolved-request data in second resolved- requests data;
(Abstract and paragraphs [0004]-[0006], [0018]-[0021], [0021], [0121], [0154] and [0212]-[0213] and [0216]-[0217] and [0248])
obtaining, by the data access and analysis system transforming the second corresponding resolved-request data, a first data query expressing the current language model generated natural language analytical request in accordance with a defined structured query language implemented by the database;
(Abstract and paragraphs [0004]-[0006], [0018]-[0021], [0021], [0121], [0154] and [0212]-[0213] and [0216]-[0217] and [0248])
and obtaining, by the data access and analysis system, from the database, second corresponding results-data-frame data responsive to the current language model generated natural language analytical request, the second corresponding results-data- frame data generated by execution of the first data query by the database;
(Abstract and paragraphs [0004]-[0006], [0018]-[0021], [0021], [0121], [0154] and [0212]-[0213] and [0216]-[0217] and [0248])
and including the second corresponding results-data-frame data in second results data.
(Abstract and paragraphs [0004]-[0006], [0018]-[0021], [0021], [0121], [0154] and [0212]-[0213] and [0216]-[0217] and [0248])
Kulkarni does not explicitly teach obtaining, by the data access and analysis system, first embeddings data for the natural language input data, however in analogous art of data retrieval Ghatage teaches:
obtaining, by the data access and analysis system, first embeddings data for a current language model generated natural language analytical request from the second one or more language model generated natural language analytical requests;
(Abstract and Column 10, lines 63-67 and Column 11, lines 1-22, Column 23, lines 64-67 and Column 24, lines 1-9 and Column 30, lines 9-58)
obtaining, by the data access and analysis system, prompting template data;
(Abstract and Column 10, lines 63-67 and Column 11, lines 1-22, Column 23, lines 64-67 and Column 24, lines 1-9 and Column 30, lines 9-58)
obtaining, by the data access and analysis system, in accordance with the first embeddings data, ranked list data;
(Abstract and Column 10, lines 63-67 and Column 11, lines 1-22, Column 23, lines 64-67 and Column 24, lines 1-9 and Column 30, lines 9-58)
obtaining, by the data access and analysis system, in accordance with the ranked list data and the prompting template data, candidate prompting data;
(Abstract and Column 10, lines 63-67 and Column 11, lines 1-22, Column 23, lines 64-67 and Column 24, lines 1-9 and Column 30, lines 9-58)
obtaining, by the data access and analysis system, second embeddings data for the candidate prompting data;
(Abstract and Column 10, lines 63-67 and Column 11, lines 1-22, Column 23, lines 64-67 and Column 24, lines 1-9 and Column 30, lines 9-58)
obtaining, by the data access and analysis system, score data indicating similarity between the first embeddings data and the second embeddings data;
(Abstract and Column 10, lines 63-67 and Column 11, lines 1-22, Column 23, lines 64-67 and Column 24, lines 1-9 and Column 30, lines 9-58)
Therefore, it would have been obvious to a person in the ordinary skill in the art at the time of the filling of the invention to combine Kulkarni and Ghatage by incorporating the teaching of Ghatage into the method of Kulkarni. One having ordinary skill in the art would have found it motivated to use the content management of Ghatage into the system of Kulkarni for the purpose improving search efficiency.
As per claim 18, Kulkarni and Ghatage teach:
The method of claim 17, wherein outputting data includes:
generating, by the data access and analysis system, a first analytical object as an internal representation of a liveboard;
(Paragraphs [0079], [0085], [0093], [0130], [0176] and [0192])( Kulkarni)
including in the liveboard:
language model generated summarization data responsive to the input analytical request and obtained from the first language model generated output;
and data for presenting one or more data visualizations, wherein the one or more data visualizations include one or more of:
a first data visualization representing language model generated results data responsive to the input analytical request, the language model generated results data generated by the language model using the first language model input data, wherein the language model generated summarization data summarizes the language model generated results data;
(Paragraphs [0079], [0085], [0093], [0130], [0176] and [0192])( Kulkarni)
one or more second data visualizations, wherein the first results data includes a first plurality of results-data-frames, wherein a first results-data-frame from the first plurality of results-data-frames includes the first corresponding results-data-frame data, and wherein a respective second data visualization from the one or more second data visualizations represents a first respective results- data-frame from the first plurality of results-data-frames; or one or more third data visualizations, wherein the second results data includes a second plurality of results-data-frames, wherein a second results-data- frame from the second plurality of results-data-frames includes the second corresponding results-data-frame data, and wherein a respective third data visualization from the one or more third data visualizations represents a second respective results-data-frame from the second plurality of results-data-frames; and outputting data for presenting at least a portion of the liveboard.
(Paragraphs [0079], [0085], [0093], [0130], [0176] and [0192])( Kulkarni)
As per claim 19, Kulkarni and Ghatage teach:
The method of claim 18, wherein outputting data includes:
generating, by the data access and analysis system, the data for presenting the one or more data visualizations, wherein generating the data for presenting the one or more data visualizations includes:
generating, by the data access and analysis system, a second analytical object as an internal representation of the language model generated results data;
(Paragraphs [0079], [0085], [0093], [0130], [0176] and [0192])( Kulkarni)
and generating, by the data access and analysis system, a second analytical object as an internal representation of a resolved request corresponding to a data frame.
(Paragraphs [0079], [0085], [0093], [0130], [0176] and [0192])( Kulkarni)
As per claim 20, Kulkarni and Ghatage teach:
The method of claim 18, wherein:
obtaining the natural language input data includes obtaining the natural language input data from an external system;
(Paragraphs [0037]-[0041] and [0044], [0057], [0066] and [0074] and [0202])( Kulkarni)
and outputting data includes outputting the data to the external system.
(Paragraphs [0037]-[0041] and [0044], [0057], [0066] and [0074] and [0202])( Kulkarni)
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Tarek Chbouki whose telephone number is 571-2703154. The examiner can normally be reached on Mon-Fri 9:00 am to 6:00 pm EST. If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Aleksandr Kerzhner can be reached at 571-2701760. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/TAREK CHBOUKI/Primary Examiner, Art Unit 2165 8/1/2026
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