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 .
DETAILED ACTION
Introduction
This office action is in response to Applicant’s submission filed on 2/13/2025. As such, claims 1-9 have been examined.
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 1-9 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Claim 1 recites a system that, under the broadest reasonable interpretation, claims limitations that cover performance of the limitations in the human mind with the assistance of physical aids (e.g., pen and paper), but for the recitation of generic or well-known or conventional computer components. That is, other than reciting “a reception unit”, “a determination unit”, “a providing unit, and generative AI” nothing in these claim limitations precludes the steps from practically being performed in the mind. As a whole, claim 1 pertains to filtering out prohibitive or unlawful or unethical question when providing answer to users, which is a mental process that a human can do. Individually, each of the limitations also pertains to a mental process and/or insignificant extra solution activity, for example:
a reception unit that receives information indicating a request of a user; (e.g., a data gathering step, a human receives a question from a user)
a determination unit that determines whether to generate information indicating a response to the request indicated by the information received by the reception unit; (e.g., determination, the human using their judgement to determine if they should gather information to respond to the user.)
and a providing unit that provides the user with response information generated by using a generative AI as the information indicating the response to the request in a case where the determination unit determines to generate the information indicating the response, (e.g., after determining the question is appropriate to answer, provide the answer to the user or the human can feed the information to a generic computer to obtain an output for the user [generative AI is a generic computer component use to provide a response])
wherein the determination unit determines whether to generate the information indicating the response to the request indicated by the information received by the reception unit on a basis of whether the request indicated by the information received by the reception unit is a request related to a first target set as a non-response target and a request related to a second target set as a response target. (e.g., the human uses their judgement or follow a guideline on how to handle questions, some which the human is allow to answer, and some which may be prohibited, and therefore should be ignored or let the user know that the answer may not be provided due to legal or ethical issues.)
The judicial exception is not integrated into a practical application. In particular, the claims only recites generic computing components. Such generic computing components are recited at a high-level of generality (i.e., as a generic processor performing a generic computer function of receiving, determining, or outputting information) such that they amount to no more than mere instructions to apply the exception using generic computer components. Accordingly, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea.
Claim 1 does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional limitations of using generic computer components amount to no more than mere instructions to apply the exception using generic computer components. Mere instructions to apply an exception using generic computer components cannot provide an inventive concept. Claim 1 is not patent eligible.
The examiner further notes that the use of claimed generic computer components (“a reception unit”, “a determination unit”, “a providing unit, and generative AI”) to obtain, extract, and/or generate data invokes such generic computer components “merely as a tool to perform an existing process”. MPEP 2106.05(f). MPEP 2106.05(f) further explains:
Use of a computer or other machinery in its ordinary capacity for economic or other tasks (e.g., to receive, store, or transmit data) or 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. See Affinity Labs v. DirecTV, 838 F.3d 1253, 1262, 120 USPQ2d 1201, 1207 (Fed. Cir. 2016) (cellular telephone); TLI Communications LLC v. AV Auto, LLC, 823 F.3d 607, 613, 118 USPQ2d 1744, 1748 (Fed. Cir. 2016) (computer server and telephone unit). Similarly, "claiming the improved speed or efficiency inherent with applying the abstract idea on a computer" does not integrate a judicial exception into a practical application or provide an inventive concept. Intellectual Ventures I LLC v. Capital One Bank (USA), 792 F.3d 1363, 1367, 115 USPQ2d 1636, 1639 (Fed. Cir. 2015).
Claim 1 recites generic computer components (“a reception unit”, “a determination unit”, “a providing unit, and generative AI”), with respect to performing tasks. MPEP 2106.05(d) and (f) further provides examples of court decisions where the courts found generic computing components to be mere instructions to apply a judicial exception, and further explains “increased speed” (e.g., using a computer to increase the speed of an otherwise mental process) does not provide an inventive concept. For example:
A commonplace business method or mathematical algorithm being applied on a general purpose computer, Alice Corp. Pty. Ltd. V. CLS Bank Int’l, 573 U.S. 208, 223, 110 USPQ2d 1976, 1983 (2014); Gottschalk v. Benson, 409 U.S. 63, 64, 175 USPQ 673, 674 (1972); Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015).
A process for monitoring audit log data that is executed on a general-purpose computer where the increased speed in the process comes solely from the capabilities of the general-purpose computer, FairWarning IP, LLC v. Iatric Sys., 839 F.3d 1089, 1095, 120 USPQ2d 1293, 1296 (Fed. Cir. 2016) (emphasis added).
Performing repetitive calculations. Bancorp Services v. Sun Life, 687 F.3d 1266, 1278, 103 USPQ2d 1425, 1433 (Fed. Cir. 2012) ("The computer required by some of Bancorp’s claims is employed only for its most basic function, the performance of repetitive calculations, and as such does not impose meaningful limits on the scope of those claims.")
Claim 8 recites a method claim that corresponds to the method of claim 1 and is therefore rejected under the same grounds as claim 1 above. Claim 8 is not patent eligible.
Claim 9 recites a CRM claim that corresponds to the method of claim 1 and is therefore rejected under the same grounds as claim 1 above. While claim 9 further recites “non-transitory computer readable storage medium causing a computer to execute a process”, these are merely generic computer components recited at a high-level of generality such that they amount to no more than mere instructions to apply the exception using a generic computer component. Therefore, none of these limitations (a) integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea or (b) amount to significantly more than the judicial exception, because in either case the additional limitations merely utilize generic computer components that amounts to no more than mere instructions to apply the exception using generic computer function. Claim 9 is not patent eligible.
Claims 2-7 depend from independent claims 1, do not remedy any of the deficiencies of claim 1, and therefore are rejected on the same grounds as claim 1 from above.
Claim 2 further comprising: wherein the first target includes a target set as a non-response target for a category designated by the user. (e.g., a user can designate a category as not to be respond to.)
Claim 3 further recite: wherein the second target includes a category designated by the user. (e.g., a user can designate a category that should be responded to.)
Claim 4 further comprising: wherein the determination unit determines by using a first language model whether the request indicated by the information received by the reception unit is a request related to the first target. (e.g., use of a language model to determine information related to first target.) [language model is generic computer component used to provide an output]
Claim 5 further recites: wherein the determination unit determines by using a second language model whether the request indicated by the information received by the reception unit is a request related to the second target. (use of a language model to determine information related to second target.) [language model is generic computer component used to provide an output]
Claim 6 further recites: further comprising a generation unit that generates information indicating the first target by using the third language model. (e.g., using a language model to provide an output.) [language model is generic computer component used to provide an output]
Claim 7 further recites: wherein the generation unit inputs, to a language model, information including non-response category information indicating a non-response category that is a category different from a designated category as a category designated by the user and is associated with the designated category in advance as a non-response category, and causes the language model to generate information indicating a risk as the information indicating the first target. (e.g., use of a language model to come up with a new or different category when the information requested doesn’t belong or fit a designated category of banned category, and provide risk with newly generated category, like why it should be banned. Imagine the human receiving a request, and while the request doesn’t exactly belong to a designated category of banned content, the human can come up with a new category and provide reasoning or risk associated with this category and/or why this category also belongs to the banned categories.) [language model is a generic computer component used to provide an output]
In sum, claims 2-7 depend from claim 1, and further recite mental processes as explained above. None of the additional limitations recited in claims 2-7 amount to anything more than the same or a similar abstract idea as recited in claims 1. Nor do any limitations in claims 2-7: (a) integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea or (b) amount to significantly more than the judicial exception because the additional limitations of using generic computer components amounts to no more than mere instructions to apply the exception using generic computer components. Claims 2-7 are not patent eligible.
Claim Interpretation
The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph:
An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The claims in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. The broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification when 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is invoked.
As explained in MPEP § 2181, subsection I, claim limitations that meet the following three-prong test will be interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph:
(A) the claim limitation uses the term “means” or “step” or a term used as a substitute for “means” that is a generic placeholder (also called a nonce term or a non-structural term having no specific structural meaning) for performing the claimed function;
(B) the term “means” or “step” or the generic placeholder is modified by functional language, typically, but not always linked by the transition word “for” (e.g., “means for”) or another linking word or phrase, such as “configured to” or “so that”; and
(C) the term “means” or “step” or the generic placeholder is not modified by sufficient structure, material, or acts for performing the claimed function.
Use of the word “means” (or “step”) in a claim with functional language creates a rebuttable presumption that the claim limitation is to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites sufficient structure, material, or acts to entirely perform the recited function.
Absence of the word “means” (or “step”) in a claim creates a rebuttable presumption that the claim limitation is not to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites function without reciting sufficient structure, material or acts to entirely perform the recited function.
Claim limitations in this application that use the word “means” (or “step”) are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action. Conversely, claim limitations in this application that do not use the word “means” (or “step”) are not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action.
This application includes one or more claim limitations that do not use the word “means,” but are nonetheless being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because the claim limitation(s) uses a generic placeholder that is coupled with functional language without reciting sufficient structure to perform the recited function and the generic placeholder is not preceded by a structural modifier. Such claim limitation(s) is/are: “a reception unit that receives”, “a determination unit….that determines…”, “a providing unit…that provides…”, in claims 1-7, and “a generating unit…that generates…” in claims 6-7.
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 § 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 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, 8 and 9 are rejected under 35 U.S.C. 102 (a)(2) as being anticipated by Namer (US 20250209308).
Regarding Claim 1, Carlock discloses: 1. An information processing apparatus comprising: a reception unit that receives information indicating a request of a user; ([0025] In accordance with example embodiments of the disclosed technology, the machine-learning system can include a controller that is configured to generate outputs indicative of the validity of queries and generative model responses for a target content domain. The system can measure input and output data in relation to a specified domain and then moderate responses or take other action(s) based on whether the query and/or response are valid for the target domain.)
a determination unit that determines whether to generate information indicating a response to the request indicated by the information received by the reception unit; ([0025] In accordance with example embodiments of the disclosed technology, the machine-learning system can include a controller that is configured to generate outputs indicative of the validity of queries and generative model responses for a target content domain. The system can measure input and output data in relation to a specified domain and then moderate responses or take other action(s) based on whether the query and/or response are valid for the target domain.)
and a providing unit that provides the user with response information generated by using a generative AI as the information indicating the response to the request in a case where the determination unit determines to generate the information indicating the response, ([0068] Controller 220 can analyze the prompt 210 and generative content 242 generated by generative model 215 to determine whether the prompt and generative content are valid for one or more target domains associated with a particular downstream entity, such as a business, school, university, governmental organization, or the like. For example, in response to prompt 210 and the resulting generative content 242, controller 220 can determine whether to generate and provide a response 240 including the generative content 242 or, alternatively, a response 244 including a filtered output 246. In some examples, controller 220 can block the prompt 210 from being executed by generative model(s) 215 or block any response from being provided to client device 250 or application 252 in response to prompt 210.)
wherein the determination unit determines whether to generate the information indicating the response to the request indicated by the information received by the reception unit on a basis of whether the request indicated by the information received by the reception unit is a request related to a first target set as a non-response target and a request related to a second target set as a response target. ([0068] Controller 220 can analyze the prompt 210 and generative content 242 generated by generative model 215 to determine whether the prompt and generative content are valid for one or more target domains associated with a particular downstream entity, such as a business, school, university, governmental organization, or the like. For example, in response to prompt 210 and the resulting generative content 242, controller 220 can determine whether to generate and provide a response 240 including the generative content 242 or, alternatively, a response 244 including a filtered output 246. In some examples, controller 220 can block the prompt 210 from being executed by generative model(s) 215 or block any response from being provided to client device 250 or application 252 in response to prompt 210.)
Regarding Claim 8, it is a method claim that recite similar elements from claim 1, therefor the rationale applied in the rejection of claim 1 is also applicable.
Regarding Claim 9, Namer discloses: 9. A non-transitory computer readable storage medium that stores an information processing program, the storage medium causing a computer to execute a process comprising: ([0005] Another example aspect of the present disclosure is directed to a computing system including one or more processors, and one or more non-transitory computer-readable storage media that collectively store a machine-learned generative model configured to produce generative content in response to a user query,)
As for the rest of the claim, they claim the elements corresponding to claim 1, therefore the rationale applied in rejection of claim 1 is equally applicable.
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 2-3 are rejected under 35 U.S.C. 103 as being unpatentable over Namer, in view of Kendrick (US 20190132629).
Regarding Claim 2, Namer discloses all the element of claim 1,
Namer does not appear to teach or suggest user setting or selection of filtering or ignoring certain categories.
Kendrick in the related field of content restriction discloses: wherein the first target includes a target set as a non-response target for a category designated by the user. ([0038] For instance, the decision logic 140 may avoid certain potential descriptions based on user settings of the entertainment device, such as parental controls that restrict certain content from being viewed (e.g., explicit content and/or types of content may be blocked).)
Namer and Kendrick are considered analogous art. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the teachings of Namer to combine the teaching of Kendrick, because user may control what types of content may be viewed on their device (Kendrick, [0038]).
Regarding Claim 3, Namer discloses all the element of claim 1,
Namer does not appear to teach or suggest user setting or selection of content.
Kendrick in the related field of content restriction discloses: wherein the second target includes a category designated by the user. ([0038] For instance, the decision logic 140 may avoid certain potential descriptions based on user settings of the entertainment device, such as parental controls that restrict certain content from being viewed (e.g., explicit content and/or types of content may be blocked).)
Where the rationale for the combination would be similar to the one already provided.
Claims 4-5 are rejected under 35 U.S.C. 103 as being unpatentable over Namer, in view of Lafon (US 20250068741).
Regarding Claim 4, Namer discloses all the element of claim 1,
Namer does not appear to teach or suggest using a language model to make the determination.
Lafon in the related field discloses: wherein the determination unit determines by using a first language model whether the request indicated by the information received by the reception unit is a request related to the first target. ([0040] the input filter 102 can flag potential rogue strings or threats as a specialized deep learning model in the form of the pre-trained language model 402 is utilized to analyze multiple input characteristics and detect hidden features.) [malicious content/query read on first target]
Namer and Lafon are considered analogous art. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the teachings of Namer to combine the teaching of Lafon, because language model can be use as an input filter to determine if the query is forbidden or dangerous (Lafon, [0040]).
Regarding Claim 5, Namer discloses all the element of claim 1,
Namer does not appear to teach or suggest using a language model to make the determination.
Lafon in the related field discloses: wherein the determination unit determines by using a second language model whether the request indicated by the information received by the reception unit is a request related to the second target. ([0040] the input filter 102 can flag potential rogue strings or threats as a specialized deep learning model in the form of the pre-trained language model 402 is utilized to analyze multiple input characteristics and detect hidden features.) [non malicious content/query read on second target]
Where the rationale for the combination would be similar to the one already provided.
Claim 6 is rejected under 35 U.S.C. 103 as being unpatentable over Namer, in view of Lafon (US 20250068741), and further in view of Parmar (US 20240289628).
Regarding Claim 6, Namer and Lafon discloses all the element of claim 4,
Namer and Lafon do not appear to teach or suggest using a language model to answer a prohibited input or query.
Parmar in the related field discloses: further comprising a generation unit that generates information indicating the first target by using the third language model. ([0027] The reasoning output provides the reasoning for the classification decision made. Taking cue from the previous example—the moderation output (harmful to general public) and input (how to make a bomb) is fed to LLM′. The response of the LLM′ would be something of the sort saying “Prohibited input”. Hence the classification of input is in prohibited category and the reasoning for such classification is that it is harmful to general public or involves violence.)
Namer/Lafon/Parmar are considered analogous art. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the teachings of Namer and Lafon to combine the teaching of Parmar, because language model can output a response to the user that their inquiry is prohibited or unlawful therefore response will not be provided (Parmar, [0027]).
Claim 7 is rejected under 35 U.S.C. 103 as being unpatentable over Namer, in view of Lafon (US 20250068741), further in view of Parmar (US 20240289628), and furthermore in view of Jin (US 20230367774).
Regarding Claim 7, Namer/Lafon/Parmar discloses all the element of claim 6,
Parmar further discloses: and causes the language model to generate information indicating a risk as the information indicating the first target. [0027] The reasoning output provides the reasoning for the classification decision made. Taking cue from the previous example—the moderation output (harmful to general public) and input (how to make a bomb) is fed to LLM′. The response of the LLM′ would be something of the sort saying “Prohibited input”. Hence the classification of input is in prohibited category and the reasoning for such classification is that it is harmful to general public or involves violence.)
Namer/Lafon/Parmar do not appear to teach or suggest using a language model to automating data classification and/or filtering out other related categories.
Jin in the related field discloses: wherein the generation unit inputs, to a language model, information including non-response category information indicating a non-response category that is a category different from a designated category as a category designated by the user and is associated with the designated category in advance as a non-response category, ([0024] Additionally, or alternatively, the remote server may filter results from the rule(s) and/or the machine learning model. For example, the remote server may remove recurrences associated with names that are on a list of non-recurring merchants (e.g., names associated with grocery stores, restaurants, or gas stations, among other examples). Similarly as described above, the list of non-recurring merchants may be determined, at least in part, using foundational LLMs. Additionally, or alternatively, the remote server may remove recurrences associated with categories that are on a list of non-recurring categories (e.g., recurrences categorized as food or general goods, among other examples). Similarly as described above, the list of non-recurring categories may be determined, at least in part, using foundational LLMs.)
Namer/Lafon/Parmar/Jin are considered analogous art. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the teachings of Namer/Lafon/Parmer to combine the teaching of Jin, because language model can filter categories and analyzing new categories (Jin, [0024]).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure: Ahmed (US 20250055867) – discloses method/system for mitigating threats of generative AI model by utilizing macro classifiers for broad initial threat categorization and specialized nano classifiers for detailed analysis of specific threat subtypes. See Abstract, para 0041-0042, 0049, 0103-0106 and figs. 3 and 5 for additional details.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Philip H Lam whose telephone number is (571)272-1721. The examiner can normally be reached 9 AM-3 PM Pacific time.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Bhavesh Mehta can be reached on 571-272-7453. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/PHILIP H LAM/ Examiner, Art Unit 2656
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