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 .
Claims 1-20 are pending for examination. Claims 1, 9, and 17 are independent.
Response to Amendment
The office action is responsive to the amendments filed on 06/03/2026. As
directed by the amendments to claims 1, 9, and 17 are amended.
Response to Arguments
Applicant's arguments filed 06/03/2026 have been fully considered but they are not fully persuasive.
Applicant arguments regarding 35 U.S.C. § 101:
A. Ex parte Desjardins Establishes the Controlling Framework […]
B. The Claims Recite Operations the Specification Credits with Specific Technical Improvements
Examiner response: Examiner respectfully disagrees, claim 1 broadly recites a plurality of models, with no further details of what the models are just the range they correspond to. Secondly, generating weights for the respective models also does not detail the models themselves and is practically performable in the human mind (i.e., evaluation). Thirdly, the gating loss describes a calculation that is based on outputs of a multilayer perceptron (MLP) gate, and does not detail the MLP gate recited at a high level. Fourthly, describing outputting information by the MLP gate is understood to be transmitting information which is understood to be insignificant extra-solution activity and data gathering - See MPEP 2106.05(g). Fifthly, the limitation describing per-sample weights is describing the data that is being outputted or transmitted.
MPEP §2106.05(a) reads:
During examination, the examiner should analyze the "improvements" consideration by evaluating the specification and the claims to ensure that a technical explanation of the asserted improvement is present in the specification, and that the claim reflects the asserted improvement.
However, the MPEP (§2106.05(a)(II)) also warns, “it is important to keep in mind that an improvement in the abstract idea itself (e.g. a recited fundamental economic concept) is not an improvement in technology.” Here, the alleged improvement in the form of dynamic expert selection, removing bias towards head classes without decreasing coverage, and as a result, models trains faster. This is an improvement to the abstract idea of expert selection and removing bias. MPEP 2106.04(d)(1) also states the specification must describe the invention such that the improvement would be apparent to one of ordinary skill in the art. Conversely, if the specification explicitly sets forth an improvement but in a conclusory manner (i.e., a bare assertion of an improvement without the detail necessary to be apparent to a person of ordinary skill in the art), the examiner should not determine the claim improves technology.
Applicant further argues:
C. Under Desjardins, the Recited Operations Integrate Any Abstract Idea into a Practical Application
D. Step 28 Does Not Change the Result
E. The Dependent Claims Are Patent Eligible for the Same Reasons
Examiner response: Examiner respectfully disagrees, the models described in the claim are recited at high-level/black-box implantation. The claims merely describe that the models correspond to different ranges of category frequencies and the input/output of the models, without a description of internal structures/modifications of the models.
In contrast Desjardins identified improvements as to how the machine learning model itself operates, including details as to how the model itself is modified in a particular way. This is not evident in applicants claims, which simply describes models at a high level/black-box. Desjardins also describes a completely different application and improvement from applicants’ application when describing training a machine learning model to learn new tasks while protecting knowledge about previous tasks to overcome the problem of "catastrophic forgetting". Instead, applicants’ claims appear to be closer related to Recentive Analytics, Inc. V. Fox Corp. In Recentive the Federal Circuit ruled that applying generic machine learning (ML) models to a new field or data environment is ineligible for patent protection under 35 U.S.C. § 101. Simply saying "do it with AI/ML" in a new context is insufficient. To be patent-eligible, claims must demonstrate an inventive improvement to the machine learning technology, software functionality, or computer system itself, rather than just using AI as a tool to predict expert weights and remove bias. Increased speed and efficiency resulting from use of computers (with no improved computer techniques) do not themselves create eligibility.
The additional elements as disclosed above in combination of the abstract idea are not sufficient to amount to significantly more than the judicial exception as they are well, understood, routine and conventional activity as disclosed in combination of generic computer functions that are implemented to perform the disclosed abstract idea. Overall, the claim limitations are a combination of mental steps and math under step 2A Prong 1, and additional elements under steps 2A Prong 2 & 2B as detailed in the 101 rejection below.
Dependent claims do not add further limitations that would overcome the 101 rejection and are also rejected for reasons detailed in the 101 rejection below.
Applicant arguments regarding 35 U.S.C. § 103:
Examiner response: Applicant’s arguments, with respect to 35 U.S.C. § 103 have been fully considered and are persuasive. The 35 U.S.C. § 103 rejection has been withdrawn.
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 rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1
According to the first part of the analysis, in the instant case, claims 1-20 are directed to the four statutory categories. Thus, each of the claims falls within one of the four statutory categories (i.e., process, machine, manufacture, or composition of matter).
Regarding Claim 1
2A Prong 1:
calculate category information corresponding to samples based on a plurality of models, the plurality of models including a first model corresponding to a first range of category frequencies, a second model corresponding to a second range of category frequencies different from the first range, and a third model corresponding to a third range of category frequencies different from the first range and the second range; (This step for calculating category information is practically performable in the human mind and is understood to be a recitation of a mental process (i.e., evaluation).)
calculate task loss values associated with respective ones of the samples and respective ones of the plurality of models based on product category information; (This step for calculating task loss values is understood to be a recitation of mathematical calculations (See spec para 0033 and equation 1) or mental process (i.e., evaluation).)
generate training target data for a multilayer perceptron (MLP) gate based on category frequency information for respective ones of the samples, the training target data indicating, for each of the respective ones of the samples, a target weight distribution for the plurality of models based on a category frequency of a category corresponding to the respective sample; (This step for generating training target data is practically performable in the human mind and is understood to be a recitation of a mental process (i.e., evaluation).)
calculate gating loss values for the MLP gate based on category frequency information, the gating loss values determined based on outputs of the MLP gate for the samples and the training target data; (This step for calculating gating loss values is understood to be a recitation of mathematical calculations (See spec para 0034 and equation 1) or mental process (i.e., evaluation).) and
train the MLP gate based on the task loss values and the gating loss values,
the training to derive weights corresponding to respective ones of the plurality of models, (This step for training the model is understood to be a recitation of mathematical calculations (See spec para 0034-0035 and equation 4) or mental process (i.e., evaluation).)
2A Prong 2: This judicial exception is not integrated into a practical application.
Additional elements:
An apparatus to train machine learning models to reduce categorization bias of predicting categories of products, the apparatus comprising: interface circuitry; machine readable instructions; and at least one programmable circuitry to at least one of instantiate or execute the machine readable instructions to: (The apparatus and comprising instructions and circuitry are understood to be generic computer elements to implement an abstract idea (e.g., predicting categories of products) on a computer - See MPEP 2106.05(f).)
the MLP gate, after the training, to output respective per-sample weights for input samples, the respective per-sample weights vary across input samples based on a characterization of each input sample with respect to the first range, the second range, and the third range of category frequencies. (This step is directed to transmitting information, which is understood to be insignificant extra-solution activity and data gathering. See MPEP 2106.05(g).)
The additional elements as disclosed above alone or in combination do not integrate the judicial exception into practical application as they are insignificant extra solution activity in combination of generic computer functions that are implemented to perform the disclosed abstract idea above.
2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Additional elements:
An apparatus to train machine learning models to reduce categorization bias of predicting categories of products, the apparatus comprising: interface circuitry; machine readable instructions; and at least one programmable circuitry to at least one of instantiate or execute the machine readable instructions to: (The apparatus and comprising instructions and circuitry are understood to be generic computer elements to implement an abstract idea (e.g., predicting categories of products) on a computer - See MPEP 2106.05(f).)
the MLP gate, after the training, to output respective per-sample weights for input samples, the respective per-sample weights vary across input samples based on a characterization of each input sample with respect to the first range, the second range, and the third range of category frequencies. (This step is directed to transmitting information, which is understood to be insignificant extra-solution activity and is well understood, routine and conventional activity of transmitting and receiving data as identified by the court (MPEP2106.05(d)(ll)(i))))
The additional elements as disclosed above in combination of the abstract idea
are not sufficient to amount to significantly more than the judicial exception as they are
well, understood, routine and conventional activity as disclosed in combination
of generic computer functions that are implemented to perform the disclosed abstract idea above.
Regarding Claim 9
2A Prong 1:
models, the plurality of models including a first model corresponding to a first range of category frequencies, a second model corresponding to a second range of category frequencies different from the first range, and a third model corresponding to a third range of category frequencies different from the first range and the second range; (This step for evaluating category information is practically performable in the human mind and is understood to be a recitation of a mental process (i.e., evaluation).)
(This step for task loss values is understood to be a recitation of mathematical calculations (See spec para 0033 and equation 1) or mental process (i.e., evaluation).)
(This step for building expert selection is understood to be a recitation of a mental process (i.e., evaluation).)
multilayer perceptron (MLP) gate based on category frequency information, the gating loss values determined based on outputs of the MLP gate for the data points and the expert selection indicator; (This step for calculating gating loss values is understood to be a recitation of mathematical calculations (See spec para 0034 and equation 1) or mental process (i.e., evaluation).) and
summation circuitry to train the MLP gate based on a sum of the task loss and the gating loss, the training to derive weights corresponding to respective ones of the plurality of models, (This step for training the model is understood to be a recitation of mathematical calculations (See spec para 0034-0035 and equation 4) or mental process (i.e., evaluation).)
2A Prong 2: This judicial exception is not integrated into a practical application.
Additional elements:
An apparatus to reduce categorization bias, the apparatus comprising: interface circuitry to retrieve data; computer readable instructions; and at least one programmable circuitry to instantiate: expert circuitry; task loss circuitry; ground truth circuitry; gating loss circuitry; summation circuitry; (The apparatus and comprising circuitry are understood to be generic computer elements - See MPEP 2106.05(f).)
the MLP gate, after the training, to output respective per-data-point weights for each input data point, the respective per-data-point weights vary across input data points based on a characterization of each input data point with respect to the first range, the second range, and the third range of category frequencies. (This step is directed to transmitting information, which is understood to be insignificant extra-solution activity and data gathering. See MPEP 2106.05(g).)
The additional elements as disclosed above alone or in combination do not integrate the judicial exception into practical application as they are insignificant extra solution activity in combination of generic computer functions that are implemented to perform the disclosed abstract idea above.
2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Additional elements:
An apparatus to reduce categorization bias, the apparatus comprising: interface circuitry to retrieve data; computer readable instructions; and at least one programmable circuitry to instantiate: expert circuitry; task loss circuitry; ground truth circuitry; gating loss circuitry; summation circuitry; (The apparatus and comprising circuitry are understood to be generic computer elements - See MPEP 2106.05(f).)
the MLP gate, after the training, to output respective per-data-point weights for each input data point, the respective per-data-point weights vary across input data points based on a characterization of each input data point with respect to the first range, the second range, and the third range of category frequencies. (This step is directed to transmitting information, which is understood to be insignificant extra-solution activity and is well understood, routine and conventional activity of transmitting and receiving data as identified by the court (MPEP2106.05(d)(ll)(i))))
The additional elements as disclosed above in combination of the abstract idea
are not sufficient to amount to significantly more than the judicial exception as they are
well, understood, routine and conventional activity as disclosed in combination
of generic computer functions that are implemented to perform the disclosed abstract idea above.
Regarding Claim 17
2A Prong 1:
A method of reducing categorization bias in machine learning models, the method comprising:
calculating, models, the plurality of models including a first model corresponding to a first range of category frequencies, a second model corresponding to a second range of category frequencies different from the first range, and a third model corresponding to a third range of category frequencies different from the first range and the second range; (This step for calculating category information is practically performable in the human mind and is understood to be a recitation of a mental process (i.e., evaluation).)
calculating, (This step for calculating task loss values is understood to be a recitation of mathematical calculations (See spec para 0033 and equation 1) or mental process (i.e., evaluation).)
building, (This step for building expert selection is understood to be a recitation of a mental process (i.e., evaluation).)
calculating, multilayer perceptron (MLP) gate based on category frequency information, the gating loss values determined based on outputs of the MLP gate for the samples and the expert selection indicator; (This step for calculating gating loss values is understood to be a recitation of mathematical calculations (See spec para 0034 and equation 1) or mental process (i.e., evaluation).) and
training, by executing instructions with at least one processor, the MLP gate based on a sum of the task loss and the gating loss,
the training to derive weights corresponding to respective ones of the plurality of models, (This step for training the model is understood to be a recitation of mathematical calculations (See spec para 0034-0035 and equation 4) or mental process (i.e., evaluation).)
2A Prong 2: This judicial exception is not integrated into a practical application.
Additional elements:
by executing instructions with at least one processor (mere instructions to apply the exception using a generic computer component - see MPEP 2106.05(f))
the MLP gate, after the training, to output respective per-sample weights for each input sample, the respective per-sample weights vary across input samples based on a characterization of each input sample with respect to the first range, the second range, and the third range of category frequencies. (This step is directed to transmitting information, which is understood to be insignificant extra-solution activity and data gathering. See MPEP 2106.05(g).)
The additional elements as disclosed above alone or in combination do not integrate the judicial exception into practical application as they are insignificant extra solution activity in combination of generic computer functions that are implemented to perform the disclosed abstract idea above.
2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Additional elements:
by executing instructions with at least one processor (mere instructions to apply the exception using a generic computer component - see MPEP 2106.05(f))
the MLP gate, after the training, to output respective per-sample weights for each input sample, the respective per-sample weights vary across input samples based on a characterization of each input sample with respect to the first range, the second range, and the third range of category frequencies. (This step is directed to transmitting information, which is understood to be insignificant extra-solution activity and is well understood, routine and conventional activity of transmitting and receiving data as identified by the court (MPEP2106.05(d)(ll)(i))))
The additional elements as disclosed above in combination of the abstract idea
are not sufficient to amount to significantly more than the judicial exception as they are
well, understood, routine and conventional activity as disclosed in combination
of generic computer functions that are implemented to perform the disclosed abstract idea above.
Regarding Claims 2, 10, and 18
2A Prong 1:
wherein a first one of the plurality of models is a softmax cross-entropy loss function. (This step is understood to be a recitation of mathematical calculations or mental process (i.e., evaluation).)
2A Prong 2 & 2B: The claim does not recite any additional elements.
Regarding Claims 3, 11, and 19
2A Prong 1:
wherein a second one of the plurality of models is a balanced softmax loss function. (This step is understood to be a recitation of mathematical calculations or mental process (i.e., evaluation).)
2A Prong 2 & 2B: The claim does not recite any additional elements.
Regarding Claims 4, 12, and 20
2A Prong 1:
wherein a third one of the plurality of models is an inverted softmax loss function. (This step is understood to be a recitation of mathematical calculations or mental process (i.e., evaluation).)
2A Prong 2 & 2B: The claim does not recite any additional elements.
Regarding Claims 5 and 13
2A Prong 1:
wherein a first category frequency corresponds to a first threshold quantity of the samples and a second category frequency corresponds to a second threshold quantity of the samples, the first threshold quantity greater than the second threshold quantity. (This step is understood to be a recitation of mental process (i.e., evaluation).)
2A Prong 2 & 2B: The claim does not recite any additional elements.
Regarding Claims 6 and 14
2A Prong 1:
wherein a third category frequency corresponds to a third threshold quantity of the samples, the second threshold quantity of the samples greater than the third threshold quantity of the samples. (This step is understood to be a recitation of mental process (i.e., evaluation).)
2A Prong 2 & 2B: The claim does not recite any additional elements.
Regarding Claims 7 and 15
2A Prong 1:
wherein the gating loss is multiplied by a multiplication factor to modulate a contribution of the gating loss. (This step is understood to be a recitation of mathematical calculations or mental process (i.e., evaluation).)
2A Prong 2 & 2B: The claim does not recite any additional elements.
Regarding Claims 8 and 16
2A Prong 1:
wherein the task loss values are multiplied by a multiplication factor to modulate contributions of the task loss values. (This step is understood to be a recitation of mathematical calculations or mental process (i.e., evaluation).)
2A Prong 2 & 2B: The claim does not recite any additional elements.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Kobren et al. (US 20230401285 A1) describes augmenting data sets and selecting machine learning models.
THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to TEWODROS E MENGISTU whose telephone number is (571)270-7714. The examiner can normally be reached Mon-Fri 9:30-5:30.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, ABDULLAH KAWSAR can be reached at (571)270-3169. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/TEWODROS E MENGISTU/Primary Examiner, Art Unit 2127
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