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 .
Examiner’s Note
In the method of claim 1, steps (g) to (i) recite determining if the output contains information present in the data object and then performing steps additional steps based on the determination. Accordingly, the claim scope as recited is until the determining step and it does not require to perform steps (h) and (i). Applicant should amend the claim to have positive recitations that will require steps (h) and (i) of the claim to be performed.
Claim Objections
Claims 1-15 are objected to because of the following informalities: In claim 1, in line 1, the limitation “detecting hallucination in output” is grammatically incomplete. In claim 1, line 1, the Examiner suggests replacing “a generative AI system” with “a generative artificial intelligence (AI) system”.
In each of claims 2-8, in line 1, “A computer implemented method” should recite “The computer implemented method”.
In claim 2, lines 3-4 should be indented one tab space to the right.
In claim 5, line 1 and claim 6, line 1, the Examiner suggests changing “comprising” to “further comprising”.
In claim 7, the Examiner suggests adding a colon to the end of line 1.
In claim 9 on page 2, line 2, the comma before “and” should be changed to a semicolon.
In each of claims 10-15, in line 1, “A system” should recite “The computer system”.
In claim 14, lines 5-7 should be indented one tab space to the right. In line 7, the limitation after the semicolon should be moved to a new line, and the comma before “and” should be changed to a semicolon. Appropriate correction is required.
Claim Interpretation
The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph:
An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The claims in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. The broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification when 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is invoked.
As explained in MPEP § 2181, subsection I, claim limitations that meet the following three-prong test will be interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph:
(A) the claim limitation uses the term “means” or “step” or a term used as a substitute for “means” that is a generic placeholder (also called a nonce term or a non-structural term having no specific structural meaning) for performing the claimed function;
(B) the term “means” or “step” or the generic placeholder is modified by functional language, typically, but not always linked by the transition word “for” (e.g., “means for”) or another linking word or phrase, such as “configured to” or “so that”; and
(C) the term “means” or “step” or the generic placeholder is not modified by sufficient structure, material, or acts for performing the claimed function.
Use of the word “means” (or “step”) in a claim with functional language creates a rebuttable presumption that the claim limitation is to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites sufficient structure, material, or acts to entirely perform the recited function.
Absence of the word “means” (or “step”) in a claim creates a rebuttable presumption that the claim limitation is not to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites function without reciting sufficient structure, material or acts to entirely perform the recited function.
Claim limitations in this application that use the word “means” (or “step”) are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action. Conversely, claim limitations in this application that do not use the word “means” (or “step”) are not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action.
This application includes one or more claim limitations that do not use the word “means,” but are nonetheless being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because the claim limitation(s) uses a generic placeholder that is coupled with functional language without reciting sufficient structure to perform the recited function and the generic placeholder is not preceded by a structural modifier. Such claim limitations are:
“a user data processing module” in claim 9, line 3 and claim 13, line 1.
Because this/these claim limitation(s) is/are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, it/they is/are being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof.
If applicant does not intend to have this/these limitation(s) interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may: (1) amend the claim limitation(s) to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitation(s) recite(s) sufficient structure to perform the claimed function so as to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 1-16 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claim 1 recites the limitation "the steps" in line 2. There is insufficient antecedent basis for this limitation in the claim. Examiner treats this limitation as “step”.
Claim 1 recites “a generative AI system” in lines 1, 11, and 12. It is unclear if these all refer to the same generative AI system. If so, the limitations in lines 11 and 12 should each recite “the generative AI system”. Examiner treats the limitations in lines 11 and 12 as “the generative AI system”.
Claims 2-8 and 16 are rejected for failing to cure the deficiencies of claim 1.
In claim 8, line 3, the limitation “should” renders the claim indefinite because it is unclear if the claim means that the database update instruction must be based on the database schema data or if the database update instruction is optionally based on the database schema data. Examiner treats claim 8 to mean the database update instruction is based on the database schema data.
Claim 9 recites “a generative AI system” in lines 1, 10, and 12. It is unclear if these all refer to the same generative AI system. If so, the limitations in lines 10 and 12 should each recite “the generative AI system”. Examiner treats the limitations in lines 10 and 12 as “the generative AI system”.
Claims 10-15 are rejected for failing to cure the deficiencies of claim 9.
Claim 15 recites the limitation "the database module" in line 1. There is insufficient antecedent basis for this limitation in the claim. It is unclear if "the database module" is supposed to recite “the database update module” which would have sufficient antecedent basis from claim 14. Examiner treats “the database module” in claim 15 as “the database update module”.
In claim 15, line 3, the limitation “should” renders the claim indefinite because it is unclear if the claim means that the database update instruction must be based on the database schema data or if the database update instruction is optionally based on the database schema data. Examiner treats claim 15 to mean the database update instruction is based on the database schema data.
The following claim limitations have been evaluated under the three-prong test set forth in MPEP § 2181, subsection I, but the results are inconclusive:
“a vector generation module” in claim 9, line 5;
“a vector comparison module” in claim 9, line 7;
“an input generation module” in claim 9, line 10;
“an output review module” in claim 9, line 14 and in claim 14, line 2; and
“the database update module” in claim 14, line 4 and claim 15, line 1.
Thus, it is unclear whether this limitation should be interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because the claim limitation uses the word “means” or a generic placeholder coupled with functional language, but it is modified by some structure or material that is ambiguous regarding whether that structure or material is sufficient for performing the claimed function. On page 20 in the specification, numbered lines 12-20 disclose the user device is provided by a computing device, and the application server implements the vector generation module, vector comparison module, input generation module, output review module, and database update module. It is unclear whether the computing device or the application server comprises a structure such as a processor for implementing the modules above. The boundaries of this claim limitation are ambiguous; therefore, the claim is indefinite and is rejected under 35 U.S.C. 112(b) or pre-AIA 35 U.S.C. 112, second paragraph.
In response to this rejection, applicant must clarify whether this limitation should be interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. Mere assertion regarding applicant’s intent to invoke or not invoke 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph is insufficient. Applicant may:
(a) Amend the claim to clearly invoke 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, by reciting “means” or a generic placeholder for means, or by reciting “step.” The “means,” generic placeholder, or “step” must be modified by functional language, and must not be modified by sufficient structure, material, or acts for performing the claimed function;
(b) Present a sufficient showing that 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, should apply because the claim limitation recites a function to be performed and does not recite sufficient structure, material, or acts to perform that function;
(c) Amend the claim to clearly avoid invoking 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, by deleting the function or by reciting sufficient structure, material or acts to perform the recited function; or
(d) Present a sufficient showing that 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, does not apply because the limitation does not recite a function or does recite a function along with sufficient structure, material or acts to perform that function.
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.
Claim 16 is rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter. The claim does not fall within at least one of the four categories of patent eligible subject matter. MPEP 2106.03, subsection I states non-limiting examples of claims that are not directed to any of the statutory categories include: transitory forms of signal transmission (often referred to as “signals per se”), such as a propagating electrical or electromagnetic signal or carrier wave.
Claim 16 does not limit the product or medium of “a computer program” and lacks a clear definition of the computer program. Claim 16 is rejected for being a signal per se. For purpose of examination, the Examiner will treat claim 16 as if it had recited a statutory product claim.
Claims 1-16 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1: Claims 1-8 recite methods, claims 9-15 recite systems, and claim 16 is treated as reciting a product. A method, a system, and a product each falls within one of the four statutory categories of patent-eligible subject matter.
Claim 1
Step 2A Prong 1: Detecting hallucination in output of a generative AI system is a judgment mental process which can reasonably be performed in the human mind with the aid of pencil and paper.
Generating a first vector representation of the user input and a second vector representation of the data object is a mathematical calculation. In the specification, page 12, line 29 to page 13, line 2 disclose generating vector embeddings using algorithms like TF-IDF or Word2Vec.
Comparing the first and second vector representations to identify one or more parts of the data object which most closely match the query or task specified in the user input is a judgement mental process which can reasonably be performed in the human mind with the aid of pencil and paper.
Generating an input
Analyzing an output produced by the generative AI system to determine if the output contains information also present in the data object, and if not: h) initiating an error process are judgement mental processes which can reasonably be performed in the human mind with the aid of pencil and paper. The claim recites an abstract idea.
Step 2A Prong 2: Receiving user input specifying a query or task relating to information contained in a data object amounts to mere data-gathering, an insignificant pre-solution activity under MPEP 2106.05(g).
Receiving the data object amounts to mere data-gathering, an insignificant pre-solution activity under MPEP 2106.05(g).
Inputting the input to a generative AI system amounts to mere instructions to apply the abstract ideas on a generic computer under MPEP 2106.05(f).
Outputting the output produced by the generative AI system amounts to an insignificant post-solution activity under MPEP 2106.05(g).
The additional elements as disclosed above, alone or in combination, do not integrate the abstract ideas into a practical application as they are mere insignificant extra solution activities as disclosed in combination with generic computer functions that are implemented to perform the abstract ideas disclosed above. The claim is directed to an abstract idea.
Step 2B: Receiving user input specifying a query or task relating to information contained in a data object is analogous to receiving data over a network, which the courts have recognized as a well-understood, routine, conventional activity under MPEP 2106.05(d)(II).
Receiving the data object is analogous to receiving data over a network, which the courts have recognized as a well-understood, routine, conventional activity under MPEP 2106.05(d)(II).
Inputting the input to a generative AI system amounts to mere instructions to apply the abstract ideas on a generic computer under MPEP 2106.05(f).
Outputting the output produced by the generative AI system is analogous to presenting offers and gathering statistics, which the courts have recognized as a well-understood, routine, conventional activity under MPEP 2106.05(d)(II).
The additional elements as disclosed above, in combination with the abstract ideas, are not sufficient to amount to significantly more than the abstract ideas as they are well-understood, routine and conventional activities as disclosed in combination with generic computer functions that are implemented to perform the abstract ideas disclosed above. The claim is not patent eligible.
Claim 2 incorporates the rejection of claim 1.
Step 2A Prong 1: The abstract ideas of claim 1 are incorporated. The error process comprises at least one of: regenerating the input for the generative AI system is a judgement mental process which can reasonably be performed in the human mind with the aid of pencil and paper.
Step 2A Prong 2: Outputting an error message amounts to an insignificant post-solution activity under MPEP 2106.05(g).
Step 2B: Outputting an error message is analogous to presenting offers and gathering statistics, which the courts have recognized as a well-understood, routine, conventional activity under MPEP 2106.05(d)(II). The claim is not patent eligible.
Claim 3 incorporates the rejection of claim 1.
Step 2A Prong 1: The abstract ideas of claim 1 are incorporated.
Step 2A Prong 2 and Step 2B: The generative AI system comprise a Large Language Model (LLM) amounts to a mere field of use and technological field under MPEP 2106.05(h). The claim is not patent eligible.
Claim 4 incorporates the rejection of claim 3.
Step 2A Prong 1: The abstract ideas of claim 3 are incorporated.
Step 2A Prong 2 and Step 2B: The input for the generative AI system comprises a prompt to use the identified part or parts of the data object to answer the user query or perform the specified task amounts to mere instructions to apply the abstract ideas on a generic computer under MPEP 2106.05(f). The claim is not patent eligible.
Claim 5 incorporates the rejection of claim 1.
Step 2A Prong 1: The abstract ideas of claim 1 are incorporated.
Step 2A Prong 2: Receiving the user input and the data object via a user interface amounts to an insignificant pre-solution activity under MPEP 2106.05(g).
Step 2B: Receiving the user input and the data object via a user interface is analogous to presenting offers and gathering statistics, which the courts have recognized as a well-understood, routine, conventional activity under MPEP 2106.05(d)(II). The claim is not patent eligible.
Claim 6 incorporates the rejection of claim 5.
Step 2A Prong 1: The abstract ideas of claim 5 are incorporated.
Step 2A Prong 2: Outputting the output via the user interface amounts to an insignificant post-solution activity under MPEP 2106.05(g).
Step 2B: Outputting the output via the user interface is analogous to presenting offers and gathering statistics, which the courts have recognized as a well-understood, routine, conventional activity under MPEP 2106.05(d)(II). The claim is not patent eligible.
Claim 7 incorporates the rejection of claim 1.
Step 2A Prong 1: The abstract ideas of claim 1 are incorporated. Generating a further input
Updating the database in accordance with the database update instruction is a judgment mental process which can reasonably be performed in the human mind with the aid of pencil and paper.
Step 2A Prong 2 and Step 2B: Inputting the further input to the generative AI system amounts to mere instructions to apply the abstract ideas on a generic computer under MPEP 2106.05(f).
Inputting a further output from the generative AI system comprising a corresponding database update instruction to a database amounts to mere instructions to apply the abstract ideas on a generic computer under MPEP 2106.05(f). The claim is not patent eligible.
Claim 8 incorporates the rejection of claim 7.
Step 2A Prong 1: The abstract ideas of claim 7 are incorporated. Generating the further input such that it further comprises database schema data associated with the database is a judgment mental process which can reasonably be performed in the human mind with the aid of pencil and paper.
Step 2A Prong 2 and Step 2B: The instruction further specifies that the database update instruction should also be based on the database schema data amounts to mere instructions to apply the abstract ideas on a generic computer under MPEP 2106.05(f). The claim is not patent eligible.
Claim 9
Step 2A Prong 1: Detecting hallucination in output of a generative AI system is a judgment mental process which can reasonably be performed in the human mind with the aid of pencil and paper.
Generate a first vector representation of the user input and a second vector representation of the data object is a mathematical calculation. In the specification, page 12, line 29 to page 13, line 2 disclose generating vector embeddings using algorithms like TF-IDF or Word2Vec.
Compare the first and second vector representations to identify one or more parts of the data object which most closely match the query or task specified in the user input is a judgement mental process which can reasonably be performed in the human mind with the aid of pencil and paper.
Generate an input
Analyze the output produced by the generative AI system to determine if the output contains information also present in the data object is a judgement mental process which can reasonably be performed in the human mind with the aid of pencil and paper.
If the output does not contain information also present in the data object, to initiate an error process is a judgement mental process which can reasonably be performed in the human mind with the aid of pencil and paper. The claim recites an abstract idea.
Step 2A Prong 2: A user data processing module amounts to mere instructions to apply the abstract ideas on a generic computer under MPEP 2106.05(f).
Receive user input specifying a query or task relating to information contained in a data object and to receive the data object amounts to mere data-gathering, an insignificant pre-solution activity under MPEP 2106.05(g).
A vector generation module amounts to mere instructions to apply the abstract ideas on a generic computer under MPEP 2106.05(f).
A vector comparison module amounts to mere instructions to apply the abstract ideas on a generic computer under MPEP 2106.05(f).
An input generation module amounts to mere instructions to apply the abstract ideas on a generic computer under MPEP 2106.05(f).
Communicate the input to an AI module, said AI module providing access to a generative AI system and configured to pass the input to the generative AI system to produce an output amounts to mere instructions to apply the abstract ideas on a generic computer under MPEP 2106.05(f). Communicating the input to an AI module further amounts to mere data-gathering, an insignificant extra-solution activity under MPEP 2106.05(g).
An output review module amounts to mere instructions to apply the abstract ideas on a generic computer under MPEP 2106.05(f).
If the output contains information also present in the data object, to output the output produced by the generative AI system amounts to an insignificant post-solution activity under MPEP 2106.05(g).
The additional elements as disclosed above, alone or in combination, do not integrate the abstract ideas into a practical application as they are mere insignificant extra solution activities as disclosed in combination with generic computer functions that are implemented to perform the abstract ideas disclosed above. The claim is directed to an abstract idea.
Step 2B: A user data processing module amounts to mere instructions to apply the abstract ideas on a generic computer under MPEP 2106.05(f).
Receive user input specifying a query or task relating to information contained in a data object and to receive the data object is analogous to receiving data over a network, which the courts have recognized as a well-understood, routine, conventional activity under MPEP 2106.05(d)(II).
A vector generation module amounts to mere instructions to apply the abstract ideas on a generic computer under MPEP 2106.05(f).
A vector comparison module amounts to mere instructions to apply the abstract ideas on a generic computer under MPEP 2106.05(f).
An input generation module amounts to mere instructions to apply the abstract ideas on a generic computer under MPEP 2106.05(f).
Communicate the input to an AI module, said AI module providing access to a generative AI system and configured to pass the input to the generative AI system to produce an output amounts to mere instructions to apply the abstract ideas on a generic computer under MPEP 2106.05(f). Communicating the input to an AI module further is analogous to transmitting data over a network, which the courts have recognized as a well-understood, routine, conventional activity under MPEP 2106.05(d)(II).
An output review module amounts to mere instructions to apply the abstract ideas on a generic computer under MPEP 2106.05(f).
If the output contains information also present in the data object, to output the output produced by the generative AI system is analogous to presenting offers and gathering statistics, which the courts have recognized as a well-understood, routine, conventional activity under MPEP 2106.05(d)(II).
The additional elements as disclosed above, in combination with the abstract ideas, are not sufficient to amount to significantly more than the abstract ideas as they are well-understood, routine and conventional activities as disclosed in combination with generic computer functions that are implemented to perform the abstract ideas disclosed above. The claim is not patent eligible.
Claim 10 incorporates the rejection of claim 9.
Step 2A Prong 1: The abstract ideas of claim 9 are incorporated. The error process comprises at least one of:
Step 2A Prong 2: Controlling the input generation module amounts to mere instructions to apply the abstract ideas on a generic computer under MPEP 2106.05(f).
Pass the regenerated input to the AI module amounts to mere data-gathering, an insignificant extra-solution activity under MPEP 2106.05(g).
Output an error message amounts to an insignificant extra-solution activity under MPEP 2106.05(g).
Step 2B: Controlling the input generation module amounts to mere instructions to apply the abstract ideas on a generic computer under MPEP 2106.05(f).
Pass the regenerated input to the AI module is analogous to transmitting data over a network, which the courts have recognized as a well-understood, routine, conventional activity under MPEP 2106.05(d)(II).
Output an error message is analogous to presenting offers and gathering statistics, which the courts have recognized as a well-understood, routine, conventional activity under MPEP 2106.05(d)(II). The claim is not patent eligible.
Claim 11 recites a system which implements the same features as the method of claim 3 and is therefore rejected for at least the same reasons.
Claim 12 incorporates the rejection of claim 9.
Step 2A Prong 1: The abstract ideas of claim 9 are incorporated.
Step 2A Prong 2 and Step 2B: The input for the generative AI system comprises a prompt to use the identified part or parts of the data object to answer the user query or perform the specified task amounts to mere instructions to apply the abstract ideas on a generic computer under MPEP 2106.05(f). The claim is not patent eligible.
Claim 13 incorporates the rejection of claim 9.
Step 2A Prong 1: The abstract ideas of claim 9 are incorporated.
Step 2A Prong 2: The user data processing module amounts to mere instructions for applying the abstract ideas on a computer under MPEP 2106.05(f).
Receive the user input and the data object via a user interface amounts to an insignificant pre-solution activity under MPEP 2106.05(g).
Step 2B: The user data processing module amounts to mere instructions for applying the abstract ideas on a computer under MPEP 2106.05(f).
Receive the user input and the data object via a user interface is analogous to presenting offers and gathering statistics, which the courts have recognized as a well-understood, routine, conventional activity under MPEP 2106.05(d)(II). The claim is not patent eligible.
Claim 14 incorporates the rejection of claim 9.
Step 2A Prong 1: The abstract ideas of claim 9 are incorporated. Generate a further input for the generative AI system comprising the output produced by the generative AI system and an instruction to generate a corresponding database update instruction based on the output is a judgment mental process which can reasonably be performed in the human mind with the aid of pencil and paper.
Update the database in accordance with the database update instruction is a judgment mental process which can reasonably be performed in the human mind with the aid of pencil and paper.
Step 2A Prong 2: A database update module amounts to mere instructions for applying the abstract ideas on a computer under MPEP 2106.05(f).
If the output contains information also present in the data object, the output review module is configured to output the output produced by the generative AI system to the database update module amounts to insignificant extra-solution activity under MPEP 2106.05(g).
Input the further input to the generative AI system amounts to mere instructions to apply the abstract ideas on a generic computer under MPEP 2106.05(f).
Input a further output from the generative AI system comprising a corresponding database update instruction to a database amounts to mere instructions to apply the abstract ideas on a generic computer under MPEP 2106.05(f).
Step 2B: A database update module amounts to mere instructions for applying the abstract ideas on a computer under MPEP 2106.05(f).
If the output contains information also present in the data object, the output review module is configured to output the output produced by the generative AI system to the database update module is analogous to presenting offers and gathering statistics, which the courts have recognized as a well-understood, routine, conventional activity under MPEP 2106.05(d)(II).
Input the further input to the generative AI system amounts to mere instructions to apply the abstract ideas on a generic computer under MPEP 2106.05(f).
Input a further output from the generative AI system comprising a corresponding database update instruction to a database amounts to mere instructions to apply the abstract ideas on a generic computer under MPEP 2106.05(f). The claim is not patent eligible.
Claim 15 incorporates the rejection of claim 14.
Step 2A Prong 1: The abstract ideas of claim 14 are incorporated. Generate the further input such that it further comprises database schema data associated with the database is a judgment mental process which can reasonably be performed in the human mind with the aid of pencil and paper.
Step 2A Prong 2 and Step 2B: The database module amounts to mere instructions to apply the abstract ideas on a generic computer under MPEP 2106.05(f).
The instruction further specifies that the database update instruction should also be based on the database schema data amounts to mere instructions to apply the abstract ideas on a generic computer under MPEP 2106.05(f). The claim is not patent eligible.
Claim 16 incorporates the abstract ideas from the method of claim 1. In Step 2A Prong 2 and Step 2B, the limitations “a computer program which when executed on a computing device controls the computing device” amount to generic computer components for applying the abstract ideas on a generic computer under MPEP 2106.05(f). The claim is not patent eligible.
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)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claims 1-7, 9-13, and 16 are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Gajek et al. (US 11861320 B1).
Regarding claim 1, Gajek teaches: A computer implemented method of detecting hallucination in output of a generative AI system, said method comprising the steps of: (C. 28, L. 60-63)
a) receiving user input specifying a query or task relating to information contained in a data object; (C. 5, L. 54 to C. 6, L. 3 where a data object includes a set of text portions to analyze such as a collection of documents or document stored in a repository.)
b) receiving the data object; (C. 5, L. 62 to C. 6, L. 1 and C. 32, L. 17-33 discloses the system analyzes a set of text portions, which indicates receiving the set of text portions.)
c) generating a first vector representation of the user input and a second vector representation of the data object; (C. 6, L. 23-31; and C. 32, L. 17-31 and 48-59 discloses encoding separate text portions to a common embedding space, and comparing the distance between the query and each text portion in the embedding space. A “second vector representation of the data object” includes the set of separate encoded text portions in the embedding space. The distance as disclosed in C. 32, L. 57-61 indicates that the query has also been encoded into the embedding space.)
d) comparing the first and second vector representations to identify one or more parts of the data object which most closely match the query or task specified in the user input; (C. 6, L. 29-31; C. 32, L. 17-31 and 48-59; C. 33, L. 30-54. Parts of the data object which most closely matches the query are text portions with relevance scores that exceed a designated threshold.)
e) generating an input for a generative AI system comprising the user input and the identified one or more parts of the data object; (C. 2, L. 26-37; C. 6, L. 32-36; and C. 33, L. 59-67 teaches a large language model (“a generative AI system”) for answering a question based on filtered text portions. The LLM would receive the filtered text portions as an input along with the question.)
f) inputting the input to a generative AI system; (C. 2, L. 26-37 and C. 7, L. 23-30)
g) analyzing an output produced by the generative AI system to determine if the output contains information also present in the data object, and if not: (h) initiating an error process, and if so: (i) outputting the output produced by the generative AI system. (C. 7, L. 23-30, 39-46; C. 28, L. 56 to C. 29, L. 14 discloses evaluating a response returned by text generation modeling system for hallucinations, and discloses ways of handling the hallucination. C. 17, L. 38-43 and Fig. 8 discloses an example of outputting a response from the text generation modeling system 270 to a client machine. When a response does not have any hallucinations, it is outputted to a client machine.)
Regarding claim 2, Gajek teaches: A computer implemented method according to claim 1, where the error process comprises at least one of: regenerating the input for the generative AI system, and outputting an error message. (C. 28, L. 32-48 and C. 29, L. 1-14 discloses transmitting new prompts to the text generation modeling system at step 1134, or informing a response recipient of a potentially inaccurate response.)
Regarding claim 3, Gajek teaches: A computer implemented method according to claim 1, wherein the generative AI system comprise a Large Language Model (LLM). (C. 7, L. 23-24)
Regarding claim 4, Gajek teaches: A computer implemented method according to claim 3, wherein the input for the generative AI system comprises a prompt to use the identified part or parts of the data object to answer the user query or perform the specified task. (C. 2, L. 26-37; C. 6, L. 32-36; and C. 33, L. 59-67 teaches a large language model for answering a question based on filtered text portions.)
Regarding claim 5, Gajek teaches: A computer implemented method according to claim 1, comprising receiving the user input and the data object via a user interface. (C. 5, L. 56 to C. 6, L. 1, where a chat session indicates a user interface at the client machine.)
Regarding claim 6, Gajek teaches: A computer implemented method according to claim 5, comprising outputting the output via the user interface. (C. 17, L. 38-43 and Fig. 8 discloses an example of outputting a response from the text generation modeling system 270 to a client machine.)
Regarding claim 9, Gajek teaches: A computer system for detecting hallucination in output of a generative AI system, said system comprising: (C. 16, L. 24-28 and C. 28, L. 60-63 discloses a computer device 700 including a processor 701 for detecting hallucinations.)
a user data processing module configured to receive user input specifying a query or task relating to information contained in a data object and to receive the data object; (C. 5, L. 54 to C. 6, L. 3 and C. 32, L. 17-33, where a data object includes a set of text portions to analyze such as a collection of documents or document stored in a repository. Since the system analyzes a set of text portions, this indicates receiving the set of text portions.)
a vector generation module configured to generate a first vector representation of the user input and a second vector representation of the data object; (C. 6, L. 23-31; and C. 32, L. 17-31 and 48-59 discloses encoding separate text portions to a common embedding space, and comparing the distance between the query and each text portion in the embedding space. A “second vector representation of the data object” includes the set of separate encoded text portions in the embedding space. The distance as disclosed in C. 32, L. 57-61 indicates that the query has also been encoded into the embedding space.)
a vector comparison module configured to compare the first and second vector representations to identify one or more parts of the data object which most closely match the query or task specified in the user input; (C. 6, L. 29-31; C. 32, L. 17-31 and 48-59; C. 33, L. 30-54. Parts of the data object which most closely matches the query are text portions with relevance scores that exceed a designated threshold.)
an input generation module configured to generate an input for a generative AI system comprising the user input and the identified one or more parts of the data object, and communicate the input to an AI module, said AI module providing access to a generative AI system and configured to pass the input to the generative AI system to produce an output; (C. 2, L. 26-37; C. 6, L. 32-36; C. 7, L. 23-30; and C. 33, L. 59-67 teaches a large language model (“a generative AI system”) for answering a question based on filtered text portions. The LLM would receive the filtered text portions as an input along with the question. An “AI module” as claimed is an input layer of the LLM.)
an output review module configured to analyze the output produced by the generative AI system to determine if the output contains information also present in the data object, said output review module further configured: if the output does not contain information also present in the data object, to initiate an error process, and if the output contains information also present in the data object, to output the output produced by the generative AI system. (C. 7, L. 23-30, 39-46; C. 28, L. 56 to C. 29, L. 14 discloses evaluating a response returned by text generation modeling system for hallucinations, and different ways of handling the hallucination. C. 17, L. 38-43 and Fig. 8 discloses an example of outputting a response from the text generation modeling system 270 to a client machine. A response without hallucinations is outputted to a client machine.)
Regarding claim 10, Gajek teaches: A system according to claim 9, wherein the error process comprises at least one of: controlling the input generation module to regenerate the input for the generative AI system and pass the regenerated input to the AI module, and controlling the input generation module to output an error message. (C. 28, L. 32-48 and C. 29, L. 1-14 discloses transmitting new prompts to the text generation modeling system at step 1134, or informing a response recipient of a potentially inaccurate response.)
Claims 11-13 each recite a system which implements the same features as the method of claim 3-5, respectively, and are therefore rejected for at least the same reasons.
Regarding claim 16, Gajek teaches: A computer program which when executed on a computing device controls the computing device to implement a method according to claim 1. (C. 16, L. 24-25 and 34-38)
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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 7-8 are rejected under 35 U.S.C. 103 as being unpatentable over Gajek et al. (US 11861320 B1) in view of Zorn et al. (US 20230418815 A1).
Regarding claim 7, Gajek teaches: A computer implemented method according to claim 1, further comprising generating a further input for the generative AI system comprising the output and an instruction to generate a corresponding
inputting the further input to the generative AI system; (C. 28, L. 32-48)
However, Gajek does not explicitly teach: an instruction to generate a corresponding database update instruction
…
inputting a further output from the generative AI system comprising a corresponding database update instruction to a database, and updating the database in accordance with the database update instruction.
But Zorn teaches: an instruction to generate a corresponding database update instruction ([0002] and [0035]-[0037] discloses an instruction in the form of an LLM prompt “calculate the grades”. A database update instruction is an instruction to modify a spreadsheet.)
…
inputting a further output from the generative AI system comprising a corresponding database update instruction to a database, and updating the database in accordance with the database update instruction. ([0039]-[0044] disclose using the LLM to generate a database update instruction to calculate grades for each student based on test scores and add a “Grades” column to the spreadsheet. The database update instruction is the imperative code portion 601.)
Gajek discloses automatically prompting the system to revising a response, and this prompt would correspond to Zorn’s prompt 420. It would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to have applied Gajek’s system to update Zorn’s database. A motivation for the combination is that generating imperative code that updates a database is merely a use case for a large language model (Zorn, [0002]). The combination would use the LLM to generate imperative code and automatically revise incorrect imperative code.
Regarding claim 8, the combination Gajek and Zorn teaches: A computer implemented method according to claim 7, further comprising
However, Gajek does not explicitly teach: generating the further input such that it further comprises database schema data associated with the database and the instruction further specifies that the database update instruction should also be based on the database schema data.
But Zorn teaches: generating the further input such that it further comprises database schema data associated with the database and the instruction further specifies that the database update instruction should also be based on the database schema data. ([0028], line 1 to page 3, col. 1, line 6; [0029], lines 1-7; and [0042]-[0044] discloses generating imperative code for updating the spreadsheet. Since each student’s grades is based on the test score in Fig. 6, this indicates the meaning of rows and columns (“database schema data”) of the spreadsheet was input to the LLM.)
It would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to have applied Gajek’s system to update Zorn’s database. A motivation for the combination is that generating imperative code that updates a database is merely a use case for a large language model. (Zorn, [0002])
Claims 14-15 are rejected under 35 U.S.C. 103 as being unpatentable over Gajek et al. (US 11861320 B1) in view of Shoham et al. (US 20240428017 A1) and Iu et al. (US 20240273286 A1).
Regarding claim 14, Gajek teaches: A system according to claim 9,
However, Gajek does not explicitly teach: further comprising a database update module, wherein if the output contains information also present in the data object, the output review module is configured to output the output produced by the generative AI system to the database update module, responsive to which, the database update module is configured to:
generate a further input for the generative AI system comprising the output produced by the generative AI system and an instruction to generate a corresponding database update instruction based on the output;
input the further input to the generative AI system, and
input a further output from the generative AI system comprising a corresponding database update instruction to a database to update the database in accordance with the database update instruction.
But Shoham teaches: generate a further input for the generative AI system comprising [data] corresponding database update instruction based on the [data]
input the further input to the generative AI system, and ([0072])
input a further output from the generative AI system comprising a corresponding database update instruction to a database to update the database in accordance with the database update instruction. ([0072])
It would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to have applied Shoham’s trained language model to generate a database update command based on natural language update information, where data in the instructions comes from Gajek’s response. A motivation for the combination is that free language input is more flexible than a structured command.
However, Gajek and Shoham do not explicitly teach: further comprising a database update module, wherein if the output contains information also present in the data object, the output review module is configured to output the output produced by the generative AI system to the database update module, responsive to which, the database update module is configured to:
generate a further input for the generative AI system comprising the output produced by the generative AI system
But Iu teaches: further comprising a [content] ([0110], lines 1-2, [0116], [0329], [0336]-[0337], [0339] discloses a prompt generation subsystem 302 (content update module) and a generative language model. The prompt generation subsystem generates a second prompt that includes a first piece of writing by the generative language model.)
In the combination of Gajek and Shoham, a human user generates a database update prompt for the generative AI model. It would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to have used Iu’s prompt generation subsystem 302 to generate prompt for updating Shoham’s database. The prompt generation subsystem corresponds to “a database update module” as claimed, and it generates a second prompt that includes an output of the generative language model. A motivation for the combination is to automatically prompt a generative language model to perform a second task based on an output from a first task.
Regarding claim 15, the combination of Gajek and Shoham teaches: A system according to claim 14, wherein the database module is further configured to
However, Gajek and Iu do not explicitly teach: generate the further input such that it further comprises database schema data associated with the database and the instruction further specifies that the database update instruction should also be based on the database schema data.
But Shoham teaches: generate the further input such that it further comprises database schema data associated with the database and the instruction further specifies that the database update instruction should also be based on the database schema data. (Database schema data includes any data about the organization of the database. In [0072], the input “add a column for entry of client country code” is an instruction and it includes data about the organization of the database.)
A motivation for the combination is the same as the motivation given for claim 14.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Asher H. Jablon whose telephone number is (571)270-7648. The examiner can normally be reached Monday - Friday, 9:00 am - 6:00 pm.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Abdullah Al Kawsar can be reached at (571)270-3169. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/A.H.J./Examiner, Art Unit 2127
/ABDULLAH AL KAWSAR/Supervisory Patent Examiner, Art Unit 2127
