DETAILED ACTION
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 .
Claim Interpretation
The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph:
An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The claims in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. The broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification when 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is invoked.
As explained in MPEP § 2181, subsection I, claim limitations that meet the following three-prong test will be interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph:
(A) the claim limitation uses the term “means” or “step” or a term used as a substitute for “means” that is a generic placeholder (also called a nonce term or a non-structural term having no specific structural meaning) for performing the claimed function;
(B) the term “means” or “step” or the generic placeholder is modified by functional language, typically, but not always linked by the transition word “for” (e.g., “means for”) or another linking word or phrase, such as “configured to” or “so that”; and
(C) the term “means” or “step” or the generic placeholder is not modified by sufficient structure, material, or acts for performing the claimed function.
Use of the word “means” (or “step”) in a claim with functional language creates a rebuttable presumption that the claim limitation is to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites sufficient structure, material, or acts to entirely perform the recited function.
Absence of the word “means” (or “step”) in a claim creates a rebuttable presumption that the claim limitation is not to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites function without reciting sufficient structure, material or acts to entirely perform the recited function.
Claim limitations in this application that use the word “means” (or “step”) are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action. Conversely, claim limitations in this application that do not use the word “means” (or “step”) are not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action.
This application includes one or more claim limitations that does not use the word “means,” and are interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because the claim limitation(s) uses a generic placeholder that is coupled with functional language without reciting sufficient structure to perform the recited function and the generic placeholder is not preceded by a structural modifier. Such claim limitation(s) is/are, where the generic place holder has been underlined and the functional language italicize:
In claim 18, “one or more analysis modules configured identify new data for the knowledge base, wherein the one or more analysis modules are further configured to update the knowledge base with the new data;
and a record keeping module configured to record changes made to the knowledge base and the response generated using the AI model”
Because this/these claim limitation(s) is/are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, it/they is/are being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof.
If applicant does not intend to have this/these limitation(s) interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may: (1) amend the claim limitation(s) to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitation(s) recite(s) sufficient structure to perform the claimed function so as to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph.
Claim Rejections - 35 USC § 112(b)
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claim 2-3, 8, 12 and 14-15 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claim 2 and analogous claim 14 recites “wherein the one or more scoring agents are configured to respectively generate individual scores for the AI model;” it is unclear if the BRI of the limitation is:
one scoring agent generates multiple individual scores or
A one-to-many relationship.
each scoring agent of the one or more scoring agents generates one individual scores
A one-to-one relationship.
For examination purposes, Examiner interprets the BRI of the claim as 2., wherein one scoring agent generates one individual score.
Claims 3 and 15 are further rejected on virtue of their dependencies to the rejected base claims 2 and 14.
Claim 8 recites “obtaining information updating the data stored in the knowledge base;” it is unclear whether the obtaining or the updating occurs first. For examination purposes, Examiner interprets the limitation as “obtaining information to update the data stored in the knowledge base.”
Claim 12 recites “wherein the report is customized based on a user using requesting the report;” it is unclear if the report is customized based on “a user using the report” or “a user requesting the report.” For examination purposes, Examiner interprets the limitation as “a user using the report.”
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 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
In regards to claim 1,
Step 1: This part of the eligibility analysis evaluates whether the claim(s) falls within any statutory category. See MPEP 2106.03:
The claim directs to a statutory category – process.
Step 2A – Prong 1: Judicial Exception Recited?
MPEP 2106.04(a)(2)(I) “Accordingly, the "mental processes" abstract idea grouping is defined as concepts performed in the human mind, and examples of mental processes include observations, evaluations, judgments, and opinions.”
Further, the MPEP recites “The courts do not distinguish between mental processes that are performed entirely in the human mind and mental processes that require a human to use a physical aid (e.g., pen and paper or a slide rule) to perform the claim limitation.”
Yes, the claim recites a mental process, specifically:
performing analysis of the AI model
This limitation encompasses an evaluation of the AI model.
generating a report including results of the analysis
This limitation encompasses providing an opinion (report) of the previous evaluation.
Therefore, the claim recites a mental process.
Step 2A – Prong 2: Integrated into a Practical Solution?
MPEP 2106.05(f) Mere Instructions To Apply An Exception has found simply adding a general purpose computer or computer components after the fact to an abstract idea (e.g., a fundamental economic practice or mathematical equation) does not integrate a judicial exception into a practical application or provide significantly more. The following steps are mere instructions to apply:
A method for performing a generative artificial intelligence model analytics operation…
[performing analysis of the AI model] using one or more scoring agents
MPEP 2106.05(g) Insignificant Extra-Solution Activity has found mere data gathering/post solution activity to be insignificant extra-solution activity. The following steps are insignificant extra-solution activities:
Mere data gathering:
obtaining an artificial intelligence (AI) model
Post-solution activity:
providing the report on a user interface
The additional elements have been considered both individually and as an ordered combination in to determine whether they integrate the exception into a practical application. Therefore, no meaningful limits are imposed on practicing the abstract idea.
The claim is directed to the abstract idea.
Step 2B: Claim provides an Inventive Concept?
No, as discussed with respect to Step 2A, the additional limitation is mere data gathering/post-solution activity (Insignificant Extra-Solution Activity) and Mere Instructions To Apply An Exception and a generic device do not impose any meaningful limits on practicing the abstract idea and therefore the claim does not provide an inventive concept in Step 2B.
The recitation of claim limitations that attempt to cover any solution to an identified problem with no restriction on how the result is accomplished and no description of the mechanism for accomplishing the result, does not integrate a judicial exception into a practical application or provide significantly more because this type of recitation is equivalent to the words "apply it".
Further, the claim recites receiving data and transmitting data by generic device.
This has been determined to be insignificant extra-solution activity as found in MPEP § 2106.05(d)(II)(i): Receiving or transmitting data over a network, e.g., using the Internet to
gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary
computer to forward information); TLI Communications LLC v. AV Auto. LLC, 823 F.3d 607,
610, 118 USPQ2d 1744, 1745 (Fed. Cir. 2016) (using a telephone for image transmission); OIP
Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015)
(sending messages over a network); buy SAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112
USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network);
but see DDR Holdings, LLC v. Hotels.com, L.P., 773 F.3d 1245, 1258, 113 USPQ2d 1097, 1106
(Fed. Cir. 2014) ("Unlike the claims in Ultramercial, the claims at issue here specify how
interactions with the Internet are manipulated to yield a desired result‐‐a result that overrides
the routine and conventional sequence of events ordinarily triggered by the click of a hyperlink."
(emphasis added)).
The additional elements have been considered both individually and as an ordered
combination in the significantly more consideration.
The claim is ineligible.
In regards to claim 2,
Step 1: This part of the eligibility analysis evaluates whether the claim(s) falls within any statutory category. See MPEP 2106.03:
The claim directs to a statutory category – process.
Step 2A Prong 1: The claim recites the following abstract ideas:
The abstract idea(s) in the parent claim(s).
respectively generate individual scores for the AI model
This limitation directs to a mental process that can be performed in the human mind, by a human using pen and paper, or using a computer as a tool to perform the concept and encompasses providing an opinion (individual scores) for the AI model. See MPEP 2106.04(a)(2)(III)
Step 2A Prong 2: The claim recites the following additional elements which, considered individually and as an ordered combination, do not integrate the abstract idea into a practical application:
The additional element(s) in the parent claim(s).
wherein the one or more scoring agents are configured to
This limitation directs to merely applying (or equivalent) an abstract idea, or implementing an abstract idea on a computer, or using a computer as a tool to perform an abstract idea. See MPEP 2106.05(f)
Step 2B: The claim recites the following additional elements which, considered individually and as an ordered combination, do not amount to significantly more than the abstract idea:
The additional element(s) in the parent claim(s).
wherein the one or more scoring agents are configured to
This limitation directs to merely applying (or equivalent) an abstract idea, or implementing an abstract idea on a computer, or using a computer as a tool to perform an abstract idea. See MPEP 2106.05(f)
In regards to claim 3,
Step 1: This part of the eligibility analysis evaluates whether the claim(s) falls within any statutory category. See MPEP 2106.03:
The claim directs to a statutory category – process.
Step 2A Prong 1: The claim recites the following abstract ideas:
The abstract idea(s) in the parent claim(s).
comparison of the individual scores
This limitation directs to a mental process that can be performed in the human mind, by a human using pen and paper, or using a computer as a tool to perform the concept. See MPEP 2106.04(a)(2)(III)
aggregated scores generated by aggregating the individual scores
This limitation directs to a mathematical calculation. See MPEP 2106.04(a)(2)(I)(C.)
Step 2A Prong 2: The claim recites the following additional elements which, considered individually and as an ordered combination, do not integrate the abstract idea into a practical application:
The additional element(s) in the parent claim(s).
wherein the results of the analysis include one or more of: the individual scores generated using the one or more scoring agents
This limitation directs to mere data gathering of insignificant extra-solution activity. See MPEP § 2106.05(g)
Step 2B: The claim recites the following additional elements which, considered individually and as an ordered combination, do not amount to significantly more than the abstract idea:
The additional element(s) in the parent claim(s).
wherein the results of the analysis include one or more of: the individual scores generated using the one or more scoring agents
This limitation directs to mere data gathering of insignificant extra-solution activity. See MPEP § 2106.05(g)
This has been determined to be insignificant extra-solution activity as found in MPEP § 2106.05(d)(II)(i): Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information); TLI Communications LLC v. AV Auto. LLC, 823 F.3d 607, 610, 118 USPQ2d 1744, 1745 (Fed. Cir. 2016) (using a telephone for image transmission); OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network); buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network); but see DDR Holdings, LLC v. Hotels.com, L.P., 773 F.3d 1245, 1258, 113 USPQ2d 1097, 1106 (Fed. Cir. 2014) ("Unlike the claims in Ultramercial, the claims at issue here specify how interactions with the Internet are manipulated to yield a desired result‐‐a result that overrides the routine and conventional sequence of events ordinarily triggered by the click of a hyperlink." (emphasis added));
In regards to claim 4,
Step 1: This part of the eligibility analysis evaluates whether the claim(s) falls within any statutory category. See MPEP 2106.03:
The claim directs to a statutory category – process.
Step 2A Prong 1: The claim recites the following abstract ideas:
The abstract idea(s) in the parent claim(s).
Step 2A Prong 2: The claim recites the following additional elements which, considered individually and as an ordered combination, do not integrate the abstract idea into a practical application:
The additional element(s) in the parent claim(s).
wherein the at least one scoring agent of the one or more scoring agents is an internal agent
This limitation directs to merely applying (or equivalent) an abstract idea, or implementing an abstract idea on a computer, or using a computer as a tool to perform an abstract idea. See MPEP 2106.05(f)
Examiner’s note: Examiner interprets an internal agent in light of the specification (“[0040] In some embodiments, the scoring agents 307 may include internal agents provided by the system 300. For example, the first scoring agent 307a may be an internal agent provided by the system 300. In these and other embodiments, the first scoring agent 307a may include algorithms and/or systems configured to evaluate and score the AI-generated contents.”) wherein an internal agent may be an agent with any algorithm to evaluate and score AI-generated content.
Step 2B: The claim recites the following additional elements which, considered individually and as an ordered combination, do not amount to significantly more than the abstract idea:
The additional element(s) in the parent claim(s).
wherein the at least one scoring agent of the one or more scoring agents is an internal agent
This limitation directs to merely applying (or equivalent) an abstract idea, or implementing an abstract idea on a computer, or using a computer as a tool to perform an abstract idea. See MPEP 2106.05(f)
Examiner’s note: Examiner interprets an internal agent in light of the specification (“[0040] In some embodiments, the scoring agents 307 may include internal agents provided by the system 300. For example, the first scoring agent 307a may be an internal agent provided by the system 300. In these and other embodiments, the first scoring agent 307a may include algorithms and/or systems configured to evaluate and score the AI-generated contents.”) wherein an internal agent may be an agent with any algorithm to evaluate and score AI-generated content.
The recitation of claim limitations that attempt to cover any solution to an identified problem with no restriction on how the result is accomplished and no description of the mechanism for accomplishing the result, does not integrate a judicial exception into a practical application or provide significantly more because this type of recitation is equivalent to the words "apply it".
In regards to claim 5,
Step 1: This part of the eligibility analysis evaluates whether the claim(s) falls within any statutory category. See MPEP 2106.03:
The claim directs to a statutory category – process.
Step 2A Prong 1: The claim recites the following abstract ideas:
The abstract idea(s) in the parent claim(s).
Step 2A Prong 2: The claim recites the following additional elements which, considered individually and as an ordered combination, do not integrate the abstract idea into a practical application:
The additional element(s) in the parent claim(s).
wherein the at least one scoring agent of the one or more scoring agents is an external agent
This limitation directs to merely applying (or equivalent) an abstract idea, or implementing an abstract idea on a computer, or using a computer as a tool to perform an abstract idea. See MPEP 2106.05(f)
Examiner’s note: Examiner interprets external agent in light of the specification (“[0041] In some embodiments, the scoring agents 307 may be an external agent provided by external systems, services, and/or users.”)
Step 2B: The claim recites the following additional elements which, considered individually and as an ordered combination, do not amount to significantly more than the abstract idea:
The additional element(s) in the parent claim(s).
wherein the at least one scoring agent of the one or more scoring agents is an external agent
This limitation directs to merely applying (or equivalent) an abstract idea, or implementing an abstract idea on a computer, or using a computer as a tool to perform an abstract idea. See MPEP 2106.05(f)
Examiner’s note: Examiner interprets external agent in light of the specification (“[0041] In some embodiments, the scoring agents 307 may be an external agent provided by external systems, services, and/or users.”)
The recitation of claim limitations that attempt to cover any solution to an identified problem with no restriction on how the result is accomplished and no description of the mechanism for accomplishing the result, does not integrate a judicial exception into a practical application or provide significantly more because this type of recitation is equivalent to the words "apply it".
In regards to claim 6,
Step 1: This part of the eligibility analysis evaluates whether the claim(s) falls within any statutory category. See MPEP 2106.03:
The claim directs to a statutory category – process.
Step 2A Prong 1: The claim recites the following abstract ideas:
The abstract idea(s) in the parent claim(s).
Step 2A Prong 2: The claim recites the following additional elements which, considered individually and as an ordered combination, do not integrate the abstract idea into a practical application:
The additional element(s) in the parent claim(s).
wherein the AI model is a large language model
This limitation directs to mere data gathering of insignificant extra-solution activity (obtaining a large language model). See MPEP § 2106.05(g)
Step 2B: The claim recites the following additional elements which, considered individually and as an ordered combination, do not amount to significantly more than the abstract idea:
The additional element(s) in the parent claim(s).
wherein the AI model is a large language model
This limitation directs to mere data gathering of insignificant extra-solution activity (obtaining a large language model). See MPEP § 2106.05(g)
This has been determined to be insignificant extra-solution activity as found in MPEP § 2106.05(d)(II)(i): Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information); TLI Communications LLC v. AV Auto. LLC, 823 F.3d 607, 610, 118 USPQ2d 1744, 1745 (Fed. Cir. 2016) (using a telephone for image transmission); OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network); buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network); but see DDR Holdings, LLC v. Hotels.com, L.P., 773 F.3d 1245, 1258, 113 USPQ2d 1097, 1106 (Fed. Cir. 2014) ("Unlike the claims in Ultramercial, the claims at issue here specify how interactions with the Internet are manipulated to yield a desired result‐‐a result that overrides the routine and conventional sequence of events ordinarily triggered by the click of a hyperlink." (emphasis added));
In regards to claim 7,
Step 1: This part of the eligibility analysis evaluates whether the claim(s) falls within any statutory category. See MPEP 2106.03:
The claim directs to a statutory category – process.
Step 2A Prong 1: The claim recites the following abstract ideas:
The abstract idea(s) in the parent claim(s).
Step 2A Prong 2: The claim recites the following additional elements which, considered individually and as an ordered combination, do not integrate the abstract idea into a practical application:
The additional element(s) in the parent claim(s).
using the AI model
This limitation directs to merely applying (or equivalent) an abstract idea, or implementing an abstract idea on a computer, or using a computer as a tool to perform an abstract idea. See MPEP 2106.05(f)
obtaining a query from a user… and generating a response to the query using data stored in a knowledge base
This limitation directs to mere data gathering of insignificant extra-solution activity. See MPEP § 2106.05(g)
Step 2B: The claim recites the following additional elements which, considered individually and as an ordered combination, do not amount to significantly more than the abstract idea:
The additional element(s) in the parent claim(s).
using the AI model
This limitation directs to merely applying (or equivalent) an abstract idea, or implementing an abstract idea on a computer, or using a computer as a tool to perform an abstract idea. See MPEP 2106.05(f)
The recitation of claim limitations that attempt to cover any solution to an identified problem with no restriction on how the result is accomplished and no description of the mechanism for accomplishing the result, does not integrate a judicial exception into a practical application or provide significantly more because this type of recitation is equivalent to the words "apply it".
using data stored in a knowledge base
This has been determined to be insignificant extra-solution activity as found in MPEP § 2106.05(d)(II)(iv): Storing and retrieving information in memory, Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015); OIP Techs., 788 F.3d at 1363, 115 USPQ2d at 1092-93;
obtaining a query from a user and generating a response to the query
This has been determined to be insignificant extra-solution activity as found in MPEP § 2106.05(d)(II)(i): Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information); TLI Communications LLC v. AV Auto. LLC, 823 F.3d 607, 610, 118 USPQ2d 1744, 1745 (Fed. Cir. 2016) (using a telephone for image transmission); OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network); buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network); but see DDR Holdings, LLC v. Hotels.com, L.P., 773 F.3d 1245, 1258, 113 USPQ2d 1097, 1106 (Fed. Cir. 2014) ("Unlike the claims in Ultramercial, the claims at issue here specify how interactions with the Internet are manipulated to yield a desired result‐‐a result that overrides the routine and conventional sequence of events ordinarily triggered by the click of a hyperlink." (emphasis added));
In regards to claim 8,
Step 1: This part of the eligibility analysis evaluates whether the claim(s) falls within any statutory category. See MPEP 2106.03:
The claim directs to a statutory category – process.
Step 2A Prong 1: The claim recites the following abstract ideas:
The abstract idea(s) in the parent claim(s).
generating a record of the update
This limitation directs to a mental process that can be performed in the human mind, by a human using pen and paper, or using a computer as a tool to perform the concept and encompasses providing an opinion (record) based on an evaluation of the update; for example, providing a time of the update with the aid of pen and paper. See MPEP 2106.04(a)(2)(III)
Step 2A Prong 2: The claim recites the following additional elements which, considered individually and as an ordered combination, do not integrate the abstract idea into a practical application:
The additional element(s) in the parent claim(s).
obtaining information updating the data stored in the knowledge base; making an update to the data stored in the knowledge base based on the information
This limitation directs to mere data gathering of insignificant extra-solution activity. See MPEP § 2106.05(g)
Step 2B: The claim recites the following additional elements which, considered individually and as an ordered combination, do not amount to significantly more than the abstract idea:
The additional element(s) in the parent claim(s).
making an update to the data stored in the knowledge base based on the information
This limitation directs to mere data gathering of insignificant extra-solution activity. See MPEP § 2106.05(g)
This has been determined to be insignificant extra-solution activity as found in MPEP § 2106.05(d)(II)(iv): Storing and retrieving information in memory, Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015); OIP Techs., 788 F.3d at 1363, 115 USPQ2d at 1092-93;
obtaining information updating the data stored in the knowledge base
This limitation directs to mere data gathering of insignificant extra-solution activity. See MPEP § 2106.05(g)
This has been determined to be insignificant extra-solution activity as found in MPEP § 2106.05(d)(II)(i): Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information); TLI Communications LLC v. AV Auto. LLC, 823 F.3d 607, 610, 118 USPQ2d 1744, 1745 (Fed. Cir. 2016) (using a telephone for image transmission); OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network); buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network); but see DDR Holdings, LLC v. Hotels.com, L.P., 773 F.3d 1245, 1258, 113 USPQ2d 1097, 1106 (Fed. Cir. 2014) ("Unlike the claims in Ultramercial, the claims at issue here specify how interactions with the Internet are manipulated to yield a desired result‐‐a result that overrides the routine and conventional sequence of events ordinarily triggered by the click of a hyperlink." (emphasis added));
In regards to claim 9,
Step 1: This part of the eligibility analysis evaluates whether the claim(s) falls within any statutory category. See MPEP 2106.03:
The claim directs to a statutory category – process.
Step 2A Prong 1: The claim recites the following abstract ideas:
The abstract idea(s) in the parent claim(s).
Step 2A Prong 2: The claim recites the following additional elements which, considered individually and as an ordered combination, do not integrate the abstract idea into a practical application:
The additional element(s) in the parent claim(s).
wherein the record is stored in a NoSQL database
This limitation directs to mere data gathering of insignificant extra-solution activity. See MPEP § 2106.05(g)
Step 2B: The claim recites the following additional elements which, considered individually and as an ordered combination, do not amount to significantly more than the abstract idea:
The additional element(s) in the parent claim(s).
wherein the record is stored in a NoSQL database
This limitation directs to mere data gathering of insignificant extra-solution activity. See MPEP § 2106.05(g)
This has been determined to be insignificant extra-solution activity as found in MPEP § 2106.05(d)(II)(iv): Storing and retrieving information in memory, Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015); OIP Techs., 788 F.3d at 1363, 115 USPQ2d at 1092-93;
In regards to claim 10,
Step 1: This part of the eligibility analysis evaluates whether the claim(s) falls within any statutory category. See MPEP 2106.03:
The claim directs to a statutory category – process.
Step 2A Prong 1: The claim recites the following abstract ideas:
The abstract idea(s) in the parent claim(s).
wherein the information is obtained from analysis of conversational data
This limitation directs to a mental process that can be performed in the human mind, by a human using pen and paper, or using a computer as a tool to perform the concept and encompasses an evaluation of conversational data to provide an opinion (information). See MPEP 2106.04(a)(2)(III)
Step 2A Prong 2: The claim recites the following additional elements which, considered individually and as an ordered combination, do not integrate the abstract idea into a practical application:
The additional element(s) in the parent claim(s).
Step 2B: The claim recites the following additional elements which, considered individually and as an ordered combination, do not amount to significantly more than the abstract idea:
The additional element(s) in the parent claim(s).
In regards to claim 11,
Step 1: This part of the eligibility analysis evaluates whether the claim(s) falls within any statutory category. See MPEP 2106.03:
The claim directs to a statutory category – process.
Step 2A Prong 1: The claim recites the following abstract ideas:
The abstract idea(s) in the parent claim(s).
wherein the information is obtained from analysis of regulations and policies associated with an entity
This limitation directs to a mental process that can be performed in the human mind, by a human using pen and paper, or using a computer as a tool to perform the concept and encompasses an evaluation of regulations and policies to provide an opinion (information). See MPEP 2106.04(a)(2)(III)
Step 2A Prong 2: The claim recites the following additional elements which, considered individually and as an ordered combination, do not integrate the abstract idea into a practical application:
The additional element(s) in the parent claim(s).
Step 2B: The claim recites the following additional elements which, considered individually and as an ordered combination, do not amount to significantly more than the abstract idea:
The additional element(s) in the parent claim(s).
In regards to claim 12,
Step 1: This part of the eligibility analysis evaluates whether the claim(s) falls within any statutory category. See MPEP 2106.03:
The claim directs to a statutory category – process.
Step 2A Prong 1: The claim recites the following abstract ideas:
The abstract idea(s) in the parent claim(s).
wherein the report is customized based on a user using requesting the report
This limitation directs to a mental process that can be performed in the human mind, by a human using pen and paper, or using a computer as a tool to perform the concept and encompasses an evaluation of a user to provide an opinion (report). See MPEP 2106.04(a)(2)(III)
Step 2A Prong 2: The claim recites the following additional elements which, considered individually and as an ordered combination, do not integrate the abstract idea into a practical application:
The additional element(s) in the parent claim(s).
Step 2B: The claim recites the following additional elements which, considered individually and as an ordered combination, do not amount to significantly more than the abstract idea:
The additional element(s) in the parent claim(s).
Claim 13 (manufacture) is rejected on the same grounds under 35 U.S.C. 101 as claim 1 as they are substantially similar, respectively, Mutatis mutandis.
Claim 14 (manufacture) is rejected on the same grounds under 35 U.S.C. 101 as claim 2 as they are substantially similar, respectively, Mutatis mutandis.
Claim 15 (manufacture) is rejected on the same grounds under 35 U.S.C. 101 as claim 3 as they are substantially similar, respectively, Mutatis mutandis.
Claim 16 (manufacture) is rejected on the same grounds under 35 U.S.C. 101 as claim 4 as they are substantially similar, respectively, Mutatis mutandis.
Claim 17 (manufacture) is rejected on the same grounds under 35 U.S.C. 101 as claim 5 as they are substantially similar, respectively, Mutatis mutandis.
In regards to claim 18,
Step 1: This part of the eligibility analysis evaluates whether the claim(s) falls within any statutory category. See MPEP 2106.03:
The claim directs to a statutory category – machine.
Step 2A – Prong 1: Judicial Exception Recited?
MPEP 2106.04(a)(2)(I) “Accordingly, the "mental processes" abstract idea grouping is defined as concepts performed in the human mind, and examples of mental processes include observations, evaluations, judgments, and opinions.”
Further, the MPEP recites “The courts do not distinguish between mental processes that are performed entirely in the human mind and mental processes that require a human to use a physical aid (e.g., pen and paper or a slide rule) to perform the claim limitation.”
Yes, the claim recites a mental process, specifically:
generate a response in response to a query
This limitation encompasses providing an opinion (response) after an evaluation of a query.
identify new data for the knowledge base
This limitation encompasses providing an opinion of new data for the knowledge base.
Therefore, the claim recites a mental process.
Step 2A – Prong 2: Integrated into a Practical Solution?
MPEP 2106.05(f) Mere Instructions To Apply An Exception has found simply adding a general purpose computer or computer components after the fact to an abstract idea (e.g., a fundamental economic practice or mathematical equation) does not integrate a judicial exception into a practical application or provide significantly more. The following steps are mere instructions to apply:
A system comprising: a knowledge base…
an AI model trained using the data stored in the knowledge base (wherein training is recited at the highest level of generality)
the AI model further configured to [generate a response in response to a query]…
one or more analysis modules configured… wherein the one or more analysis modules are further configured…
a record keeping module configured
[the response generated] using the AI model
MPEP 2106.05(g) Insignificant Extra-Solution Activity has found mere data gathering/post solution activity to be insignificant extra-solution activity. The following steps are insignificant extra-solution activities:
Mere data gathering:
[a knowledge base] storing data
record changes made to the knowledge base
Post-solution activity:
update the knowledge base with the new data
The additional elements have been considered both individually and as an ordered combination in to determine whether they integrate the exception into a practical application. Therefore, no meaningful limits are imposed on practicing the abstract idea.
The claim is directed to the abstract idea.
Step 2B: Claim provides an Inventive Concept?
No, as discussed with respect to Step 2A, the additional limitation is mere data gathering/post-solution activity (Insignificant Extra-Solution Activity) and Mere Instructions To Apply An Exception and a generic device do not impose any meaningful limits on practicing the abstract idea and therefore the claim does not provide an inventive concept in Step 2B.
The recitation of claim limitations that attempt to cover any solution to an identified problem with no restriction on how the result is accomplished and no description of the mechanism for accomplishing the result, does not integrate a judicial exception into a practical application or provide significantly more because this type of recitation is equivalent to the words "apply it".
Further, the claim recites receiving data and transmitting data by generic device.
This has been determined to be insignificant extra-solution activity as found in MPEP § 2106.05(d)(II)(i): Receiving or transmitting data over a network, e.g., using the Internet to
gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary
computer to forward information); TLI Communications LLC v. AV Auto. LLC, 823 F.3d 607,
610, 118 USPQ2d 1744, 1745 (Fed. Cir. 2016) (using a telephone for image transmission); OIP
Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015)
(sending messages over a network); buy SAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112
USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network);
but see DDR Holdings, LLC v. Hotels.com, L.P., 773 F.3d 1245, 1258, 113 USPQ2d 1097, 1106
(Fed. Cir. 2014) ("Unlike the claims in Ultramercial, the claims at issue here specify how
interactions with the Internet are manipulated to yield a desired result‐‐a result that overrides
the routine and conventional sequence of events ordinarily triggered by the click of a hyperlink."
(emphasis added)).
Further, the claim recites storing data in the generic device.
This has been determined to be insignificant extra-solution activity as found in MPEP § 2106.05(d)(II)(iv): Storing and retrieving information in memory, Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015); OIP Techs., 788 F.3d at 1363, 115 USPQ2d at 1092-93;
The additional elements have been considered both individually and as an ordered
combination in the significantly more consideration.
The claim is ineligible.
In regards to claim 19,
Step 1: This part of the eligibility analysis evaluates whether the claim(s) falls within any statutory category. See MPEP 2106.03:
The claim directs to a statutory category – machine.
Step 2A Prong 1: The claim recites the following abstract ideas:
The abstract idea(s) in the parent claim(s).
Step 2A Prong 2: The claim recites the following additional elements which, considered individually and as an ordered combination, do not integrate the abstract idea into a practical application:
The additional element(s) in the parent claim(s).
wherein the one or more analysis modules include one or more of a conversational analytics module or a content analytics module
This limitation directs to merely applying (or equivalent) an abstract idea, or implementing an abstract idea on a computer, or using a computer as a tool to perform an abstract idea. See MPEP 2106.05(f)
Step 2B: The claim recites the following additional elements which, considered individually and as an ordered combination, do not amount to significantly more than the abstract idea:
The additional element(s) in the parent claim(s).
wherein the one or more analysis modules include one or more of a conversational analytics module or a content analytics module
This limitation directs to merely applying (or equivalent) an abstract idea, or implementing an abstract idea on a computer, or using a computer as a tool to perform an abstract idea. See MPEP 2106.05(f)
In regards to claim 20,
Step 1: This part of the eligibility analysis evaluates whether the claim(s) falls within any statutory category. See MPEP 2106.03:
The claim directs to a statutory category – machine.
Step 2A Prong 1: The claim recites the following abstract ideas:
The abstract idea(s) in the parent claim(s).
Step 2A Prong 2: The claim recites the following additional elements which, considered individually and as an ordered combination, do not integrate the abstract idea into a practical application:
The additional element(s) in the parent claim(s).
wherein the knowledge base includes one or more of a vector database or a regulation and policies repository
This limitation directs to merely applying (or equivalent) an abstract idea, or implementing an abstract idea on a computer, or using a computer as a tool to perform an abstract idea. See MPEP 2106.05(f)
Step 2B: The claim recites the following additional elements which, considered individually and as an ordered combination, do not amount to significantly more than the abstract idea:
The additional element(s) in the parent claim(s).
wherein the knowledge base includes one or more of a vector database or a regulation and policies repository
This limitation directs to merely applying (or equivalent) an abstract idea, or implementing an abstract idea on a computer, or using a computer as a tool to perform an abstract idea. See MPEP 2106.05(f)
Claim Rejections - 35 USC § 102
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
(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.
Claim(s) 1-5 and 12-17 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by US Pub No. US20200050968A1 Lee et al. (“Lee”).
In regards to claim 1 and analogous claim 13,
Lee teaches A method for performing a generative artificial intelligence model analytics operation, comprising:
(Lee, “[0079] Various embodiments of methods and apparatus for a customizable, easy-to-use machine learning service (MLS) designed to support large numbers of users and a wide variety of algorithms and problem sizes are described.”)
Lee teaches obtaining an artificial intelligence (AI) model; performing analysis of the AI model using one or more scoring agents; generating a report including results of the analysis; and providing the report on a user interface.
(Lee, “[0258] The client for whom the example dashboard shown in FIG. 49 is displayed [generating a report including results of the analysis; and providing the report on a user interface; see fig. 49 dashboard] has three models that were run in the covered time period of 24 hours: a brain tumor detection model BTM1 [obtaining an artificial intelligence (AI) model ex BTM1], a hippocampus atrophy detection model HADM1 and a motor cortex damage detection model MCDD 1. As indicated in region 4912 of the dashboard, the quality metric selected by the client for BTM1 is ROC AUC [performing analysis of the AI model using one or more scoring agents ie ROC AUC], the run-time performance goal is that the prediction be completed in less than X seconds, and 95% of the prediction runs in the last 24 hours have met that goal.”
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In regards to claim 2 and analogous claim 14,
Lee teaches The method of claim 1,
Lee teaches wherein the one or more scoring agents are configured to respectively generate individual scores for the AI model.
Examiner’s note: Individual score is not a term of art and the specification of the instant application does not provide an explicit definition of an individual score, thus, Examiner interprets an individual score as an evaluation metric of the AI model in its BRI. Further, Examiner interprets one scoring agent generating one individual score (see 112(b) rejection above)
(Lee, “[0258] The client for whom the example dashboard shown in FIG. 49 is displayed has three models that were run in the covered time period of 24 hours: a brain tumor detection model BTM1, a hippocampus atrophy detection model HADM1 and a motor cortex damage detection model MCDD 1. As indicated in region 4912 of the dashboard, the quality metric selected by the client for BTM1 is ROC AUC [wherein the one or more scoring agents are configured to respectively generate individual scores ie ROC AUC for the AI model], the run-time performance goal is that the prediction be completed in less than X seconds, and 95% of the prediction runs in the last 24 hours have met that goal.”)
In regards to claim 3 and analogous claim 15,
Lee teaches The method of claim 2,
Lee teaches wherein the results of the analysis include one or more of: the individual scores generated using the one or more scoring agents; aggregated scores generated by aggregating the individual scores; or comparison of the individual scores.
(Lee, “[0258] The client for whom the example dashboard shown in FIG. 49 is displayed [wherein the results of the analysis include one] has three models that were run in the covered time period of 24 hours: a brain tumor detection model BTM1, a hippocampus atrophy detection model HADM1 and a motor cortex damage detection model MCDD 1. As indicated in region 4912 of the dashboard, the quality metric selected by the client for BTM1 is ROC AUC, the run-time performance goal is that the prediction be completed in less than X seconds [the individual scores ex ROC AUC generated using the one or more scoring agents], and 95% of the prediction runs in the last 24 hours have met that goal.”)
In regards to claim 4 and analogous claim 16,
Lee teaches The method of claim 1,
Lee teaches wherein the at least one scoring agent of the one or more scoring agents is an internal agent.
(Lee, [0231], “FIG. 40 illustrates an example of a machine learning service configured to generate feature processing proposals for clients based on an analysis of costs and benefits of candidate feature processing transformations, according to at least some embodiments…. In addition, in the depicted embodiment, the FP manager 4080 [wherein the at least one scoring agent of the one or more scoring agents is an internal agent; ie the FP manager determines the quality metric] may also determine one or more prediction quality metrics 4012, and one or more run-time goals 4016 for the predictions. A variety of quality metrics 4012 may be determined in different embodiments and for different types of models, such as ROC (receiver operating characteristics) AUC (area under curve) measures for binary classification problems, mean square error metrics for regression problems, and so on.”)
In regards to claim 5 and analogous claim 17,
Lee teaches The method of claim 1,
Lee teaches wherein the at least one scoring agent of the one or more scoring agents is an external agent.
(Lee, “[0258] The client for whom the example dashboard shown in FIG. 49 is displayed has three models that were run in the covered time period of 24 hours: a brain tumor detection model BTM1, a hippocampus atrophy detection model HADM1 and a motor cortex damage detection model MCDD 1. As indicated in region 4912 of the dashboard, the quality metric selected by the client [wherein the at least one scoring agent of the one or more scoring agents is an external agent; wherein the client selects the quality metric] for BTM1 is ROC AUC, the run-time performance goal is that the prediction be completed in less than X seconds, and 95% of the prediction runs in the last 24 hours have met that goal.”)
In regards to claim 12,
Lee teaches The method of claim 1,
Lee teaches wherein the report is customized based on a user using requesting the report.
Examiner’s note: Examiner interprets the limitation as “the report is customized based on user using the report.” (See 112(b) rejection above)
(Lee, “[0258] The client for whom the example dashboard shown in FIG. 49 is displayed has three models that were run in the covered time period of 24 hours: a brain tumor detection model BTM1, a hippocampus atrophy detection model HADM1 and a motor cortex damage detection model MCDD 1. As indicated in region 4912 of the dashboard, the quality metric selected by the client [wherein the report is customized based on a user using requesting the report; wherein the client selects the quality metric] for BTM1 is ROC AUC, the run-time performance goal is that the prediction be completed in less than X seconds, and 95% of the prediction runs in the last 24 hours have met that goal.”)
Claim(s) 18-20 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Shuster, Kurt, et al. "Blenderbot 3: a deployed conversational agent that continually learns to responsibly engage." arXiv preprint arXiv:2208.03188 (2022) (“Shuster”)
In regards to claim 18,
Shuster teaches A system comprising: a knowledge base storing data;
(Shuster, Section 3.1, “BB3 is a modular system [A system comprising] but the modules are not independent components– this is achieved by training a single transformer model to execute the mod ules, with special control codes in the input context telling the model which module it is executing…
Access long-term memory Given the full input context, and a store of (text-based) memories, out put a memory from the memory store [a knowledge base ie memory store storing data; see annotated fig. 2 below], referred to as a recalled memory.”)
Shuster teaches an AI model trained using the data stored in the knowledge base,
(Shuster, Section 3.2.2, “Generate a long-term memory The MSC dataset is exclusively used for this task as it contains crowdsourced examples of summarized facts derived from the last utterance of dialogue contexts in natural conversations. We use these summarized facts as the targets for training this module [an AI model trained using the data stored in the knowledge base].”)
Shuster teaches the AI model further configured to generate a response in response to a query;
(Shuster, Section 3.1, “Generate dialogue response Given the full input context and optionally a knowledge response and recalled memory, generate a final conversational response [the AI model further configured to generate a response in response to a query]. The knowledge and memory sequences are marked with special prefix tokens.”)
Shuster teaches one or more analysis modules configured identify new data for the knowledge base, wherein the one or more analysis modules are further configured to update the knowledge base with the new data;
(Shuster, Section 3.1, “Generate a long-term memory Given the last turn of context, output a summary of that last turn that will be stored in the long-term memory [one or more analysis modules configured identify new data ie summary of last turn for the knowledge base, wherein the one or more analysis modules are further configured to update the knowledge base with the new data; ie storing the summary in the memory store (knowledge base)]. For example if the last turn was “Yes, it’s all true, my cat is black!” the output summary generated might be “I have a black cat.”. This is based off the system in Xu et al. (2022a). If the model thinks no summary should be generated for that turn it outputs “no persona”.”)
Shuster teaches and a record keeping module configured to record changes made to the knowledge base and the response generated using the AI model.
Examiner’s note: Examiner interprets the record keeping module as the same as the knowledge base. (see 112(a) rejection above)
(Shuster, Section 3.1, “The input context otherwise typically contains the dialogue history (sometimes truncated, depending on the module), with each speaker prefixed with their ID, either “Person 1:” or “Person 2:” in order to differentiate them. The modules are called in succession, conditional on the results of previous modules, the flow of which is described in §3.1.1 and Figure 2.”; see fig. 2 Memory store wherein the LT memories are stored)
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In regards to claim 19,
Shuster teaches The system of claim 18,
Shuster teaches wherein the one or more analysis modules include one or more of a conversational analytics module or a content analytics module.
(Shuster, Section 3.1, “Generate a long-term memory Given the last turn of context, output a summary of that last turn that will be stored in the long-term memory [wherein the one or more analysis modules include one or more of a conversational analytics module; wherein a the conversation is analyzed to generate an LT memory in the form of a summary]. For example if the last turn was “Yes, it’s all true, my cat is black!” the output summary generated might be “I have a black cat.”.”)
In regards to claim 20,
Shuster teaches The system of claim 18,
Shuster teaches wherein the knowledge base includes one or more of a vector database or a regulation and policies repository.
(Shuster, Section 4., “Firstly, there is a separate safety classifier, which itself is a transformer model trained similarly to the one in Xu et al. (2020). The datasets Wikipedia Toxic Comments dataset (WTC) (Wul- czyn et al., 2017), Build-It Break-It Fix-It (BBF) (Dinan et al., 2019a) and Bot Adversarial Dialogue dataset (BAD) (Xu et al., 2020) are used to train a binary classifier (safe or not safe) given the dialogue context as input. In addition, a safety key word list is used to flag potentially inappropriate responses [wherein the knowledge base includes a regulation and policies repository; wherein the KB includes a separate classifier built from safe/not safe datasets and includes a safety key word list to ensure the dialogue response is safe (thus, storing only safe responses in the Memory store)], again following Xu et al. (2020). We also have explicit checks for topics like intent to self-harm and medical issues such as covid, with canned messages for those cases. Otherwise, when the bot generates a response, before it is displayed, these safety systems are invoked as a final check. If our systems predict a potentially unsafe response, the bot instead will output a nonsequitur, similar to Xu et al. (2020). For a given user turn, these systems are also invoked to check if the user’s mes sage is safe. If either system predicts a potentially unsafe user response, the bot will also output a nonsequitur, in order to prevent the bot from being caught in a potentially difficult conversation.”
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Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (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 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claim(s) 6-8 and 10-11 are rejected under 35 U.S.C. 103 as being unpatentable over Lee in view of Shuster.
In regards to claim 6,
Lee teaches The method of claim 1,
However, Lee does not explicitly teach wherein the AI model is a large language model.
Rather, Lee discloses AI algorithms are used to solve problems in natural language processing (Lee, “[0002] Machine learning combines techniques from statistics and artificial intelligence to create algorithms that can learn from empirical data and generalize to solve problems in various domains such as natural language processing, financial fraud detection, terrorism.”) wherein Lee further disclose a variety of model types (Lee, [0234], “Such optimization based on feature processing cost-benefit tradeoffs may be used for a variety of model types, including for example classification models, regression models, clustering models, natural language processing models and the like, and for a variety of problem domains in different embodiments.”)
Shuster teaches wherein the AI model is a large language model.
(Shuster, pg. 5 Table 1, “Table 1: Set of modules inside BlenderBot 3. All modules except Internet Search are implemented by the same underlying language model [wherein the AI model is a large language model] fed different control codes (with internet search itself being executed by an independent search engine).”)
Lee is considered to be analogous to the claimed invention because they are in the same field of providing a user interface for analyzing AI models. Shuster is considered to be analogous to the claimed invention because they are in the same field of large language models. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified Lee to incorporate the teachings of Shuster in order to apply the particular AI model of Shuster in order to solve problems in the natural language processing domain with a high-performing language model. (Lee, “[0002] Machine learning combines techniques from statistics and artificial intelligence to create algorithms that can learn from empirical data and generalize to solve problems in various domains such as natural language processing, financial fraud detection, terrorism.”) (Lee, [0234], “Such optimization based on feature processing cost-benefit tradeoffs may be used for a variety of model types, including for example classification models, regression models, clustering models, natural language processing models and the like, and for a variety of problem domains in different embodiments.”) (Shuster, Section 1, “We report overall results of our model. Our newly released system outperforms existing openly available chatbots including its two predecessors by a wide margin.”)
In regards to claim 7,
Lee teaches The method of claim 1,
However, Lee does not explicitly teach further comprising: obtaining a query from a user; and generating a response to the query using the AI model and data stored in a knowledge base.
Rather, Lee discloses AI algorithms are used to solve problems in natural language processing (Lee, “[0002] Machine learning combines techniques from statistics and artificial intelligence to create algorithms that can learn from empirical data and generalize to solve problems in various domains such as natural language processing, financial fraud detection, terrorism.”) wherein Lee further disclose a variety of model types (Lee, [0234], “Such optimization based on feature processing cost-benefit tradeoffs may be used for a variety of model types, including for example classification models, regression models, clustering models, natural language processing models and the like, and for a variety of problem domains in different embodiments.”)
Shuster teaches further comprising: obtaining a query from a user; and generating a response to the query using the AI model and data stored in a knowledge base.
(Shuster, Section 3.1, “Generate dialogue response Given the full input context [obtaining a query from a user] and optionally a knowledge response and recalled memory, generate a final conversational response [generating a response to the query using the AI model and data stored in a knowledge base; wherein recalled memory is the data stored in the Memory Store (knowledge base); See fig. 2]. The knowledge and memory sequences are marked with special prefix tokens.”
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Lee is considered to be analogous to the claimed invention because they are in the same field of providing a user interface for analyzing AI models. Shuster is considered to be analogous to the claimed invention because they are in the same field of large language models. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified Lee to incorporate the teachings of Shuster in order to apply the particular AI model of Shuster in order to solve problems in the natural language processing domain with a high-performing language model. (Lee, “[0002] Machine learning combines techniques from statistics and artificial intelligence to create algorithms that can learn from empirical data and generalize to solve problems in various domains such as natural language processing, financial fraud detection, terrorism.”) (Lee, [0234], “Such optimization based on feature processing cost-benefit tradeoffs may be used for a variety of model types, including for example classification models, regression models, clustering models, natural language processing models and the like, and for a variety of problem domains in different embodiments.”) (Shuster, Section 1, “We report overall results of our model. Our newly released system outperforms existing openly available chatbots including its two predecessors by a wide margin.”)
In regards to claim 8,
Lee and Shuster teach The method of claim 7,
Shuster teaches further comprising: obtaining information updating the data stored in the knowledge base;
Examiner’s note: Examiner interprets the limitation as “obtaining information to update the data stored in the knowledge base” (See 112(b) rejection above)
(Shuster, Section 3.1, “Access long-term memory Given the full input context, and a store of (text-based) memories, output a memory from the memory store, referred to as a recalled memory [further comprising: obtaining information ie recalled memory (ie a previous LT memory) updating the data stored in the knowledge base].”)
Shuster teaches making an update to the data stored in the knowledge base based on the information; and generating a record of the update.
(Shuster, Section 3.1, “Generate a long-term memory Given the last turn of context, output a summary of that last turn that will be stored in the long-term memory [making an update to the data stored in the knowledge base based on the information; and generating a record of the update; wherein the new LT memory is stored in the Memory Store (knowledge base) after the final dialogue response is output; wherein the final dialogue response is based on the previous LT memory (recalled memory)]. For example if the last turn was “Yes, it’s all true, my cat is black!” the output summary generated might be “I have a black cat.”. This is based off the system in Xu et al. (2022a). If the model thinks no summary should be generated for that turn it outputs “no persona”.”)
In regards to claim 10,
Lee and Shuster teach The method of claim 8,
Shuster teaches wherein the information is obtained from analysis of conversational data.
(Shuster, Section 3.1, “Generate a long-term memory Given the last turn of context, output a summary of that last turn that will be stored in the long-term memory [wherein the information is obtained from analysis of conversational data; wherein the conversation is analyzed to generate an LT memory in the form of a summary]. For example if the last turn was “Yes, it’s all true, my cat is black!” the output summary generated might be “I have a black cat.”.”)
In regards to claim 11,
Lee and Shuster teach The method of claim 8,
Shuster teaches wherein the information is obtained from analysis of regulations and policies associated with an entity.
(Shuster, Section 4., “Firstly, there is a separate safety classifier, which itself is a transformer model trained similarly to the one in Xu et al. (2020). The datasets Wikipedia Toxic Comments dataset (WTC) (Wul- czyn et al., 2017), Build-It Break-It Fix-It (BBF) (Dinan et al., 2019a) and Bot Adversarial Dialogue dataset (BAD) (Xu et al., 2020) are used to train a binary classifier (safe or not safe) given the dialogue context as input. In addition, a safety key word list is used to flag potentially inappropriate responses [wherein the information is obtained from analysis of regulations and policies; wherein the KB includes a separate classifier built from safe/not safe datasets and includes a safety key word list to ensure the dialogue response is safe (thus, storing only safe responses in the Memory store)], again following Xu et al. (2020). We also have explicit checks for topics like intent to self-harm and medical issues such as covid, with canned messages for those cases. Otherwise, when the bot generates a response, before it is displayed, these safety systems are invoked as a final check. If our systems predict a potentially unsafe response, the bot instead will output a nonsequitur, similar to Xu et al. (2020). For a given user turn, these systems are also invoked to check if the user’s mes sage is safe. If either system predicts a potentially unsafe user response, the bot will also output a nonsequitur, in order to prevent the bot from being caught in a potentially difficult conversation.”
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Wherein the BlenderBot3 of Shuster is aligned with Meta AI [associated with an entity]
(Shuster, “Following our and Meta AI’s existing research program, we aim to fully and responsibly share both the models, code and collected conversations with interested researchers in order to make this research accessible and reproducible, and thus to enable further research into responsible conversational AI (Sonnenburg et al., 2007; Pineau et al., 2021; Zhang et al., 2022; Roller et al., 2020; Dinan et al., 2021). Considerations for release are detailed in §9. We summarize below the set of public releases involved in BlenderBot 3.”)
Claim(s) 9 is rejected under 35 U.S.C. 103 as being unpatentable over Lee in view of Shuster in further view of Ram, Ashwin, et al. "Conversational ai: The science behind the alexa prize." arXiv preprint arXiv:1801.03604 (2018). (“Ram”)
In regards to claim 9,
Lee and Shuster teach The method of claim 8,
However, Lee and Shuster does not explicitly teach wherein the record is stored in a NoSQL database.
Ram teaches wherein the record is stored in a NoSQL database.
(Ram, Section 4.2, “During the competition, multiple load tests were conducted to evaluate the scalability of socialbots, in which a sustained amount of artificial traffic was sent to each with the goal of identifying choke points within the system. We developed a load testing tool to specifically target individual socialbots at a particular rate per second for each test. Systems that exhibited the best resiliency to failure were often those that did the majority of their processing inside Lambda and not on EC2 instances. Favoring DynamoDB (a NoSQL database) [wherein the record is stored in a NoSQL database] over traditional SQL databases was also a common feature found in teams that were able to stay functional under increased load.”)
Ram is considered to be analogous to the claimed invention because they are in the same field of analyzing performance of large language models. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified Lee and Shuster to incorporate the teachings of Ram in order to provide a NoSQL database as the Memory store of Shuster as doing so allows for the LLM to stay functional under increased load. (Ram, Section 4.2, “Favoring DynamoDB (a NoSQL database) over traditional SQL databases was also a common feature found in teams that were able to stay functional under increased load.”)
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
US Pub No. US20210234814A1 Wu et al. teaches human-machine interaction
Fig. 2 discloses a conversation control system and long-term memory modules
US Pub No. US20070094168A1 Ayala et al. discloses Artificial neural network design and evaluation tool
U.S. Pub. No. US20200334570A1: Franklin et al. discloses Data visualization for machine learning model performance
U.S. Pub. No. US20200349466A1: Hoogerwerf et al. discloses Providing performance views associated with performance of a machine learning system
U.S. Pub. No. US20120158623A1: Bilenko et al. discloses Visualizing machine learning accuracy
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/J.T.T./Examiner, Art Unit 2129
/SEHWAN KIM/Examiner, Art Unit 2129
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