DETAILED ACTION
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
This action is responsive to the original application filed on 2/5/2024. Acknowledgment is made with respect to a claim of priority to Japanese Application JP2021-129344 filed on 8/5/2021 and to PCT Application PCT/JP2022/030203 filed on 8/5/2022.
Claim Objections
Claims 4-7 and 13 are objected to because of the following informalities:
Claim 4 recites the limitation “the detection unit detects whether a updated master model is used by the second user” (emphasis added) which should read as the detection unit detects whether an updated master model is used by the second user” (emphasis added) for better grammar. Dependent claims 5-7 depend on objected claim 4, and are also objected to by virtue of this dependency. Appropriate correction is required.
Claim 7 recites the limitation “the contribution level expands the authority of the second user to use the master model and the custom model according to magnitude of the contribution level” (emphasis added) which should read as “the contribution level expands the authority of the second user to use the master model and the custom model according to a magnitude of the contribution level” (emphasis added) for better grammar. Appropriate correction is required.
Claim 13 recites the limitation “wherein the first model trained based on the first training data and configured to recognize a target object in input information and the second model trained based on the second training data and the first model” (emphasis added) which should read as “wherein the first model is trained based on the first training data and configured to recognize a target object in input information and the second model is trained based on the second training data and the first model” (emphasis added) for better grammatical clarity. Appropriate correction is required.
Claim Interpretation
The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph:
An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The claims in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. The broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification when 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is invoked.
As explained in MPEP § 2181, subsection I, claim limitations that meet the following three-prong test will be interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph:
(A) the claim limitation uses the term “means” or “step” or a term used as a substitute for “means” that is a generic placeholder (also called a nonce term or a non-structural term having no specific structural meaning) for performing the claimed function;
(B) the term “means” or “step” or the generic placeholder is modified by functional language, typically, but not always linked by the transition word “for” (e.g., “means for”) or another linking word or phrase, such as “configured to” or “so that”; and
(C) the term “means” or “step” or the generic placeholder is not modified by sufficient structure, material, or acts for performing the claimed function.
Use of the word “means” (or “step”) in a claim with functional language creates a rebuttable presumption that the claim limitation is to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites sufficient structure, material, or acts to entirely perform the recited function.
Absence of the word “means” (or “step”) in a claim creates a rebuttable presumption that the claim limitation is not to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites function without reciting sufficient structure, material or acts to entirely perform the recited function.
Claim limitations in this application that use the word “means” (or “step”) are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action. Conversely, claim limitations in this application that do not use the word “means” (or “step”) are not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action.
This application includes one or more claim limitations that do not use the word “means,” but are nonetheless being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because the claim limitation(s) uses a generic placeholder that is coupled with functional language without reciting sufficient structure to perform the recited function and the generic placeholder is not preceded by a structural modifier.
Such claim limitation(s) is/are: “an update determination unit” in claim 1 and its dependents, “the detection unit” in claim 4 and its dependents, and “an update processing unit” in claim 11.
Because this/these claim limitation(s) is/are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, it/they is/are being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof.
If applicant does not intend to have this/these limitation(s) interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may: (1) amend the claim limitation(s) to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitation(s) recite(s) sufficient structure to perform the claimed function so as to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 4-8, 10, and 13 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 4 recites the limitation “the detection unit detects whether a updated master model is used by the second user” (emphasis added). There is insufficient antecedent basis for the claimed “the detection unit”. For examination purposes, the limitation will be interpreted to mean “[[the]] a detection unit detects whether a updated master model is used by the second user” (emphasis added). Dependent claims 5-7 depend on indefinite claim 4, and are also rejected under 35 USC § 112(b) by virtue of this dependency. Appropriate correction is required.
Claim 5 recites “The trained model management device according to claims 2 to 4” (emphasis added). It is unclear, under a broadest reasonable interpretation of the claim language, if the claim depends on all of claims 2, 3, and 4, two of claims 2, 3, ad 4, or just one of claims 2, 3, or 4. For examination purposes, the limitation will be interpreted to mean “The trained model management device according to claim[[s 2 to 4]] 4” (emphasis added). Claim 5 further recites the limitation “a result of the recognition is presented to the user” (emphasis added). It is unclear if the referenced “the user” in this limitation” refers to the previously introduced first user or the second user. Please explain. For examination purposes, the limitation will be interpreted to mean “a result of the recognition is presented to the first user or the second user” Dependent claims 6-7 depend on indefinite claim 5, and are also rejected under 35 USC § 112(b) by virtue of this dependency. Appropriate correction is required.
Claim 7 recites “The trained model management device according to claims 2 to 6” (emphasis added). It is unclear, under a broadest reasonable interpretation of the claim language, if the claim depends on all of claims 2, 3, 4, 5, and 6, or some other combination of claims 2-6. For examination purposes, the limitation will be interpreted to mean “The trained model management device according to claim[[s 2 to 6]] 6” (emphasis added). Appropriate correction is required.
Claim 8 recites the limitation “based on the second training data, that the target object to be added is highly versatile” (emphasis added). The term “highly versatile” in claim 8 is a relative term which renders the claim indefinite. The term “highly versatile” is not defined by the claim, the specification does not provide a standard for ascertaining the requisite degree, and one of ordinary skill in the art would not be reasonably apprised of the scope of the invention. How can one determine whether a target object is highly versatile, just versatile, or something besides versatile? Neither the claim language nor the originally filed specification make this determination clear. Please explain. For examination purposes, the limitation will be interpreted to mean, in view of paragraph [0035] of the originally filed specification, “based on the second training data, that the target object to be added [[is highly versatile]] can be used” (emphasis added). Appropriate correction is required.
Claim 10 recites the limitation “the target object to be made recognizable by training is identical or similar to that of the second training data” (emphasis added). The term “recognizable” in claim 10 is a relative term which renders the claim indefinite. The term “recognizable” is not defined by the claim, the specification does not provide a standard for ascertaining the requisite degree, and one of ordinary skill in the art would not be reasonably apprised of the scope of the invention. How can one determine whether a target object is to be made recognizable versus unrecognizable by training? Neither the claim language nor the originally filed specification make this determination clear. Please explain. For examination purposes, the limitation will be interpreted to mean “the target object is identical or similar to that of the second training data” (emphasis added). Appropriate correction is required.
Claim 13 recites the limitations “determining whether or not to update the first model based on the second training data in response to the second model being generated, and wherein the first model trained based on the first training data and configured to recognize a target object in input information and the second model trained based on the second training data and the first model” (emphasis added). There is insufficient antecedent basis for the claimed “the first model”, “the second training data”, “the second model”, and “the first training data”. For examination purposes, the limitations will be interpreted to mean “determining whether or not to update [[the]] a first model based on [[the]] second training data in response to [[the]] a second model being generated, and wherein the first model trained based on [[the]] first training data and configured to recognize a target object in input information and the second model trained based on the second training data and the first model” (emphasis added). Appropriate correction is required.
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 1-13 are rejected under 35 U.S.C 101 because the claimed invention is directed to an abstract idea without significantly more. The analysis of the claims will follow the 2019 Revised Patent Subject Matter Eligibility Guidance, 84 Fed. Reg. 50 (“2019 PEG”).
When considering subject matter eligibility under 35 U.S.C. 101, it must be determined whether the claim is directed to one of the four statutory categories of invention, i.e., process, machine, manufacture, or composition of matter (Step 1). If the claim does fall within one of the statutory categories, the second step in the analysis is to determine whether the claim is directed to a judicial exception (Step 2A). The Step 2A analysis is broken into two prongs. In the first prong (Step 2A, Prong 1), it is determined whether or not the claims recite a judicial exception (e.g., mathematical concepts, mental processes, certain methods of organizing human activity). If it is determined in Step 2A, Prong 1 that the claims recite a judicial exception, the analysis proceeds to the second prong (Step 2A, Prong 2), where it is determined whether or not the claims integrate the judicial exception into a practical application. If it is determined at step 2A, Prong 2 that the claims do not integrate the judicial exception into a practical application, the analysis proceeds to determining whether the claim is a patent-eligible application of the exception (Step 2B). If an abstract idea is present in the claim, any element or combination of elements in the claim must be sufficient to ensure that the claim integrates the judicial exception into a practical application, or else amounts to significantly more than the abstract idea itself.
Claim 1
Step 1: The claim recites a trained model management device; therefore, it is directed to the statutory category of a machine.
Step 2A Prong 1: The claim recites, inter alia:
recognize a target object in input information: Under its broadest reasonable interpretation in light of the specification, this limitation encompasses the mental process of recognizing an object in input information such as an image, which is an evaluation or observation that is practically capable of being performed in the human mind with the assistance of pen and paper. For example, one can practically and mentally recognize an object like a dog in an image.
makes a determination as to whether or not to update the first model based on the second training data in response to the second model being generated: Under its broadest reasonable interpretation in light of the specification, this limitation encompasses the mental process of determining whether or not to update a model based on another model being generated, which is an evaluation or observation that is practically capable of being performed in the human mind with the assistance of pen and paper. For example, one can practically and mentally determine to update a model based on the occurrence of another model being generated.
Step 2A Prong 2: The claim does not recite any additional limitations which integrate the abstract idea into a practical application. Specifically, the additional elements consist of “a first storage storing first training data and a first model trained based on the first training data, the first model configured to”, “a second storage storing second training data and a second model trained based on the second training data and the first model”, and “an update determination unit that”.
The additional elements of “a first storage”, “a second storage”, and “an update determination unit that” amount to generic computer components used as a tool to perform an existing process. Thus, the additional elements amount to no more than a recitation of the words "apply it" (or an equivalent) or are more than mere instructions to implement an abstract idea or other exception on a computer (see MPEP § 2106.05(f)).
The additional elements “storing first training data and a first model trained based on the first training data” and “storing first training data and a first model trained based on the first training data” are insignificant extra-solution activities required for any uses of the abstract ideas (see MPEP § 2106.05(g)).
Thus, even when viewed individually and as an ordered combination, these additional elements do not integrate the abstract idea into a practical application and the claim is thus directed to the abstract idea.
Step 2B: Finally, the claim taken as a whole does not contain an inventive concept which provides significantly more than the abstract idea.
The additional elements of “a first storage”, “a second storage”, and “an update determination unit that” amount to generic computer components used as a tool to perform an existing process. Thus, the additional elements amount to no more than a recitation of the words "apply it" (or an equivalent) or are more than mere instructions to implement an abstract idea or other exception on a computer (see MPEP § 2106.05(f)).
The additional elements “storing first training data and a first model trained based on the first training data” and “storing first training data and a first model trained based on the first training data” are insignificant extra-solution activities required for any uses of the abstract ideas (see MPEP § 2106.05(g)), and are well-understood, routine, conventional activities (see MPEP § 2106.05(d)(II)(i); “Storing and retrieving information in memory”).
Taken alone or in combination, the additional elements of the claim do not provide an inventive concept and thus the claim is subject-matter ineligible.
Claim 2
Step 1: A machine, as above.
Step 2A Prong 1: The claim recites the abstract ideas of the preceding claims from which it depends.
Step 2A Prong 2, Step 2B: The additional element of “the first model is a master model available to a first user and a second user different from the first user, and
the second model is a custom model available only to the second user” amounts to no more than generally linking the use of a judicial exception to a particular technological environment or field of use (see MPEP § 2106.05(h). Taken alone or in combination, the additional elements of the claim do not provide an inventive concept, integrate the abstract ideas into a practical application, or provide significantly more than the abstract ideas of the claim and thus the claim is subject-matter ineligible.
Claim 3
Step 1: A machine, as above.
Step 2A Prong 1: The claim recites the abstract ideas of the preceding claims from which it depends.
Step 2A Prong 2, Step 2B: The additional element of “wherein the second storage is configured to be available only to the second user” amounts to no more than generally linking the use of a judicial exception to a particular technological environment or field of use (see MPEP § 2106.05(h). Taken alone or in combination, the additional elements of the claim do not provide an inventive concept, integrate the abstract ideas into a practical application, or provide significantly more than the abstract ideas of the claim and thus the claim is subject-matter ineligible.
Claim 4
Step 1: A machine, as above.
Step 2A Prong 1: The claim recites, inter alia:
detects whether a updated master model is used by the second user instead of the custom model, the updated master model acquired by updating the master model: Under its broadest reasonable interpretation in light of the specification, this limitation encompasses the mental process of determining which model a user is using, which is an evaluation or observation that is practically capable of being performed in the human mind with the assistance of pen and paper.
when the determination is made that the master model is to be updated: Under its broadest reasonable interpretation in light of the specification, this limitation encompasses the mental process of determining when to update a model, which is an evaluation or observation that is practically capable of being performed in the human mind with the assistance of pen and paper.
when the second user uses the updated master model instead of the custom model, the second training data stored in the second storage is deleted: Under its broadest reasonable interpretation in light of the specification, this limitation encompasses the mental process of deleting information based on a model update, which is an evaluation or observation that is practically capable of being performed in the human mind with the assistance of pen and paper.
Step 2A Prong 2, Step 2B: The additional element of “the detection unit” amounts to a generic computer component used as a tool to perform an existing process. Thus, the additional elements amount to no more than a recitation of the words "apply it" (or an equivalent) or are more than mere instructions to implement an abstract idea or other exception on a computer (see MPEP § 2106.05(f)). The additional element “when the determination is made that the master model is to be updated, the second training data is stored in the first storage” is an insignificant extra-solution activity required for any uses of the abstract ideas (see MPEP § 2106.05(g)), and is a well-understood, routine, conventional activity (see MPEP § 2106.05(d)(II)(i); “Storing and retrieving information in memory”). Taken alone or in combination, the additional elements of the claim do not provide an inventive concept, integrate the abstract ideas into a practical application, or provide significantly more than the abstract ideas of the claim and thus the claim is subject-matter ineligible.
Claim 5
Step 1: A machine, as above.
Step 2A Prong 1: The claim recites the abstract ideas of the preceding claims from which it depends.
Step 2A Prong 2, Step 2B: The additional element of “recognition of the target object is executed using the updated master model with the evaluation data as the input information” amounts no more than a recitation of the words "apply it" (or an equivalent) or are more than mere instructions to implement an abstract idea or other exception on a computer (see MPEP § 2106.05(f)). The additional elements “the second storage stores evaluation data prepared by the second user about the target object that is made recognizable by the second training data” and “a result of the recognition is presented to the user” are insignificant extra-solution activities required for any uses of the abstract ideas (see MPEP § 2106.05(g)), and are well-understood, routine, conventional activities (see MPEP § 2106.05(d)(II)(i); “Storing and retrieving information in memory” and “Presenting offers and gathering statistics”). Taken alone or in combination, the additional elements of the claim do not provide an inventive concept, integrate the abstract ideas into a practical application, or provide significantly more than the abstract ideas of the claim and thus the claim is subject-matter ineligible.
Claim 6
Step 1: A machine, as above.
Step 2A Prong 1: The claim recites, inter alia:
wherein the evaluation data stored in the second storage is not deleted even when the second user uses the updated master model instead of the custom model: Under its broadest reasonable interpretation in light of the specification, this limitation encompasses the mental process of determining to not delete data based a model is updated, which is an evaluation or observation that is practically capable of being performed in the human mind with the assistance of pen and paper.
Step 2A Prong 2, Step 2B: The claim does not recite any additional elements that are sufficient to integrate the judicial exceptions into a practical application or amount to significantly more than the judicial exception. As such, the claim is ineligible.
Claim 7
Step 1: A machine, as above.
Step 2A Prong 1: The claim recites, inter alia:
when the determination is made that the master model is to be updated, data indicating a contribution level of the second user stored in the second storage is updated: Under its broadest reasonable interpretation in light of the specification, this limitation encompasses the mental process of updating data, which is an evaluation or observation that is practically capable of being performed in the human mind with the assistance of pen and paper.
Step 2A Prong 2, Step 2B: The additional element of “the contribution level expands the authority of the second user to use the master model and the custom model according to magnitude of the contribution level” amounts to no more than generally linking the use of a judicial exception to a particular technological environment or field of use (see MPEP § 2106.05(h). Taken alone or in combination, the additional elements of the claim do not provide an inventive concept, integrate the abstract ideas into a practical application, or provide significantly more than the abstract ideas of the claim and thus the claim is subject-matter ineligible.
Claim 8
Step 1: A machine, as above.
Step 2A Prong 1: The claim recites, inter alia:
makes the determination that the master model is to be updated upon determining, based on the second training data, that the target object to be added is highly versatile: Under its broadest reasonable interpretation in light of the specification, this limitation encompasses the mental process of determining to update a model based on a target object, which is an evaluation or observation that is practically capable of being performed in the human mind with the assistance of pen and paper.
Step 2A Prong 2, Step 2B: The claim does not recite any additional elements that are sufficient to integrate the judicial exceptions into a practical application or amount to significantly more than the judicial exception. As such, the claim is ineligible.
Claim 9
Step 1: A machine, as above.
Step 2A Prong 1: The claim recites the abstract ideas of the preceding claims from which it depends.
Step 2A Prong 2, Step 2B: The additional element of “the master model is updated by updating the portion of the trained models specified by the update determination unit” amounts no more than a recitation of the words "apply it" (or an equivalent) or are more than mere instructions to implement an abstract idea or other exception on a computer (see MPEP § 2106.05(f)). The additional elements of “t the master model includes trained models, the trained models being different from each other,
the update determination unit specifies a portion of the trained models to be updated by training using the second training data” amount to no more than generally linking the use of a judicial exception to a particular technological environment or field of use (see MPEP § 2106.05(h). Taken alone or in combination, the additional elements of the claim do not provide an inventive concept, integrate the abstract ideas into a practical application, or provide significantly more than the abstract ideas of the claim and thus the claim is subject-matter ineligible.
Claim 10
Step 1: A machine, as above.
Step 2A Prong 1: The claim recites the abstract ideas of the preceding claims from which it depends.
Step 2A Prong 2, Step 2B: The additional element of “wherein the portion of the trained models is a model trained based on the first training data in which the target object to be made recognizable by training is identical or similar to that of the second training data” amounts to no more than generally linking the use of a judicial exception to a particular technological environment or field of use (see MPEP § 2106.05(h). Taken alone or in combination, the additional elements of the claim do not provide an inventive concept, integrate the abstract ideas into a practical application, or provide significantly more than the abstract ideas of the claim and thus the claim is subject-matter ineligible.
Claim 11
Step 1: A machine, as above.
Step 2A Prong 1: The claim recites the abstract ideas of the preceding claims from which it depends.
Step 2A Prong 2, Step 2B: The additional element of “an update processing unit that executes update processing of the second model based on the first training data and the second training data when updating the second model” amounts no more than a recitation of the words "apply it" (or an equivalent) or are more than mere instructions to implement an abstract idea or other exception on a computer (see MPEP § 2106.05(f)). Taken alone or in combination, the additional elements of the claim do not provide an inventive concept, integrate the abstract ideas into a practical application, or provide significantly more than the abstract ideas of the claim and thus the claim is subject-matter ineligible.
Claim 12
Step 1: A machine, as above.
Step 2A Prong 1: The claim recites the abstract ideas of the preceding claims from which it depends.
Step 2A Prong 2, Step 2B: The additional element of “wherein the second storage further stores a custom model available only to a third user” amounts to no more than generally linking the use of a judicial exception to a particular technological environment or field of use (see MPEP § 2106.05(h). Taken alone or in combination, the additional elements of the claim do not provide an inventive concept, integrate the abstract ideas into a practical application, or provide significantly more than the abstract ideas of the claim and thus the claim is subject-matter ineligible.
Claim 13
Step 1: The claim recites a method; therefore, it is directed to the statutory category of a process.
Step 2A Prong 1: The claim recites, inter alia:
determining whether or not to update the first model based on the second training data in response to the second model being generated: Under its broadest reasonable interpretation in light of the specification, this limitation encompasses the mental process of determining to update a model based on the generation of another model, which is an evaluation or observation that is practically capable of being performed in the human mind with the assistance of pen and paper. For example, one can practically and mentally determine to update a model based on the occurrence of an event, such as the creation of another model.
recognize a target object in input information: Under its broadest reasonable interpretation in light of the specification, this limitation encompasses the mental process of recognizing a target object in input information, which is an evaluation or observation that is practically capable of being performed in the human mind with the assistance of pen and paper. For example, one can practically and mentally recognize an object such as a dog in an image.
Step 2A Prong 2: The claim does not recite any additional limitations which integrate the abstract idea into a practical application. Specifically, the additional elements consist of “wherein the first model trained based on the first training data” and “the second model trained based on the second training data and the first model”.
The additional elements “wherein the first model trained based on the first training data” and “the second model trained based on the second training data and the first model” amount to no more than generally linking the use of a judicial exception to a particular technological environment or field of use (see MPEP § 2106.05(h)).
Thus, even when viewed individually and as an ordered combination, these additional elements do not integrate the abstract idea into a practical application and the claim is thus directed to the abstract idea.
Step 2B: Finally, the claim taken as a whole does not contain an inventive concept which provides significantly more than the abstract idea.
The additional elements “wherein the first model trained based on the first training data” and “the second model trained based on the second training data and the first model” amount to no more than generally linking the use of a judicial exception to a particular technological environment or field of use (see MPEP § 2106.05(h)).
Taken alone or in combination, the additional elements of the claim do not provide an inventive concept and thus the claim is subject-matter ineligible.
Claim Rejections - 35 USC § 102
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claims 1, 2, 4, 5, and 13 are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Flanagan et al. (US 20220083911 A1, hereinafter “Flanagan”).
Regarding claim 1, Flanagan discloses [a] trained model management device comprising: ([0010]; “the server apparatus includes a processor that is configured to receive a plurality of ε-differential privacy encoded model updates for a master machine learning model; aggregate the plurality of the received ε-differential privacy encoded updates; decode the aggregation of the plurality of received ε-differential privacy encoded updates to recover an aggregated version of the plurality of received ε-differential privacy encoded updates; and update the master machine learning model from the aggregated version of the aggregated version of the plurality of received ε-differential privacy encoded updates”, wherein the server apparatus is interpreted as the trained model management device)
a first storage storing first training data and a first model trained based on the first training data, the first model configured to recognize a target object in input information; ([0040-0041]; “the Master model Y in the Federated Learning (FL) mode is distributed to all of the user devices 100 a-100 m from the backend server 200 … The Master model Y will be stored locally on the user equipment 100 a-100 m as Xi. The storage can utilize a memory 108, such as that shown in FIG. 1”, which discloses a first storage or backend server that stores training data and a first model or master model trained based on the first training data; and [0007-0008]; “the downloaded master machine learning model is one or more of a collaborative filter (CF) model or a Federated Learning collaborative filter model. … the processor is configured to generate the user recommendation related to the use of the application based on the downloaded master machine learning model and the data related to one or more of the user of the user equipment or the user interaction with the user equipment”, which discloses that the master model or first model is trained on initial data or first training data and it is used to recognize a target object or make recommendations based on input information from users)
a second storage storing second training data and a second model trained based on the second training data and the first model; and ([0006]; “the user equipment includes a processor configured to configured to download a master machine learning model for generating a user recommendation related to one or more of a use or interaction with an application of the user equipment; calculate a model update for the master machine learning model using the master machine learning model and data related to one or more of a user of the user equipment or a user interaction with the user equipment”, which discloses that the user equipment’s local memory 108 or a second storage holds user-specific interaction data or second training data and the locally-updated model Xi is computed from both the master model and the local data, which is functionally equivalent to a second model (updated model Xi) trained on second training data and the first model or master model)
an update determination unit that makes a determination as to whether or not to update the first model based on the second training data in response to the second model being generated ([0050]; “a plurality of encoded model updates E(ΔXi) are received 310 at the backend server, such as backend server 200 illustrated in FIGS. 1 and 2. The plurality of encoded model updates E(ΔXi) will be for a given master model Y. The plurality of encoded model updates E(ΔXi) will be aggregated 312 and decoded 314, generally as described with respect to FIG. 2. The given master model Y will be updated 316”, which discloses server-side logic that decides whether to update the master model Y or first model based on the received local model updates (reflecting second training data) and in response to the second model (updated model Xi) being generated).
Regarding claim 2, the rejection of claim 1 is incorporated and Flanagan discloses the first model is a master model available to a first user and a second user different from the first user, and the second model is a custom model available only to the second user ([0040]; “As shown in FIG. 2, in one embodiment, the Master model Y in the Federated Learning (FL) mode is distributed to all of the user devices 100 a-100 m from the backend server 200”, which discloses that master model Y or the first model is distributed to all users including a first and second user. Each user device independent computes its own local model update using its own private data, making the local model Xi a custom model available only to that individual user).
Regarding claim 4, the rejection of claims 1 and 2 are incorporated and Flanagan discloses the detection unit detects whether a updated master model is used by the second user instead of the custom model, the updated master model acquired by updating the master model, when the determination is made that the master model is to be updated, the second training data is stored in the first storage, and when the second user uses the updated master model instead of the custom model, the second training data stored in the second storage is deleted ([0015]; “By aggregating the encoded model updates from many users and decoding the resulting aggregate, an estimate of the actual model updates can be calculated. This aggregate of the model updates is all that is required in the Federated Learning system as opposed to knowing the updates from the individual users, which further enhances privacy”).
Regarding claim 5, the rejection of claims 1 and 2 and 4 are incorporated and Flanagan discloses the second storage stores evaluation data prepared by the second user about the target object that is made recognizable by the second training data, and recognition of the target object is executed using the updated master model with the evaluation data as the input information, and a result of the recognition is presented to the user ([0017]; “the application is a video service running on the user equipment. The aspects of the disclosed embodiments provide a high level of user privacy when the user uses the personalised recommendations that propose for example video choices to the user based on for example, videos they have previously watched through the service, user demographics, user gender and user preferences selected through the application and service”).
Regarding claim 13, Flanagan discloses [a] trained model management method comprising: ([0013])
determining whether or not to update the first model based on the second training data in response to the second model being generated ([0050]; “a plurality of encoded model updates E(ΔXi) are received 310 at the backend server, such as backend server 200 illustrated in FIGS. 1 and 2. The plurality of encoded model updates E(ΔXi) will be for a given master model Y. The plurality of encoded model updates E(ΔXi) will be aggregated 312 and decoded 314, generally as described with respect to FIG. 2. The given master model Y will be updated 316”, which discloses server-side logic that decides whether to update the master model Y or first model based on the received local model updates (reflecting second training data) and in response to the second model (updated model Xi) being generated)
wherein the first model trained based on the first training data and configured to recognize a target object in input information ([0040-0041]; “the Master model Y in the Federated Learning (FL) mode is distributed to all of the user devices 100 a-100 m from the backend server 200 … The Master model Y will be stored locally on the user equipment 100 a-100 m as Xi. The storage can utilize a memory 108, such as that shown in FIG. 1”, which discloses a first storage or backend server that stores training data and a first model or master model trained based on the first training data; and [0007-0008]; “the downloaded master machine learning model is one or more of a collaborative filter (CF) model or a Federated Learning collaborative filter model. … the processor is configured to generate the user recommendation related to the use of the application based on the downloaded master machine learning model and the data related to one or more of the user of the user equipment or the user interaction with the user equipment”, which discloses that the master model or first model is trained on initial data or first training data and it is used to recognize a target object or make recommendations based on input information from users)
and the second model trained based on the second training data and the first model. ([0006]; “the user equipment includes a processor configured to configured to download a master machine learning model for generating a user recommendation related to one or more of a use or interaction with an application of the user equipment; calculate a model update for the master machine learning model using the master machine learning model and data related to one or more of a user of the user equipment or a user interaction with the user equipment”, which discloses that the user equipment’s local memory 108 or a second storage holds user-specific interaction data or second training data and the locally-updated model Xi is computed from both the master model and the local data, which is functionally equivalent to a second model (updated model Xi) trained on second training data and the first model or master model).
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.
Claims 3, 6, and 12 are rejected under 35 USC § 103 as being obvious over Flanagan in view of Besling et al. (US 6363348 B1, hereinafter “Besling”).
Regarding claim 3, the rejection of claims 1 and 2 are incorporated and Flanagan fails to explicitly disclose but Besling discloses wherein the second storage is configured to be available only to the second user (Column 4, Lines 25-35; “the recognition enrolment step comprises selecting a recognition model from the plurality of different recognition models of a same type in dependence on the model improvement data associated with the user; and storing an indication of the selected recognition model in association with the user identifier; and in that the step of recognising the input pattern comprises retrieving a recognition model associated with the user identifier transferred to the server station and incorporating the retrieved recognition model in the model collection”, which discloses a user-oriented model that is stored and retrieved exclusively by a user identifier and the storage partition holding this model and its data is accessible only to the user whose identifier is matched or availably only to the second user).
Flanagan and Besling are analogous art because both are concerned with distributed computing and machine learning. Before the effective filing date of the claimed invention, it would have been obvious to one skilled in distributed computing to combine the second storage of Besling and the management device of Flanaghan to yield to the predictable result of wherein the second storage is configured to be available only to the second user. The motivation for doing so would be to enable pattern recognition in a client-server configuration, without an undue training burden on a user (Besling; Column 4, Lines 14-16).
Regarding claim 6, the rejection of claims 1, 2, 4, and 5 are incorporated and Flanagan fails to explicitly disclose but Besling discloses wherein the evaluation data stored in the second storage is not deleted even when the second user uses the updated master model instead of the custom model (Claim 1; “for a recognition session between the user station and the server station, transferring a user identifier associated with a user of the user station and an input pattern representative of time sequential input generated by the user from the user station to the server station; and using the recognition unit to recognize the input pattern by incorporating at least one recognition model in the model collection which reflects the model improvement data associated with the user”).
The motivation to combine Flanagan and Besling is the same as discussed above with respect to claim 3.
Regarding claim 12, the rejection of claims 1 and 2 are incorporated and Flanagan fails to explicitly disclose but Besling discloses wherein the second storage further stores a custom model available only to a third user (Claim 1; “for a recognition session between the user station and the server station, transferring a user identifier associated with a user of the user station and an input pattern representative of time sequential input generated by the user from the user station to the server station; and using the recognition unit to recognize the input pattern by incorporating at least one recognition model in the model collection which reflects the model improvement data associated with the user”; and Claim 11).
The motivation to combine Flanagan and Besling is the same as discussed above with respect to claim 3.
Claim 7 is rejected under 35 USC § 103 as being obvious over Flanagan in view of Besling and further in view of Nishio et al. (Nishio et al., “Estimation of Individual Device Contributions for Incentivizing Federated Learning”, Sep. 20, 2020, arXiv:2009.09371v1, pp. 1-6, hereinafter “Nishio”).
Regarding claim 7, the rejection of claims 1, 2, and 4-6 are incorporated and Flanagan fails to explicitly disclose but Nishio discloses when the determination is made that the master model is to be updated, data indicating a contribution level of the second user stored in the second storage is updated, and the contribution level expands the authority of the second user to use the master model and the custom model according to magnitude of the contribution level (Abstract; “Federated learning (FL) is an emerging technique used to train a machine-learning model collaboratively using the data and computation resource of the mobile devices with out exposing privacy-sensitive user data. Appropriate incentive mechanisms that motivate the data and mobile-device owner to participate in FL is key to building a sustainable platform for FL. However, it is difficult to evaluate the contribution level of the devices/owners to determine appropriate rewards without large computation and communication overhead. This paper proposes a computation-and communication-efficient method of estimating a participating device’s contribution level”; and §1; “This paper proposes a method that estimates the individual contribution level of FL participants with no overhead traffic and little computation overhead”).
Flanagan, Besling, and Nishio are analogous art because both are concerned with distributed computing and machine learning. Before the effective filing date of the claimed invention, it would have been obvious to one skilled in distributed computing to combine the contribution levels of Nishio and the management device of Flanaghan and Besling to yield to the predictable result of when the determination is made that the master model is to be updated, data indicating a contribution level of the second user stored in the second storage is updated, and the contribution level expands the authority of the second user to use the master model and the custom model according to magnitude of the contribution level. The motivation for doing so would be to estimate a participating device’s contribution level (Nishio; Abstract).
Claims 8 and 11 are rejected under 35 USC § 103 as being obvious over Flanagan in view of Lee (US 20170148430 A1, hereinafter “Lee”).
Regarding claim 8, the rejection of claims 1 and 2 are incorporated and Flanagan fails to explicitly disclose but Lee discloses wherein the update determination unit makes the determination that the master model is to be updated upon determining, based on the second training data, that the target object to be added is highly versatile ([0106]; “In another example, the model update determiner 520 may determine whether to update a recognition model based on the accuracy of recognition by the currently embedded recognition model. In this example, the model update determiner 520 determines the accuracy of recognition based on recognition results obtained using the recognition model for a predetermined time period prior to the present, and when the determined accuracy of recognition does not satisfy a predetermined standard (e.g. 80% average), the model update determiner 520 determines that an update of the recognition model is required. In this example, the predetermined standard may be preset based on an application field and/or other features in which the recognition model is mostly likely to be used”).
Flanagan and Lee are analogous art because both are concerned with machine learning. Before the effective filing date of the claimed invention, it would have been obvious to one skilled in machine learning to combine the versatility determination of Lee and the management device of Flanaghan to yield to the predictable result of wherein the update determination unit makes the determination that the master model is to be updated upon determining, based on the second training data, that the target object to be added is highly versatile. The motivation for doing so would be to provide for a method of recognition using a recognition model (Lee; [0003]).
Regarding claim 11, the rejection of claim 1 is incorporated and Flanagan fails to explicitly disclose but Lee discloses an update processing unit that executes update processing of the second model based on the first training data and the second training data when updating the second model (Claim 20; “a recognition data inputter configured to receive the data; a model update determiner configured to determine whether to update the recognition model constructed in advance; a model updater configured to train and update the recognition model; and a data recognizer configured to recognize the received data”; and Claim 21).
The motivation to combine Flanagan and Lee is the same as discussed above with respect to claim 8.
Claims 9-10 are rejected under 35 USC § 103 as being obvious over Flanagan in view of Kim (US 20180121732 A1, hereinafter “Kim”).
Regarding claim 9, the rejection of claims 1 and 2 are incorporated and Flanagan fails to explicitly disclose but Kim discloses the master model includes trained models, the trained models being different from each other, the update determination unit specifies a portion of the trained models to be updated by training using the second training data, and the master model is updated by updating the portion of the trained models specified by the update determination unit ([0079-0080]; “According to various exemplary embodiments, the model learning unit 140 may learn the data recognition model using learning algorithm including, for example, error back-propagation or gradient descent. … When the data recognition model is learnt, the model storage 150 as illustrated in FIG. 1A may store the learnt data recognition model”; and [0087]; “In the meantime, if there are a plurality of learnt data recognition models, the model evaluation unit 160 may evaluate whether each learnt data recognition model satisfies predetermined criteria, and determine a model satisfying the predetermined criteria as a final data recognition model”; and Claim 14).
Flanagan and Kim are analogous art because both are concerned with machine learning. Before the effective filing date of the claimed invention, it would have been obvious to one skilled in machine learning to combine the versatility determination of Lee and the management device of Flanaghan to yield to the predictable result of the master model includes trained models, the trained models being different from each other, the update determination unit specifies a portion of the trained models to be updated by training using the second training data, and the master model is updated by updating the portion of the trained models specified by the update determination unit. The motivation for doing so would be to utilize machine learning algorithm such as deep learning to simulate functions such as recognition and determination of human brain and the like (Kim; [0003]).
Regarding claim 10, the rejection of claims 1 and 2 and 9 are incorporated and Flanagan fails to explicitly disclose but Kim discloses wherein the portion of the trained models is a model trained based on the first training data in which the target object to be made recognizable by training is identical or similar to that of the second training data ([0078]; “when there are a plurality of pre-constructed data recognition models, the model learning unit 140 may determine a data recognition model in which relevance between the input composition image and the basic learning data is high as a data recognition model to be learnt. In this case, the basic learning data can be pre-classified by types of data, and the data recognition model can be pre-constructed by types of data. For example, the basic learning data can be pre-classified based on various criteria such as an area where learning data is generated, time when learning data is generated, size of learning data, genre of learning data, generator of learning data, and types of object within learning data”).
The motivation to combine Flanagan and Kim is the same as discussed above with respect to claim 9.
Conclusion
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/BRENT JOHNSTON HOOVER/ Primary Examiner, Art Unit 2127
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