DETAILED ACTION
This communication is in response to Application No. 18/543,609 filed on December 18th, 2023 in which claims 1-30 are presented for examination.
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 .
Information Disclosure Statement
The information disclosure statements submitted on 12/18/2023, 07/18/2024, and 02/09/2026 are in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statements were considered by the examiner.
Specification
The contents of the specification are sufficient for examination purposes.
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 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.
Because these claim limitations 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.
Claims 10 and 17-24 are rejected under 35 U.S.C. 112(b) as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor regards as the invention.
Regarding Claim 10, the claim recites the limitation “the personal knowledge repository” (ln. 2). There is insufficient antecedent basis for this limitation in the claim. Specifically, a “personal knowledge repository” was not previously recited in either Claim 10 or in a claim which Claim 10 depends on. As a result, it is not clear what “the personal knowledge repository” is referring to. Therefore, the claim is indefinite. As a result, it is rejected. The claim should be amended to remedy the insufficient antecedent basis issue, such as by remove “the” or positively reciting the “the personal knowledge repository” prior to this limitation.
Regarding Claim 17, the claim includes multiple limitations that recite “means for” (ln. 2, 3, 6, 8, 10, 12, and 14), which invokes 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. However, the written description fails to disclose the corresponding structure, material, or acts for performing the entire claimed function and to clearly link the structure, material, or acts to the function. Specifically, the disclosure is devoid of the necessary structure that performs the function in the claim, and, as a result, there is no association between a structure and the function can be found in the specification. 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.
Applicant may:
(a) Amend the claim so that the claim limitation will no longer be interpreted as a limitation under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph;
(b) Amend the written description of the specification such that it expressly recites what structure, material, or acts perform the entire claimed function, without introducing any new matter (35 U.S.C. 132(a)); or
(c) Amend the written description of the specification such that it clearly links the structure, material, or acts disclosed therein to the function recited in the claim, without introducing any new matter (35 U.S.C. 132(a)).
If applicant is of the opinion that the written description of the specification already implicitly or inherently discloses the corresponding structure, material, or acts and clearly links them to the function so that one of ordinary skill in the art would recognize what structure, material, or acts perform the claimed function, applicant should clarify the record by either:
(a) Amending the written description of the specification such that it expressly recites the corresponding structure, material, or acts for performing the claimed function and clearly links or associates the structure, material, or acts to the claimed function, without introducing any new matter (35 U.S.C. 132(a)); or
(b) Stating on the record what the corresponding structure, material, or acts, which are implicitly or inherently set forth in the written description of the specification, perform the claimed function. For more information, see 37 CFR 1.75(d) and MPEP §§ 608.01(o) and 2181.
Regarding Claims 18-19, the claims include multiple limitations that recite “means for” (Claim 18, ln. 1-2; Claim 19, ln. 2-3 and 5), which as discussed above, invokes 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. However, the written description fails to disclose the corresponding structure, material, or acts for performing the entire claimed function and to clearly link the structure, material, or acts to the function. Therefore, as elaborated on above, the claims are indefinite and are rejected under 35 U.S.C. 112(b). Applicant may amend or respond in the manner outlined in the rejection of Claim 17 above. Additionally, the claims are rejected because they are dependent on a rejected claim.
Regarding Claims 20-21, the claims are rejected because they are dependent on a rejected claim.
Regarding Claim 22, the claim includes multiple limitations that recite “means for” (ln. 2 and 4), which as discussed above, invokes 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. However, the written description fails to disclose the corresponding structure, material, or acts for performing the entire claimed function and to clearly link the structure, material, or acts to the function. Therefore, as elaborated on above, the claim is indefinite and is rejected under 35 U.S.C. 112(b). Applicant may amend or respond in the manner outlined in the rejection of Claim 17 above. Additionally, the claim is rejected because it is dependent on a rejected claim.
Regarding Claims 23-24, the claims are rejected because they are dependent on a rejected claim.
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 1-30 are rejected under 35 U.S.C. 101 because the claimed invention is directed to abstract ideas without significantly more.
Regarding Claim 1:
Step 1: Claim 1 is a machine claim. Therefore, claims 1-8 are directed to a statutory category of eligible subject matter.
Step 2A Prong 1: If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then it falls within the "Mental Processes" grouping of abstract ideas. Here, steps of the claimed process are mental processes. Specifically, the claim recites
“generate a query based on the input prompt and the contextual information associated with the input prompt” (mental process – amounts to exercising judgement to form an opinion, with reference to known or observed information, which may be aided by pen and paper).
Step 2A Prong 2: This judicial exception is not integrated into a practical application.
The claim recites the additional elements:
“A processing system, comprising: at least one memory having executable instructions stored thereon; and one or more processors configured to execute the executable instructions in order to cause the processing system to . . . to a generative artificial intelligence model for processing . . . from the generative artificial intelligence model” (amounts to mere instructions to apply the judicial exception on generic and unspecialized computer components, which do not impose any meaningful limits on practicing the abstract idea);
“receive an input prompt . . . request . . . contextual information . . . output the generated query . . . receive . . . a response . . . output the received response” (receiving, requesting, and outputting is insignificant extra-solution activity because it amounts to the transmission of data, which is incidental to the claimed subject matter);
“retrieve . . . the contextual information” (retrieving is insignificant extra-solution activity because it amounts to retrieving information in memory, which is incidental to the claimed subject matter); and
“for processing . . . based on user information associated with the received prompt . . . associated with the received prompt from a knowledge repository . . . to the generated query . . . as a response to the input prompt” (amounts to merely generally linking the use of the judicial exception to a particular technological environment or field of use, which do not impose any meaningful limits on practicing the abstract idea).
Step 2B: The claim does not include additional elements considered individually and in combination that are sufficient to amount to significantly more than the judicial exception.
The claim recites the additional elements:
“A processing system, comprising: at least one memory having executable instructions stored thereon; and one or more processors configured to execute the executable instructions in order to cause the processing system to . . . to a generative artificial intelligence model for processing . . . from the generative artificial intelligence model” (mere instructions to apply the exception using generic computer components does not provide an inventive concept);
“receive an input prompt . . . request . . . contextual information . . . output the generated query . . . receive . . . a response . . . output the received response” (transmitting data is well‐understood, routine, and conventional, see Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362; buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014); therefore the limitation, which is recited with a high level of generality, remains insignificant extra-solution activity even upon reconsideration);
“retrieve . . . the contextual information” (retrieving information in memory is well‐understood, routine, and conventional, see 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; therefore the limitation, which is recited with a high level of generality, remains insignificant extra-solution activity even upon reconsideration); and
“for processing . . . based on user information associated with the received prompt . . . associated with the received prompt from a knowledge repository . . . to the generated query . . . as a response to the input prompt” (merely generally linking the use of the judicial exception to a particular technological environment or field of use does not provide an inventive concept).
For the reasons above, Claim 1 is rejected as being directed to an abstract idea without significantly more. This rejection applies equally to dependent claims 2-8. The additional limitations of the dependent claims are addressed below.
Regarding Claim 2:
Step 2A Prong 1: See the rejection of Claim 1 above, which Claim 2 depends on. Here, the claim recites additional elements that are mental processes. Specifically, the claim recites:
“identify the knowledge repository from a group of repositories for which a user associated with the user information has access permissions” (mental process – amounts to exercising judgement to form an opinion, with reference to known or observed information, which may be aided by pen and paper).
Step 2A Prong 2: This judicial exception is not integrated into a practical application.
The claim recites the additional elements:
“wherein to request the contextual information” (requesting is insignificant extra-solution activity because it amounts to the transmission of data, which is incidental to the claimed subject matter);
“from the knowledge repositor” (amounts to merely generally linking the use of the judicial exception to a particular technological environment or field of use, which do not impose any meaningful limits on practicing the abstract idea); and
“the one or more processors are configured to cause the processing system to” (amounts to mere instructions to apply the judicial exception on generic and unspecialized computer components, which do not impose any meaningful limits on practicing the abstract idea).
Step 2B: The claim does not include additional elements considered individually and in combination that are sufficient to amount to significantly more than the judicial exception.
The claim recites the additional elements:
“wherein to request the contextual information” (transmitting data is well‐understood, routine, and conventional, see Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362; buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014); therefore the limitation, which is recited with a high level of generality, remains insignificant extra-solution activity even upon reconsideration);
“from the knowledge repositor” (merely generally linking the use of the judicial exception to a particular technological environment or field of use does not provide an inventive concept); and
“the one or more processors are configured to cause the processing system to” (mere instructions to apply the exception using generic computer components does not provide an inventive concept).
Accordingly, Claim 2 is rejected as being directed to an abstract idea without significantly more.
Regarding Claim 3:
Step 2A Prong 1: See the rejection of Claim 1 above, which Claim 3 depends on.
Step 2A Prong 2: This judicial exception is not integrated into a practical application.
The claim recites the additional elements:
“to request the contextual information . . . request the contextual information” (requesting is insignificant extra-solution activity because it amounts to the transmission of data, which is incidental to the claimed subject matter);
“from the knowledge repository . . . from a plurality of knowledge repositories . . . from one or more knowledge repositories of the plurality of knowledge repositories, the one or more knowledge repositories comprising knowledge repositories which a user associated with the user information has permissions to access” (amounts to merely generally linking the use of the judicial exception to a particular technological environment or field of use, which do not impose any meaningful limits on practicing the abstract idea);
“the one or more processors are configured to cause the processing system to . . . the one or more processors are configured to cause the processing system to” (amounts to mere instructions to apply the judicial exception on generic and unspecialized computer components, which do not impose any meaningful limits on practicing the abstract idea); and
“to retrieve the contextual information . . . receive the contextual information” (retrieving is insignificant extra-solution activity because it amounts to retrieving information in memory, which is incidental to the claimed subject matter).
Step 2B: The claim does not include additional elements considered individually and in combination that are sufficient to amount to significantly more than the judicial exception.
The claim recites the additional elements:
“to request the contextual information . . . request the contextual information” (transmitting data is well‐understood, routine, and conventional, see Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362; buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014); therefore the limitation, which is recited with a high level of generality, remains insignificant extra-solution activity even upon reconsideration);
“from the knowledge repository . . . from a plurality of knowledge repositories . . . from one or more knowledge repositories of the plurality of knowledge repositories, the one or more knowledge repositories comprising knowledge repositories which a user associated with the user information has permissions to access” (merely generally linking the use of the judicial exception to a particular technological environment or field of use does not provide an inventive concept);
“the one or more processors are configured to cause the processing system to . . . the one or more processors are configured to cause the processing system to” (mere instructions to apply the exception using generic computer components does not provide an inventive concept); and
“to retrieve the contextual information . . . receive the contextual information” (retrieving information in memory is well‐understood, routine, and conventional, see 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; therefore the limitation, which is recited with a high level of generality, remains insignificant extra-solution activity even upon reconsideration).
Accordingly, Claim 3 is rejected as being directed to an abstract idea without significantly more.
Regarding Claim 4:
Step 2A Prong 1: See the rejection of Claim 1 above, which Claim 4 depends on.
Step 2A Prong 2: This judicial exception is not integrated into a practical application.
The claim recites the additional elements:
“wherein the knowledge repository comprises a knowledge repository co-located with the generative artificial intelligence model” (amounts to merely generally linking the use of the judicial exception to a particular technological environment or field of use, which do not impose any meaningful limits on practicing the abstract idea).
Step 2B: The claim does not include additional elements considered individually and in combination that are sufficient to amount to significantly more than the judicial exception.
The claim recites the additional elements:
“wherein the knowledge repository comprises a knowledge repository co-located with the generative artificial intelligence model” (merely generally linking the use of the judicial exception to a particular technological environment or field of use does not provide an inventive concept).
Accordingly, Claim 4 is rejected as being directed to an abstract idea without significantly more.
Regarding Claim 5:
Step 2A Prong 1: See the rejection of Claim 4 above, which Claim 5 depends on.
Step 2A Prong 2: This judicial exception is not integrated into a practical application.
The claim recites the additional elements:
“wherein the knowledge repository and the generative artificial intelligence model are co-located on an edge device that received the input prompt” (amounts to merely generally linking the use of the judicial exception to a particular technological environment or field of use, which do not impose any meaningful limits on practicing the abstract idea).
Step 2B: The claim does not include additional elements considered individually and in combination that are sufficient to amount to significantly more than the judicial exception.
The claim recites the additional elements:
“wherein the knowledge repository and the generative artificial intelligence model are co-located on an edge device that received the input prompt” (merely generally linking the use of the judicial exception to a particular technological environment or field of use does not provide an inventive concept).
Accordingly, Claim 5 is rejected as being directed to an abstract idea without significantly more.
Regarding Claim 6:
Step 2A Prong 1: See the rejection of Claim 5 above, which Claim 6 depends on. Here, the claim recites additional elements that are mental processes. Specifically, the claim recites
“update the received response based on the information” (mental process – amounts to exercising judgment to form an opinion on how a known or observed entity should be altered, based on known or observed information, which may be aided by pen and paper).
Step 2A Prong 2: This judicial exception is not integrated into a practical application.
The claim recites the additional elements:
“wherein the one or more processors are further configured to cause the processing system to” (amounts to mere instructions to apply the judicial exception on generic and unspecialized computer components, which do not impose any meaningful limits on practicing the abstract idea);
“retrieve information . . . retrieved” (retrieving is insignificant extra-solution activity because it amounts to retrieving information in memory, which is incidental to the claimed subject matter); and
“related to the query from an external resource . . . from the external resource” (amounts to merely generally linking the use of the judicial exception to a particular technological environment or field of use, which do not impose any meaningful limits on practicing the abstract idea).
Step 2B: The claim does not include additional elements considered individually and in combination that are sufficient to amount to significantly more than the judicial exception.
The claim recites the additional elements:
“wherein the one or more processors are further configured to cause the processing system to” (mere instructions to apply the exception using generic computer components does not provide an inventive concept);
“retrieve information . . . retrieved” (retrieving information in memory is well‐understood, routine, and conventional, see 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; therefore the limitation, which is recited with a high level of generality, remains insignificant extra-solution activity even upon reconsideration); and
“related to the query from an external resource . . . from the external resource” (merely generally linking the use of the judicial exception to a particular technological environment or field of use does not provide an inventive concept).
Accordingly, Claim 6 is rejected as being directed to an abstract idea without significantly more.
Regarding Claim 7:
Step 2A Prong 1: See the rejection of Claim 1 above, which Claim 7 depends on.
Step 2A Prong 2: This judicial exception is not integrated into a practical application.
The claim recites the additional elements:
“wherein the knowledge repository comprises a knowledge repository hosted on a local network and accessible by a group of users including a user associated with the user information” (amounts to merely generally linking the use of the judicial exception to a particular technological environment or field of use, which do not impose any meaningful limits on practicing the abstract idea).
Step 2B: The claim does not include additional elements considered individually and in combination that are sufficient to amount to significantly more than the judicial exception.
The claim recites the additional elements:
“wherein the knowledge repository comprises a knowledge repository hosted on a local network and accessible by a group of users including a user associated with the user information” (merely generally linking the use of the judicial exception to a particular technological environment or field of use does not provide an inventive concept).
Accordingly, Claim 7 is rejected as being directed to an abstract idea without significantly more.
Regarding Claim 8:
Step 2A Prong 1: See the rejection of Claim 1 above, which Claim 8 depends on.
Step 2A Prong 2: This judicial exception is not integrated into a practical application.
The claim recites the additional elements:
“wherein the knowledge repository comprises a public knowledge repository located on a remote computing system” (amounts to merely generally linking the use of the judicial exception to a particular technological environment or field of use, which do not impose any meaningful limits on practicing the abstract idea).
Step 2B: The claim does not include additional elements considered individually and in combination that are sufficient to amount to significantly more than the judicial exception.
The claim recites the additional elements:
“wherein the knowledge repository comprises a public knowledge repository located on a remote computing system” (merely generally linking the use of the judicial exception to a particular technological environment or field of use does not provide an inventive concept).
Accordingly, Claim 8 is rejected as being directed to an abstract idea without significantly more.
Regarding Claim 9:
Step 1: Claim 9 is a process claim. Therefore, claims 9-16 are directed to a statutory category of eligible subject matter.
Step 2A Prong 1: If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then it falls within the "Mental Processes" grouping of abstract ideas. Here, the claim recites limitations that are substantially the same as the limitations of Claim 1. As a result, and as elaborated above, these limitations are abstract ideas because they are mental processes.
Step 2A Prong 2: This judicial exception is not integrated into a practical application.
The claim recites the additional elements:
“A processor-implemented method, comprising . . . to a generative artificial intelligence model for processing . . . from the generative artificial intelligence model” (amounts to mere instructions to apply the judicial exception on generic and unspecialized computer components, which do not impose any meaningful limits on practicing the abstract idea);
“receiving an input prompt . . . requesting . . . contextual information . . . outputting the generated query . . . receiving . . . a response . . . outputting the received response” (receiving, requesting, and outputting is insignificant extra-solution activity because it amounts to the transmission of data, which is incidental to the claimed subject matter);
“retrieving . . . the contextual information” (retrieving is insignificant extra-solution activity because it amounts to retrieving information in memory, which is incidental to the claimed subject matter); and
“for processing . . . based on user information associated with the received prompt . . . associated with the received prompt from a knowledge repository . . . to the generated query . . . as a response to the input prompt” (amounts to merely generally linking the use of the judicial exception to a particular technological environment or field of use, which do not impose any meaningful limits on practicing the abstract idea).
Step 2B: The claim does not include additional elements considered individually and in combination that are sufficient to amount to significantly more than the judicial exception.
The claim recites the additional elements:
“A processor-implemented method, comprising . . . to a generative artificial intelligence model for processing . . . from the generative artificial intelligence model” (mere instructions to apply the exception using generic computer components does not provide an inventive concept);
“receiving an input prompt . . . requesting . . . contextual information . . . outputting the generated query . . . receiving . . . a response . . . outputting the received response” (transmitting data is well‐understood, routine, and conventional, see Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362; buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014); therefore the limitation, which is recited with a high level of generality, remains insignificant extra-solution activity even upon reconsideration);
“retrieving . . . the contextual information” (retrieving information in memory is well‐understood, routine, and conventional, see 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; therefore the limitation, which is recited with a high level of generality, remains insignificant extra-solution activity even upon reconsideration); and
“for processing . . . based on user information associated with the received prompt . . . associated with the received prompt from a knowledge repository . . . to the generated query . . . as a response to the input prompt” (merely generally linking the use of the judicial exception to a particular technological environment or field of use does not provide an inventive concept).
For the reasons above, Claim 9 is rejected as being directed to an abstract idea without significantly more. This rejection applies equally to dependent claims 10-16. The additional limitations of the dependent claims are addressed below.
Regarding Claim 10, the claim recites limitations that are all substantially the same as limitations of Claim 2, in the form of a process. The claim is also directed to performing mental processes without integration into a practical component or significantly more.
Accordingly, Claim 10 is rejected under the same rationale.
Regarding Claim 11, the claim recites limitations that are all substantially the same as limitations of Claim 3, in the form of a process. The claim is also directed to performing mental processes without integration into a practical component or significantly more.
Accordingly, Claim 11 is rejected under the same rationale.
Regarding Claim 12, the claim recites limitations that are all substantially the same as limitations of Claim 4, in the form of a process. The claim is also directed to performing mental processes without integration into a practical component or significantly more.
Accordingly, Claim 12 is rejected under the same rationale.
Regarding Claim 13, the claim recites limitations that are all substantially the same as limitations of Claim 5, in the form of a process. The claim is also directed to performing mental processes without integration into a practical component or significantly more.
Accordingly, Claim 13 is rejected under the same rationale.
Regarding Claim 14, the claim recites limitations that are all substantially the same as limitations of Claim 6, in the form of a process. The claim is also directed to performing mental processes without integration into a practical component or significantly more.
Accordingly, Claim 14 is rejected under the same rationale.
Regarding Claim 15, the claim recites limitations that are all substantially the same as limitations of Claim 7, in the form of a process. The claim is also directed to performing mental processes without integration into a practical component or significantly more.
Accordingly, Claim 15 is rejected under the same rationale.
Regarding Claim 16, the claim recites limitations that are all substantially the same as limitations of Claim 8, in the form of a process. The claim is also directed to performing mental processes without integration into a practical component or significantly more.
Accordingly, Claim 16 is rejected under the same rationale.
Regarding Claim 17:
Step 1: Claim 17 is a machine claim. Therefore, claims 17-24 are directed to a statutory category of eligible subject matter.
Step 2A Prong 1: If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then it falls within the "Mental Processes" grouping of abstract ideas. Here, the claim recites limitations that are substantially the same as the limitations of Claim 1. As a result, and as elaborated above, these limitations are abstract ideas because they are mental processes.
Step 2A Prong 2: This judicial exception is not integrated into a practical application.
The claim recites the additional elements:
“A processing system, comprising . . . means for . . . means for . . . means for . . . means for . . . means for . . . to a generative artificial intelligence model for processing . . . means for . . . from the generative artificial intelligence model . . . means for . . . ” (amounts to mere instructions to apply the judicial exception on generic and unspecialized computer components, which do not impose any meaningful limits on practicing the abstract idea);
“receiving an input prompt . . . requesting . . . contextual information . . . outputting the generated query . . . receiving . . . a response . . . outputting the received response” (receiving, requesting, and outputting is insignificant extra-solution activity because it amounts to the transmission of data, which is incidental to the claimed subject matter);
“retrieving . . . the contextual information” (retrieving is insignificant extra-solution activity because it amounts to retrieving information in memory, which is incidental to the claimed subject matter); and
“for processing . . . based on user information associated with the received prompt . . . associated with the received prompt from a knowledge repository . . . to the generated query . . . as a response to the input prompt” (amounts to merely generally linking the use of the judicial exception to a particular technological environment or field of use, which do not impose any meaningful limits on practicing the abstract idea).
Step 2B: The claim does not include additional elements considered individually and in combination that are sufficient to amount to significantly more than the judicial exception.
The claim recites the additional elements:
“A processing system, comprising . . . means for . . . means for . . . means for . . . means for . . . means for . . . to a generative artificial intelligence model for processing . . . means for . . . from the generative artificial intelligence model . . . means for . . .” (mere instructions to apply the exception using generic computer components does not provide an inventive concept);
“receiving an input prompt . . . requesting . . . contextual information . . . outputting the generated query . . . receiving . . . a response . . . outputting the received response” (transmitting data is well‐understood, routine, and conventional, see Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362; buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014); therefore the limitation, which is recited with a high level of generality, remains insignificant extra-solution activity even upon reconsideration);
“retrieving . . . the contextual information” (retrieving information in memory is well‐understood, routine, and conventional, see 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; therefore the limitation, which is recited with a high level of generality, remains insignificant extra-solution activity even upon reconsideration); and
“for processing . . . based on user information associated with the received prompt . . . associated with the received prompt from a knowledge repository . . . to the generated query . . . as a response to the input prompt” (merely generally linking the use of the judicial exception to a particular technological environment or field of use does not provide an inventive concept).
For the reasons above, Claim 17 is rejected as being directed to an abstract idea without significantly more. This rejection applies equally to dependent claims 18-24. The additional limitations of the dependent claims are addressed below.
Regarding Claim 18, the claim recites limitations that are all substantially the same as limitations of Claim 2, in the form of a machine. The claim is also directed to performing mental processes without integration into a practical component or significantly more.
Accordingly, Claim 18 is rejected under the same rationale.
Regarding Claim 19, the claim recites limitations that are all substantially the same as limitations of Claim 3, in the form of a machine. The claim is also directed to performing mental processes without integration into a practical component or significantly more.
Accordingly, Claim 19 is rejected under the same rationale.
Regarding Claim 20, the claim recites limitations that are all substantially the same as limitations of Claim 4, in the form of a machine. The claim is also directed to performing mental processes without integration into a practical component or significantly more.
Accordingly, Claim 20 is rejected under the same rationale.
Regarding Claim 21, the claim recites limitations that are all substantially the same as limitations of Claim 5, in the form of a machine. The claim is also directed to performing mental processes without integration into a practical component or significantly more.
Accordingly, Claim 21 is rejected under the same rationale.
Regarding Claim 22, the claim recites limitations that are all substantially the same as limitations of Claim 6, in the form of a machine. The claim is also directed to performing mental processes without integration into a practical component or significantly more.
Accordingly, Claim 22 is rejected under the same rationale.
Regarding Claim 23, the claim recites limitations that are all substantially the same as limitations of Claim 7, in the form of a machine. The claim is also directed to performing mental processes without integration into a practical component or significantly more.
Accordingly, Claim 23 is rejected under the same rationale.
Regarding Claim 24, the claim recites limitations that are all substantially the same as limitations of Claim 8, in the form of a machine. The claim is also directed to performing mental processes without integration into a practical component or significantly more.
Accordingly, Claim 24 is rejected under the same rationale.
Regarding Claim 25:
Step 1: Claim 25 is a machine claim. Therefore, claims 25-30 are directed to a statutory category of eligible subject matter.
Step 2A Prong 1: If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then it falls within the "Mental Processes" grouping of abstract ideas. Here, the claim recites limitations that are substantially the same as the limitations of Claim 1. As a result, and as elaborated above, these limitations are abstract ideas because they are mental processes.
Step 2A Prong 2: This judicial exception is not integrated into a practical application.
The claim recites the additional elements:
“A non-transitory computer-readable medium having executable instructions stored thereon which, when executed by one or more processors, performs an operation comprising. . . to a generative artificial intelligence model for processing . . . from the generative artificial intelligence model” (amounts to mere instructions to apply the judicial exception on generic and unspecialized computer components, which do not impose any meaningful limits on practicing the abstract idea);
“receiving an input prompt . . . requesting . . . contextual information . . . outputting the generated query . . . receiving . . . a response . . . outputting the received response” (receiving, requesting, and outputting is insignificant extra-solution activity because it amounts to the transmission of data, which is incidental to the claimed subject matter);
“retrieving . . . the contextual information” (retrieving is insignificant extra-solution activity because it amounts to retrieving information in memory, which is incidental to the claimed subject matter); and
“for processing . . . based on user information associated with the received prompt . . . associated with the received prompt from a knowledge repository . . . to the generated query . . . as a response to the input prompt” (amounts to merely generally linking the use of the judicial exception to a particular technological environment or field of use, which do not impose any meaningful limits on practicing the abstract idea).
Step 2B: The claim does not include additional elements considered individually and in combination that are sufficient to amount to significantly more than the judicial exception.
The claim recites the additional elements:
“A non-transitory computer-readable medium having executable instructions stored thereon which, when executed by one or more processors, performs an operation comprising. . . to a generative artificial intelligence model for processing . . . from the generative artificial intelligence model” (mere instructions to apply the exception using generic computer components does not provide an inventive concept);
“receiving an input prompt . . . requesting . . . contextual information . . . outputting the generated query . . . receiving . . . a response . . . outputting the received response” (transmitting data is well‐understood, routine, and conventional, see Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362; buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014); therefore the limitation, which is recited with a high level of generality, remains insignificant extra-solution activity even upon reconsideration);
“retrieving . . . the contextual information” (retrieving information in memory is well‐understood, routine, and conventional, see 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; therefore the limitation, which is recited with a high level of generality, remains insignificant extra-solution activity even upon reconsideration); and
“for processing . . . based on user information associated with the received prompt . . . associated with the received prompt from a knowledge repository . . . to the generated query . . . as a response to the input prompt” (merely generally linking the use of the judicial exception to a particular technological environment or field of use does not provide an inventive concept).
For the reasons above, Claim 25 is rejected as being directed to an abstract idea without significantly more. This rejection applies equally to dependent claims 26-30. The additional limitations of the dependent claims are addressed below.
Regarding Claim 26, the claim recites limitations that are all substantially the same as limitations of Claim 2, in the form of a machine. The claim is also directed to performing mental processes without integration into a practical component or significantly more.
Accordingly, Claim 26 is rejected under the same rationale.
Regarding Claim 27, the claim recites limitations that are all substantially the same as limitations of Claim 3, in the form of a machine. The claim is also directed to performing mental processes without integration into a practical component or significantly more.
Accordingly, Claim 27 is rejected under the same rationale.
Regarding Claim 28, the claim recites limitations that are all substantially the same as limitations of Claim 4, in the form of a machine. The claim is also directed to performing mental processes without integration into a practical component or significantly more.
Accordingly, Claim 28 is rejected under the same rationale.
Regarding Claim 29, the claim recites limitations that are all substantially the same as limitations of Claim 6, which is dependent on Claim 5, in the form of a machine. The claim is also directed to performing mental processes without integration into a practical component or significantly more.
Accordingly, Claim 29 is rejected under the same rationale.
Regarding Claim 30:
Step 2A Prong 1: See the rejection of Claim 25 above, which Claim 30 depends on.
Step 2A Prong 2: This judicial exception is not integrated into a practical application.
The claim recites the additional elements:
“wherein the knowledge repository comprises one or more of: a knowledge repository hosted on a local network and accessible by a group of users including a user associated with the user information, or a public knowledge repository located on a remote computing system” (amounts to merely generally linking the use of the judicial exception to a particular technological environment or field of use, which do not impose any meaningful limits on practicing the abstract idea).
Step 2B: The claim does not include additional elements considered individually and in combination that are sufficient to amount to significantly more than the judicial exception.
The claim recites the additional elements:
“wherein the knowledge repository comprises one or more of: a knowledge repository hosted on a local network and accessible by a group of users including a user associated with the user information, or a public knowledge repository located on a remote computing system” (merely generally linking the use of the judicial exception to a particular technological environment or field of use does not provide an inventive concept).
Accordingly, Claim 30 is rejected as being directed to an abstract idea without significantly more.
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 1-4, 7-12, 15-20, 23-28, and 30 are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Qin et al. (hereinafter Qin) (Pat. Pub. No. US 2024/0346256 A1).
Regarding Claim 1, Qin teaches a processing system, comprising (Abstract, “Systems, methods, apparatuses, and computer program products are disclosed for using retrieval augmented artificial intelligence to generate a response to a query”, where “Systems” for processing, “for using retrieval augmented artificial intelligence to generate a response to a query”, are disclosed; see also Fig. 2 and Para. [0038], “FIG. 2 shows a block diagram of an example system for employing a retrieval augmented LLM for response generation in accordance with an embodiment”):
at least one memory having executable instructions stored thereon; and one or more processors configured to execute the executable instructions in order to cause the processing system to (Para. [0101], “a system for augmenting a large language model includes: a processor; and a memory device that stores program code structured to cause the processor to . . .”, where the “system” comprises at least one memory, “a memory device”, having executable instructions stored thereon, “stores program code”, and one or more processors, “a processor”, configured to execute the executable instructions in order to cause the processing system to, “structured to cause the processor to . . .”):
receive an input prompt for processing (Fig. 2 and Para. [0039], “Pre-processor 202 may receive contextual information 215 and/or query 216 . . . pre-processor 202 may process query 216”, where “Pre-processor 202” “receive[s]” an input prompt, “query”, for processing, “pre-processor 202 may process query 216”);
request, based on user information associated with the received prompt, contextual information associated with the received prompt from a knowledge repository (Fig. 2 and Para. [0051] – [0052], “comparator 208 may provide, to retriever 210, indication(s) 230 that correspond to the second feature vectors 228 that are most similar to first feature vector 226. As discussed above, indication(s) 230 may include . . . identifiers of augmentation information that correspond to the second feature vectors 228 that are most similar to first feature vector 226 . . . pieces of augmentation information corresponding to the determined second feature vectors are retrieved. For instance, retriever 210 may retrieve augmentation information 232 from dataset(s) 112 that correspond to the indication(s) 230.”, where the “comparator 208 may provide, to retriever 210, indication(s) 230 that correspond to the second feature vectors 228 that are most similar to first feature vector 226”, which are within the broadest reasonable interpretation of a request for contextual information, “augmentation information that correspond to the second feature vectors 228”, associated with the received prompt, “that are most similar to first feature vector 226”, because they “identif[y]” “pieces of augmentation information” and initiate “retriev[al]” of the “pieces of augmentation information” from a knowledge repository, “dataset(s) 112”; see also Para. [0018], “an LLM may be augmented with augmentation information (e.g., domain-specific information; entity-specific information; product-specific information; recent information unavailable at generation of the large language model; or information changed after generation of the large language model)”, where “augmentation information” is within the broadest reasonable interpretation of contextual information because it provides context on “domain-specific information; entity-specific information; product-specific information; recent information unavailable at generation of the large language model; or information changed after generation of the large language model)”; see also Fig. 2 and Para. [0039] - [0043], “pre-processor 202 may further include some or all of contextual information 215 in query text string 220 . . . encoder 204 may process query text string 220 to generate a first feature vector 226. . . comparator 208 receives first feature vector 226 from encoder 204 and second feature vectors 228 from second feature vectors 206, and compares first feature vector 226 to second feature vectors 228 to determine the similarity”, where the request is based on “the first feature vector 226”, which in turn is based on the “query text string 220”, that “include[s] some or all of contextual information 215”, which is within the broadest reasonable interpretation of user information associated with the received prompt, see Para. [0039], “contextual information 215 may describe the context of the user (e.g., user identifier, user role, user profile, user location, browsing history, etc.) and/or the context of the query (e.g., the current webpage, the product or service associated with the current webpage, query timestamp information, etc.)”);
retrieve, from the knowledge repository, the contextual information associated with the input prompt (Fig. 2 and Para. [0045], “Retriever 210 may be configured to determine and retrieve pieces of augmentation information from dataset(s) 112”, where, as discussed above, the “pieces of augmentation information” are the contextual information associated with the input prompt and the “dataset(s) 112” are the knowledge repositories);
generate a query based on the input prompt and the contextual information associated with the input prompt (Fig. 2 and Para. [0046], “Prompt generator 212 may generate a prompt for LLM 214 based on one or more of query 216, first feature vector 226, one or more of second feature vectors 228, indications 230, and/or augmentation information 232”, where the “Prompt generator 212” generates a query, “generate a prompt for LLM 214”, based on the input prompt, “based on one or more of query 216”, and the contextual information associated with the input prompt, “and . . . augmentation information 232”; see also Para. [0046], “”For example, prompt generator 212 may generate an augmented prompt 236 that includes the original query, contextual information (e.g., the current webpage, the product or service of the current webpage, temporal information, location information, etc.), content information (e.g., the retrieved augmentation information 232), and a request to answer the original query based on the provided contextual information using the included content information”, where the generated prompt is “an augmented prompt 236”);
output the generated query to a generative artificial intelligence model for processing (Fig. 2 and Para. [0047], “LLM 214 receives augmented prompt 236 from prompt generator 212 . . . LLM 214 may process prompt augmented 236”, where the generated query, “augmented prompt 236”, is outputted “from prompt generator 212” and to a generative intelligence model, “LLM 214 receives”, for processing, “LLM 214 may process prompt augmented 236”; see generally Para. [0016], “Large language models (LLM) (e.g., ChatGPT™ developed by OpenAI) are machine learning models designed to generate human-like text for a wide range of applications, including chatbots, language translation, and content creation”);
receive, from the generative artificial intelligence model, a response to the generated query (Fig. 2 and Para. [0047], “LLM 214 receives augmented prompt 236 from prompt generator 212 and generates response 238 . . . LLM 214 prioritizes augmentation information 232 over information in its training data when generating the response 238. In embodiments, LLM 214 may determine that augmentation information 232 does not contain an answer to the query and may generate a response that is not based on augmentation information 232. For instance, LLM 214 may respond by indicating that it does not know the answer to the query, by generating a response based on the information in its training data, and/or by asking the user to clarify their query”, where a response to the generated query, “generates response 238” or “LLM 214 may respond by indicating that it does not know the answer to the query, by generating a response based on the information in its training data, and/or by asking the user to clarify their query”, from the generative artificial intelligence model, “LLM”, is received by the “GUI Manager 108” as output from the “Response Generator 110”, see Fig. 1-3 and Para. [0054], “a response generated by the large language model is received. For instance, GUI manager 108 may receive from response generator 110 a response 238 generated by LLM 214”); and
output the received response as a response to the input prompt (Fig. 1 and Para. [0034], “GUI manager 108 receives a query from GUI 114 and provides the query to response generator 110. GUI manager 108 may also provide a response from response generator 110 to GUI 114”, where the “GUI manager 108” outputs the received response, “a response from response generator 110”, to the “GUI 114” in response to the input prompt, “GUI manager 108 receives a query from GUI 114”; see also Para. [0028], “Augmenting an LLM with retrieved augmentation information may improve responses in a variety of situations. For instance, a search engine or chatbot on an internal corporate website may provide employees with accurate responses when presented with a query that is directed to internal or proprietary information. In another example, an external-facing company webpage may provide customers with responses that are focused on the company's products or services”, where “Augmenting an LLM with retrieved augmentation information” allows for the output of the received response, “provide employees with accurate responses” or “provide customers with responses”, as a response to the input query, “when presented with a query”).
Regarding Claim 2, Qin teaches the processing system of Claim 1, wherein to request the contextual information from the knowledge repository, the one or more processors are configured to cause the processing system to (Fig. 2 and Para. [0051] – [0052], “comparator 208 may provide, to retriever 210, indication(s) 230 that correspond to the second feature vectors 228 that are most similar to first feature vector 226. As discussed above, indication(s) 230 may include . . . identifiers of augmentation information that correspond to the second feature vectors 228 that are most similar to first feature vector 226 . . . pieces of augmentation information corresponding to the determined second feature vectors are retrieved. For instance, retriever 210 may retrieve augmentation information 232 from dataset(s) 112 that correspond to the indication(s) 230.”, where the “comparator 208 may provide, to retriever 210, indication(s) 230 that correspond to the second feature vectors 228 that are most similar to first feature vector 226”, which are within the broadest reasonable interpretation of a request for contextual information, “augmentation information that correspond to the second feature vectors 228”, associated with the received prompt, “that are most similar to first feature vector 226”, because they “identif[y]” “pieces of augmentation information” and initiate “retriev[al]” of the “pieces of augmentation information” from a knowledge repository, “dataset(s) 112”; see also Para. [0101], “a system for augmenting a large language model includes: a processor; and a memory device that stores program code structured to cause the processor to . . .”)
identify the knowledge repository from a group of repositories (Fig. 2 and Para. [0052], “retriever 210 may retrieve augmentation information 232 from dataset(s) 112 that correspond to the indication(s) 230.”, where the knowledge repository, one of “dataset(s) 112”, from the group of repositories, “dataset(s) 112”, must be identified for “augmentation information 232” to be retrieved from it)
for which a user associated with the user information (Fig. 1 and Para. [0030] – [0033], “FIG. 1 shows a block diagram of an example system 100 for generating a response using a retrieval augmented LLM, in accordance with an embodiment. As shown in FIG. 1, system 100 may include one or more servers 102 connected to one or more clients 104 via one or more networks 106. One or more of servers 102 may further include a graphical user interface (GUI) manager 108, a response generator 110, and one or more datasets 112. Each of clients 104 may further include a GUI 114 . . . Network(s) 106 may comprise one or more networks such as local area networks (LANs)”, where the knowledge repository, “Dataset(s)” is accessible through “Network(s) 106” to a group of users, the operators of “Client(s) 104” with the “graphical user interface[s]”, which, as discussed above, are the users associated with the user information, see Para. [0039], “contextual information 215 may describe the context of the user (e.g., user identifier, user role, user profile, user location, browsing history, etc.) and/or the context of the query (e.g., the current webpage, the product or service associated with the current webpage, query timestamp information, etc.)”; see also Para. [0028], “Augmenting an LLM with retrieved augmentation information may improve responses in a variety of situations. For instance, a search engine or chatbot on an internal corporate website may provide employees with accurate responses when presented with a query that is directed to internal or proprietary information”)
has access permissions (Para. [0028], “Augmenting an LLM with retrieved augmentation information may improve responses in a variety of situations. For instance, a search engine or chatbot on an internal corporate website may provide employees with accurate responses when presented with a query that is directed to internal or proprietary information. In another example, an external-facing company webpage may provide customers with responses that are focused on the company's products or services”, where users that are “employees” have access permissions for “internal or proprietary information” and users that are “customers” have access permissions for “the company's products or services”).
Regarding Claim 3, Qin teaches the processing system of Claim 1, wherein: to request the contextual information from the knowledge repository, the one or more processors are configured to
cause the processing system to request the contextual information from a plurality of knowledge repositories (Fig. 2 and Para. [0051] – [0052], “comparator 208 may provide, to retriever 210, indication(s) 230 that correspond to the second feature vectors 228 that are most similar to first feature vector 226. As discussed above, indication(s) 230 may include . . . identifiers of augmentation information that correspond to the second feature vectors 228 that are most similar to first feature vector 226 . . . pieces of augmentation information corresponding to the determined second feature vectors are retrieved. For instance, retriever 210 may retrieve augmentation information 232 from dataset(s) 112 that correspond to the indication(s) 230.”, where the “comparator 208 may provide, to retriever 210, indication(s) 230 that correspond to the second feature vectors 228 that are most similar to first feature vector 226”, which are within the broadest reasonable interpretation of a request for contextual information, “augmentation information that correspond to the second feature vectors 228”, associated with the received prompt, “that are most similar to first feature vector 226”, because they “identif[y]” “pieces of augmentation information” and initiate “retriev[al]” of the “pieces of augmentation information” from a plurality of knowledge repositories, “dataset(s) 112”; see also Para. [0101], “a system for augmenting a large language model includes: a processor; and a memory device that stores program code structured to cause the processor to . . .”);
and to retrieve the contextual information, the one or more processors are configured to cause the processing system to receive the contextual information from one or more knowledge repositories of the plurality of knowledge repositories, the one or more knowledge repositories comprising knowledge repositories (Fig. 2 and Para. [0045], “Retriever 210 may be configured to determine and retrieve pieces of augmentation information from dataset(s) 112”, where, as discussed above, the “pieces of augmentation information” are the contextual information associated with the input prompt and the “dataset(s) 112” are the one or more knowledge repositories of a plurality of knowledge repositories; see also Para. [0101], “a system for augmenting a large language model includes: a processor; and a memory device that stores program code structured to cause the processor to . . .”)
which a user associated with the user information (Fig. 1 and Para. [0030] – [0033], “FIG. 1 shows a block diagram of an example system 100 for generating a response using a retrieval augmented LLM, in accordance with an embodiment. As shown in FIG. 1, system 100 may include one or more servers 102 connected to one or more clients 104 via one or more networks 106. One or more of servers 102 may further include a graphical user interface (GUI) manager 108, a response generator 110, and one or more datasets 112. Each of clients 104 may further include a GUI 114 . . . Network(s) 106 may comprise one or more networks such as local area networks (LANs)”, where the knowledge repository, “Dataset(s)” is accessible through “Network(s) 106” to a group of users, the operators of “Client(s) 104” with the “graphical user interface[s]”, which, as discussed above, are the users associated with the user information, see Para. [0039], “contextual information 215 may describe the context of the user (e.g., user identifier, user role, user profile, user location, browsing history, etc.) and/or the context of the query (e.g., the current webpage, the product or service associated with the current webpage, query timestamp information, etc.)”; see also Para. [0028], “Augmenting an LLM with retrieved augmentation information may improve responses in a variety of situations. For instance, a search engine or chatbot on an internal corporate website may provide employees with accurate responses when presented with a query that is directed to internal or proprietary information”)
has permissions to access (Para. [0028], “Augmenting an LLM with retrieved augmentation information may improve responses in a variety of situations. For instance, a search engine or chatbot on an internal corporate website may provide employees with accurate responses when presented with a query that is directed to internal or proprietary information. In another example, an external-facing company webpage may provide customers with responses that are focused on the company's products or services”, where users that are “employees” have access permissions for “internal or proprietary information” and users that are “customers” have access permissions for “the company's products or services”).
Regarding Claim 4, Qin teaches the processing system of Claim 1, wherein the knowledge repository comprises a knowledge repository co-located with the generative artificial intelligence model (Fig. 1-2, where the knowledge repository is co-located with the “Response Generator 110” on the “Server . . . 102”, and is thus collocated with the generative artificial intelligence model, “Large Language Model 214”, contained in the “Response Generator 110”; see also Para. [0030], “One or more of servers 102 may further include a graphical user interface (GUI) manager 108, a response generator 110, and one or more datasets 112”).
Regarding Claim 7, Qin teaches the processing system of Claim 1, wherein the knowledge repository comprises a knowledge repository hosted on a local network (Fig. 1-2, where, as discussed in detail above, the “Datasets” are the knowledge repositories, which comprises a knowledge repository hosted on a local network, see Para. [0088] - [0089], “Application data 898 may be shared by on-premises servers 892 . . . through a local network of the organization . . . Embodiments described herein may be implemented in . . . on-premises servers 892”, where the “on-premises servers 892” are hosted on a local network, “through a local network of the organization”)
and accessible by a group of users including a user associated with the user information (Fig. 1 and Para. [0030] – [0033], “FIG. 1 shows a block diagram of an example system 100 for generating a response using a retrieval augmented LLM, in accordance with an embodiment. As shown in FIG. 1, system 100 may include one or more servers 102 connected to one or more clients 104 via one or more networks 106. One or more of servers 102 may further include a graphical user interface (GUI) manager 108, a response generator 110, and one or more datasets 112. Each of clients 104 may further include a GUI 114 . . . Network(s) 106 may comprise one or more networks such as local area networks (LANs)”, where the knowledge repository, “Dataset(s)” is accessible through “Network(s) 106” to a group of users, the operators of “Client(s) 104” with the “graphical user interface[s]”, which, as discussed above, are the users associated with the user information, see Para. [0039], “contextual information 215 may describe the context of the user (e.g., user identifier, user role, user profile, user location, browsing history, etc.) and/or the context of the query (e.g., the current webpage, the product or service associated with the current webpage, query timestamp information, etc.)”; see also Para. [0028], “Augmenting an LLM with retrieved augmentation information may improve responses in a variety of situations. For instance, a search engine or chatbot on an internal corporate website may provide employees with accurate responses when presented with a query that is directed to internal or proprietary information”).
Regarding Claim 8, Qin teaches the processing system of Claim 1, wherein the knowledge repository comprises a public knowledge repository (Fig. 1-2, where, as discussed in detail above, the “Datasets” are the knowledge repositories, which comprises a public knowledge repository, see Para. [0028], “Augmenting an LLM with retrieved augmentation information may improve responses in a variety of situations. For instance . . . an external-facing company webpage may provide customers with responses that are focused on the company's products or services”, where a database for “an external facing webpage” that “provide[s] customers with responses that are focused on the company's products or services” is within the broadest reasonable interpretation of a public knowledge repository; see also Para. [0084], “Each of nodes 874 may, as a compute node, comprise one or more server computers, server systems, and/or computing devices. For instance, a node 874 may include one or more of the components of computing device 802 disclosed herein. Each of nodes 874 may be configured to execute one or more software applications (or “applications”) and/or services and/or manage hardware resources (e.g., processors, memory, etc.), which may be utilized by users (e.g., customers) of the network-accessible server set”)
located on a remote computing system (Fig. 1, where the “Dataset(s) 112” are located on a remote computing system, “Server(s) 102”; see also Para. [0033], “Network(s) 106 may comprise one or more networks such as . . . enterprise networks, the Internet, etc., and may include wired and/or wireless portions. Server(s) 102 and client(s) 104 may be communicatively coupled via network(s) 106”, where “Server(s)” connected to “client(s)” through “Internet” “networks” are within the broadest reasonable interpretation of remote computing systems; see also Para. [0084] – [0089], “Embodiments described herein may be implemented in one or more of computing device 802, network-based server infrastructure 870, and on-premises servers 892. For example, in some embodiments, computing device 802 may be used to implement systems, clients, or devices, or components/subcomponents thereof, disclosed elsewhere herein. In other embodiments, a combination of computing device 802, network-based server infrastructure 870, and/or on-premises servers 892 may be used to implement the systems, clients, or devices, or components/subcomponents thereof, disclosed elsewhere herein”).
Regarding Claim 9, Qin teaches a processor-implemented method, comprising . . . (Abstract, “Systems, methods, apparatuses, and computer program products are disclosed for using retrieval augmented artificial intelligence to generate a response to a query”; see also Figs. 1-3 and Para. [0048] - [0049], “FIG. 3 depicts a flowchart 300 of a process for generating a response using a retrieval augmented LLM, in accordance with an embodiment . . . Flowchart 300 is described as follows with respect to FIGS. 1 and 2 . . . Flowchart 300 starts at step 302. In Step 302, a query is received. For instance, pre-processor 202 of response generator 110 may receive query 216”, where the method, “FIG. 3 depicts a flowchart 300 of a process” is processor-implemented, including “pre-processor 202 of response generator 110 may receive query 216”).
The remaining limitations are substantially the same as limitations of Claim 1, therefore it is rejected under the same rationale.
Regarding Claim 10, the additional elements of the dependent claim are substantially the same as limitations of Claim 2, therefore it is rejected under the same rationale.
Regarding Claim 11, the additional elements of the dependent claim are substantially the same as limitations of Claim 3, therefore it is rejected under the same rationale.
Regarding Claim 12, the additional elements of the dependent claim are substantially the same as limitations of Claim 4, therefore it is rejected under the same rationale.
Regarding Claim 15, the additional elements of the dependent claim are substantially the same as limitations of Claim 7, therefore it is rejected under the same rationale.
Regarding Claim 16, the additional elements of the dependent claim are substantially the same as limitations of Claim 8, therefore it is rejected under the same rationale.
Regarding Claim 17, Qin teaches a processing system, comprising (Abstract, “Systems, methods, apparatuses, and computer program products are disclosed for using retrieval augmented artificial intelligence to generate a response to a query”, where “Systems” for processing, “for using retrieval augmented artificial intelligence to generate a response to a query”, are disclosed; see also Fig. 2 and Para. [0038], “FIG. 2 shows a block diagram of an example system for employing a retrieval augmented LLM for response generation in accordance with an embodiment”):
means for . . . (Fig. 1-3 and Para. [0030] and [0038], “As shown in FIG. 1, system 100 may include one or more servers 102 connected to one or more clients 104 via one or more networks 106. One or more of servers 102 may further include a graphical user interface (GUI) manager 108, a response generator 110, and one or more datasets 112. Each of clients 104 may further include a GUI 114 . . . As shown in FIG. 2, system 200 includes response generator 110 and dataset(s) 112 as shown and described with respect to FIG. 1. Response generator 110 further includes a pre-processor 202, an encoder 204, a plurality of second feature vectors 206, a comparator 208, a retriever 210, a prompt generator 212, and a large language model (LLM) 214. These features of system 200 are described in further detail as follows”, where each reference number is associated with a component of the system, “system 100” or “system 200”, which, collectively or individually, are within the broadest reasonable interpretation of a means for performing their associated function; redundant recitations of “means for” omitted; see also Para. [0101], “a system for augmenting a large language model includes: a processor; and a memory device that stores program code structured to cause the processor to . . .”).
The remaining limitations are substantially the same as limitations of Claim 1, therefore it is rejected under the same rationale.
Regarding Claim 18, the additional elements of the dependent claim are substantially the same as limitations of Claim 2, therefore it is rejected under the same rationale.
Regarding Claim 19, the additional elements of the dependent claim are substantially the same as limitations of Claim 3, therefore it is rejected under the same rationale.
Regarding Claim 20, the additional elements of the dependent claim are substantially the same as limitations of Claim 4, therefore it is rejected under the same rationale.
Regarding Claim 23, the additional elements of the dependent claim are substantially the same as limitations of Claim 7, therefore it is rejected under the same rationale.
Regarding Claim 24, the additional elements of the dependent claim are substantially the same as limitations of Claim 8, therefore it is rejected under the same rationale.
Regarding Claim 25, Qin teaches a non-transitory computer-readable medium having executable instructions stored thereon which, when executed by one or more processors, performs an operation comprising . . . (Para. [0109], “In an embodiment, a computer-readable storage medium comprising computer-executable instructions, that when executed by a processor, cause the processor to . . .”, where “a computer-readable storage medium” having executable instructions stored thereon, “comprising computer-executable instructions”, that performs operations when executed by one or more processors, “when executed by a processor, cause the processor to . . .”, and the “computer-readable medium” is non-transitory, see Para. [0090], “As used herein, the terms “computer program medium,” “computer-readable medium,” and “computer-readable storage medium,” etc., are used to refer to physical hardware media. Examples of such physical hardware media include any hard disk, optical disk, SSD, other physical hardware media such as RAMs, ROMs, flash memory, digital video disks, zip disks, MEMs (microelectronic machine) memory, nanotechnology-based storage devices, and further types of physical/tangible hardware storage media of storage”).
The remaining limitations are substantially the same as limitations of Claim 1, therefore it is rejected under the same rationale.
Regarding Claim 26, the additional elements of the dependent claim are substantially the same as limitations of Claim 2, therefore it is rejected under the same rationale.
Regarding Claim 27, the additional elements of the dependent claim are substantially the same as limitations of Claim 3, therefore it is rejected under the same rationale.
Regarding Claim 28, the additional elements of the dependent claim are substantially the same as limitations of Claim 4, therefore it is rejected under the same rationale.
Regarding Claim 30, Qin teaches the computer-readable medium of Claim 25, wherein the knowledge repository comprises one or more of: a knowledge repository hosted on a local network (Fig. 1-2, where, as discussed in detail above, the “Datasets” are the knowledge repositories, which comprises a knowledge repository hosted on a local network, see Para. [0088] - [0089], “Application data 898 may be shared by on-premises servers 892 . . . through a local network of the organization . . . Embodiments described herein may be implemented in . . . on-premises servers 892”, where the “on-premises servers 892” are hosted on a local network, “through a local network of the organization”)
and accessible by a group of users including a user associated with the user information (Fig. 1 and Para. [0030] – [0033], “FIG. 1 shows a block diagram of an example system 100 for generating a response using a retrieval augmented LLM, in accordance with an embodiment. As shown in FIG. 1, system 100 may include one or more servers 102 connected to one or more clients 104 via one or more networks 106. One or more of servers 102 may further include a graphical user interface (GUI) manager 108, a response generator 110, and one or more datasets 112. Each of clients 104 may further include a GUI 114 . . . Network(s) 106 may comprise one or more networks such as local area networks (LANs)”, where the knowledge repository, “Dataset(s)” is accessible through “Network(s) 106” to a group of users, the operators of “Client(s) 104” with the “graphical user interface[s]”, which, as discussed above, are the users associated with the user information, see Para. [0039], “contextual information 215 may describe the context of the user (e.g., user identifier, user role, user profile, user location, browsing history, etc.) and/or the context of the query (e.g., the current webpage, the product or service associated with the current webpage, query timestamp information, etc.)”; see also Para. [0028], “Augmenting an LLM with retrieved augmentation information may improve responses in a variety of situations. For instance, a search engine or chatbot on an internal corporate website may provide employees with accurate responses when presented with a query that is directed to internal or proprietary information”),
or a public knowledge repository (Fig. 1-2, where, as discussed in detail above, the “Datasets” are the knowledge repositories, which comprises a public knowledge repository, see Para. [0028], “Augmenting an LLM with retrieved augmentation information may improve responses in a variety of situations. For instance . . . an external-facing company webpage may provide customers with responses that are focused on the company's products or services”, where a database for “an external facing webpage” that “provide[s] customers with responses that are focused on the company's products or services” is within the broadest reasonable interpretation of a public knowledge repository; see also Para. [0084], “Each of nodes 874 may, as a compute node, comprise one or more server computers, server systems, and/or computing devices. For instance, a node 874 may include one or more of the components of computing device 802 disclosed herein. Each of nodes 874 may be configured to execute one or more software applications (or “applications”) and/or services and/or manage hardware resources (e.g., processors, memory, etc.), which may be utilized by users (e.g., customers) of the network-accessible server set”)
located on a remote computing system (Fig. 1, where the “Dataset(s) 112” are located on a remote computing system, “Server(s) 102”; see also Para. [0033], “Network(s) 106 may comprise one or more networks such as . . . enterprise networks, the Internet, etc., and may include wired and/or wireless portions. Server(s) 102 and client(s) 104 may be communicatively coupled via network(s) 106”, where “Server(s)” connected to “client(s)” through “Internet” “networks” are within the broadest reasonable interpretation of remote computing systems; see also Para. [0084] – [0089], “Embodiments described herein may be implemented in one or more of computing device 802, network-based server infrastructure 870, and on-premises servers 892. For example, in some embodiments, computing device 802 may be used to implement systems, clients, or devices, or components/subcomponents thereof, disclosed elsewhere herein. In other embodiments, a combination of computing device 802, network-based server infrastructure 870, and/or on-premises servers 892 may be used to implement the systems, clients, or devices, or components/subcomponents thereof, disclosed elsewhere herein”).
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 5-6, 13-14, 21-22, and 29 are rejected under 35 U.S.C. 103 as being unpatentable over Qin in view of Singer et al. (hereinafter Singer) (Pat. Pub. No. US 2022/0101096 A1).
Regarding Claim 5, Qin teaches the processing system of Claim 4, wherein the knowledge repository and the generative artificial intelligence model are co-located on . . . [a server] that received the input prompt (Fig. 1-2, where the knowledge repository is co-located with the “Response Generator 110” which receives the input prompt, see Para. [0039], “Pre-processor 202 may receive contextual information 215 and/or query 216”, on the “Server . . . 102”, and is thus collocated with the generative artificial intelligence model, “Large Language Model 214”, contained in the “Response Generator 110”; see also Para. [0030], “One or more of servers 102 may further include a graphical user interface (GUI) manager 108, a response generator 110, and one or more datasets 112”).
Qin does not explicitly disclose . . . an edge device . . . (where the server is not specifically described as an edge device).
However, Singer teaches . . . [a knowledge-based deep learning refactoring system using retrieval augmented generation] (Abstract, “Methods and apparatus for a knowledge-based deep learning refactoring model with tightly integrated functional nonparametric memory are disclosed”, where an “apparatus” is part of a “system” that uses “Retrieval-Augmented Generation (RAG)”, see Para. [0076], “FIG. 5 is a table 500 illustrating a comparison of features of the KBRM system 100 of FIG. 1 with that of previous approaches. The table 500 includes previous approaches such as Retrieval-Augmented Generation (RAG) 504”)
[wherein the server is] . . . an edge device . . . (Para. [0092], “the machine readable instructions may be fragmented and stored on one or more storage devices and/or computing devices (e.g., servers) located at the same or different locations of a network or collection of networks (e.g., in the cloud, in edge devices, etc.”).
Before the effective filing date of the invention, it would have been obvious to one of ordinary skill in the art to combine the retrieval augmented generation system, wherein the knowledge repository and the generative artificial intelligence model are co-located on a server that received the input prompt of Qin with the knowledge-based deep learning refactoring system using retrieval augmented generation wherein the server is an edge device of Singer in order to utilize edge devices for server functionality (Qin, Para. [0030], “One or more of servers 102 may further include a graphical user interface (GUI) manager 108, a response generator 110, and one or more datasets 112”; Singer, Para. [0092], “the machine readable instructions may be fragmented and stored on one or more storage devices and/or computing devices (e.g., servers) located at the same or different locations of a network or collection of networks (e.g., in the cloud, in edge devices, etc.”), which will reduce power and cost expenditures of the system by operating the servers on less computationally intensive hardware (compare Qin, Para. [0083], “Server infrastructure 870, when present, may be a network-accessible server set (e.g., a cloud-based environment or platform)” with Singer, Para. [0032], “The pace of growth of Language Models and other large DL models is costly and unsustainable. By refactoring the DL models and retaining the vast majority of information in an auxiliary KB, model size can be reduced by orders of magnitude and the pace of model growth substantially curtailed . . . Reducing model size opens more opportunities to deploy highly capable AI approaches in systems with power and cost constrains, such as in Edge computing”).
Regarding Claim 6, Qin in view of Singer teach the processing system of Claim 5, wherein the one or more processors are further configured to cause the processing system to (Qin, Para. [0101], “a system for augmenting a large language model includes: a processor; and a memory device that stores program code structured to cause the processor to . . .”):
retrieve information related to the query from an external resource; and update the received response based on the information retrieved from the external resource (Qin, Fig. 1-2; Qin, Para. [0047], “LLM 214 receives augmented prompt 236 from prompt generator 212 and generates response 238 . . . LLM 214 prioritizes augmentation information 232 over information in its training data when generating the response 238. In embodiments, LLM 214 may determine that augmentation information 232 does not contain an answer to the query and may generate a response that is not based on augmentation information 232. For instance, LLM 214 may respond by indicating that it does not know the answer to the query, by generating a response based on the information in its training data, and/or by asking the user to clarify their query”; and Qin, Para. [0018], “Embodiments are disclosed herein that improve the scope and accuracy of responses generated by an LLM. For instance, in embodiments, an LLM may be augmented with augmentation information . . . A retrieval augmented generation (RAG) approach is disclosed herein that adds an information retrieval component to create augmented prompts to feed into the generative language model for generating the final answer/prediction”, where “asking the user to clarify their query” in order to “improve the scope and accuracy of responses generated by an LLM”, requires retrieval of information related to the query, the “clarify[ication of] their query” from an external resource, the “Client(s) 104” “GUI 114”, which will update the received response based on the information retrieved from the external resource, “improve the scope and accuracy of responses generated by an LLM” using the “clarify[ication of] their query”).
Regarding Claim 13, the additional elements of the dependent claim are substantially the same as limitations of Claim 5, therefore it is rejected under the same rationale.
Regarding Claim 14, the additional elements of the dependent claim are substantially the same as limitations of Claim 6, therefore it is rejected under the same rationale.
Regarding Claim 21, the additional elements of the dependent claim are substantially the same as limitations of Claim 5, therefore it is rejected under the same rationale.
Regarding Claim 22, the additional elements of the dependent claim are substantially the same as limitations of Claim 6, therefore it is rejected under the same rationale.
Regarding Claim 29, the additional elements of the dependent claim are substantially the same as limitations of Claim 6, which is dependent upon Claim 5, therefore it is rejected under the same rationale.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Apon et al. (“Shibboleth as a Tool for Authorized Access Control to the Subversion Repository System”) discloses an access control system for knowledge repositories.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to MATTHEW BRYCE GOLAN whose telephone number is (571)272-5159. The examiner can normally be reached Monday through Friday, 8:00 AM to 5:00 PM ET.
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/MATTHEW BRYCE GOLAN/Examiner, Art Unit 2123
/ALEXEY SHMATOV/Supervisory Patent Examiner, Art Unit 2123
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