Detailed Action
This Office Action is in response to the remarks entered on 06/19/2026. No claims have been canceled. Claims 1-20 are presently pending.
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 .
Note
Examiner notes that if the applicant expands claims 1-10 and 11-15 in a manner that changes the scope of the claims from the original presented claimed inventive concept as presented in claims 16-20 in a way that is not a generic environmental implemental, the claims might be subject to restriction based on original presentation.
Claim Rejections - 35 USC § 112
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention.
Claims 1-10 are rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention.
Regarding claim 1, the claim recites “generating one or more concatenated vectors by concatenating a result of the normalizing, the one or more concatenated vectors including: a first latent vector representation … a second latent vector representation ... and a third latent vector representation;” Nowhere in the specification does it describe “generating one or more concatenated vectors by concatenating a result of the normalizing, the one or more concatenated vectors including: a first latent vector representation of the first segment of the client devices; a second latent vector representation of the second segment of client devices; and a third latent vector representation of the third segment of client devices;” The spec [0037] only discloses generating the concatenated data by concatenating batch normalized categorical data 210 with the numerical data 208, not concatenating a first latent vector representation of the first segment, a second latent vector representation of the second segment, and a third latent vector representation of the third segment of client devices. Instant spec [0050] discloses generating the first latent vector and the second latent vector using the concatenated vector using an encoder, however it is silent in “the one or more concatenated vectors including: a first latent vector representation of the first segment of the client devices; a second latent vector representation of the second segment of client devices; and a third latent vector representation of the third segment of client devices;”. What constitutes “the one or more concatenated vectors including: a first latent vector representation of the first segment of the client devices; a second latent vector representation of the second segment of client devices; and a third latent vector representation of the third segment of client devices;”?
Claims 2-10 depend from claim 1 and inherit the same deficiency.
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 1-10 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.
Regarding claim 1, the claim recites “segmenting, by the processing device, the input data forming a first segment of client devices, a second segment of client devices, and a third segment of client devices; processing, by the processing device, the categorical and numerical data using a variational autoencoder of one or more machine learning models, the processing including: …” It is unclear if the single ‘variational autoencoder’ processes all segments of client devices at once, processes each segment of client devices one at a time, or have three different variational autoencoders to process each segment. The claim does not focus on the ‘segments of client devices’ starting from the “processing, by the processing device, the categorical and numerical data…” limitation. Additionally claim further recites the concatenated vector having first, second and third latent vector which is also unclear as the claim never recites having individual embedding or normalization to have necessity for concatenation. Therefore it is also unclear what constitutes concatenation vector.
For purpose of examination, the examiner treats the limitation to mean: the autoencoder processes each segment of client devices one at a time to generate latent space representations.
Claims 2-10 depend from claim 1 and inherit the same deficiency.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Regarding claim 1,
Step 1: Claim 1 recites a method comprising: embedding and normalizing a categorical data, generating concatenated vectors, calculating a change vector, and combining the change vector with a third latent representation. Therefore, it is directed to the statutory category of Processes.
2A Prong 1:
A method comprising:
segmenting, (mental process of judgment – dividing the data which can be done with the aid of pen and paper)
embedding the categorical data (mental process of evaluation – selecting and modifying the data, which can be done with the aid of pen and paper, and is directed to mathematical calculation, because embedding layer )
normalizing the embedded categorical data (mathematical calculation – normalization, instant specification paragraph [0036])
generating one or more concatenated vectors by concatenating a result of the normalizing, the one or more concatenated vectors including: a first latent vector representation of the first segment of the client devices; a second latent vector representation of the second segment of client devices; and a third latent vector representation of the third segment of client devices; (mental process of evaluation – combining vectors, which can be done with the aid of pen and paper, also can be directed to mathematical calculation, because ‘generating concatenated vectors’ is performed by batch normalizing the categorical data [0050] which includes processes of computing batch mean, batch variance, normalize activations, and scaling of parameters)
computingone or more machine learning model; and (a mathematical concept – calculating a difference between the first vector and the second vector, See spec [0041])
determining, an effect of a treatment on the third segment of the client devices by combining the change vector with the third latent vector representation in the latent space; and (a mathematical concept – calculating the sum of the difference between the first vector and the second vector and the third vector, See spec [0041])
generating, vector
2A Prong 2:
receiving, by a processing device via a network, input data describing interactions with digital content by client devices included in a group of client devices, the input data including categorical and numerical data; (insignificant extra-solution activity MPEP 2106.05(g)(iii) of gathering statistics)
segmenting, by the processing device (mere instructions to apply an exception using a generic computer component MPEP 2106.05(f))
processing, by the processing device, the categorical and numerical data using a variational autoencoder of one or more machine learning models, the processing including: (mere instructions to apply an exception using a generic computer component MPEP 2106.05(f))
embedding (mere instructions to apply an exception using a generic computer component MPEP 2106.05(f))
normalizing (mere instructions to apply an exception using a generic computer component MPEP 2106.05(f))
computing, by the processing device (mere instructions to apply an exception using a generic computer component MPEP 2106.05(f))
determining, by the processing device (mere instructions to apply an exception using a generic computer component MPEP 2106.05(f))
generating, by the processing device, … using a decoder of the one or more machine learning model (mere instructions to apply an exception using a generic computer component MPEP 2106.05(f))
The additional elements as disclosed above alone or in combination do not integrate the judicial exception into practical application as they are mere insignificant extra solution activity, combination of generic computer functions that are restricted to field of use are implemented to perform the disclosed abstract idea above.
2B:
receiving, by a processing device via a network, input data describing interactions with digital content by client devices included in a group of client devices, respectively; (indicated as an insignificant extra-solution activity MPEP 2106.05(g)(iii) in Step 2A Prong 2. Therefore, the limitation is re-evaluated in Step 2B as well understood, routine and conventional activity MPEP 2106.05(d)(II)(i) of transmitting or receiving data over a network)
segmenting, by the processing device (mere instructions to apply an exception using a generic computer component MPEP 2106.05(f))
processing, by the processing device, the categorical and numerical data using a variational autoencoder of one or more machine learning models, the processing including: (mere instructions to apply an exception using a generic computer component MPEP 2106.05(f))
embedding (mere instructions to apply an exception using a generic computer component MPEP 2106.05(f))
normalizing (mere instructions to apply an exception using a generic computer component MPEP 2106.05(f))
computing, by the processing device (mere instructions to apply an exception using a generic computer component MPEP 2106.05(f))
determining, by the processing device (mere instructions to apply an exception using a generic computer component MPEP 2106.05(f))
generating, by the processing device, … using a decoder of the one or more machine learning model (mere instructions to apply an exception using a generic computer component MPEP 2106.05(f))
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 and usage of elements that are restricted to field of use that are implemented to perform the disclosed abstract idea above.
Regarding claim 2,
Step 1: Processes, as above.
2A Prong 1: Incorporates the rejection of claim 1.
2A Prong 2: wherein the one or more machine learning models are trained on the input data without an indication of the digital content recommendation. (mere instructions to apply an exception using a computer MPEP 2106.05(f) – generic training process of a model)
2B: wherein the one or more machine learning models are trained on the input data without an indication of the digital content recommendation. (mere instructions to apply an exception using a computer MPEP 2106.05(f) – generic training process of a model)
Regarding claim 3,
Step 1: Processes, as above.
2A Prong 1: further comprising regularizing the latent space using a Kullback-Leibler divergence loss. (a mathematical concept – normalizing the latent space matrix (a set of values) using a math calculation)
2A Prong 2: This judicial exception is not integrated into a practical application.
2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Regarding claim 4,
Step 1: Processes, as above.
2A Prong 1: Incorporates the rejection of claim 1.
2A Prong 2: wherein the variational autoencoder performs decoding in the latent space and determines the effect of the treatment on the third segment of the client devices. (mere instructions to apply an exception using a generic computer component MPEP 2106.05(f))
2B: wherein the variational autoencoder performs decoding in the latent space and determines the effect of the treatment on the third segment of the client devices. (mere instructions to apply an exception using a generic computer component MPEP 2106.05(f))
Regarding claim 5,
Step 1: Processes, as above.
2A Prong 1: Incorporates the rejection of claim 1.
2A Prong 2: wherein the one or more normalization layers perform batch normalization of the categorical and numerical data. (mere instructions to apply an exception using a generic computer component MPEP 2106.05(f))
2B: wherein the one or more normalization layers perform batch normalization of the categorical and numerical data. (mere instructions to apply an exception using a generic computer component MPEP 2106.05(f))
Regarding claim 6,
Step 1: Processes, as above.
2A Prong 1: Incorporates the rejection of claim 5.
2A Prong 2: wherein the one or more machine learning models include a machine learning model is trained using a binary cross-entropy loss for the categorical data. (mere instructions to apply an exception using a generic computer component MPEP 2106.05(f) – conventional machine learning model training process)
2B: wherein the one or more machine learning models include a machine learning model is trained using a binary cross-entropy loss for the categorical data. (mere instructions to apply an exception using a generic computer component MPEP 2106.05(f) – conventional machine learning model training process)
Regarding claim 7,
Step 1: Processes, as above.
2A Prong 1: Incorporates the rejection of claim 5.
2A Prong 2: wherein the one or more machine learning models include a machine learning model is trained using a mean squared loss for the numerical data. (mere instructions to apply an exception using a generic computer component MPEP 2106.05(f) – conventional machine learning model training process)
2B: wherein the one or more machine learning models include a machine learning model is trained using a mean squared loss for the numerical data. (mere instructions to apply an exception using a generic computer component MPEP 2106.05(f) – conventional machine learning model training process)
Regarding claim 8,
Step 1: Processes, as above.
2A Prong 1: Incorporates the rejection of claim 5.
2A Prong 2: wherein the one or more machine learning model process the categorical data using a softmax activation. (mere instructions to apply an exception using a generic computer component MPEP 2106.05(f))
2B: wherein the one or more machine learning model process the categorical data using a softmax activation. (mere instructions to apply an exception using a generic computer component MPEP 2106.05(f))
Regarding claim 9,
Step 1: Processes, as above.
2A Prong 1: Incorporates the rejection of claim 5.
2A Prong 2: wherein the one or more machine learning model process the numerical data using a linear activation. (mere instructions to apply an exception using a generic computer component MPEP 2106.05(f))
2B: wherein the one or more machine learning model process the numerical data using a linear activation. (mere instructions to apply an exception using a generic computer component MPEP 2106.05(f))
Regarding claim 10,
Step 1: Processes, as above.
2A Prong 1: Incorporates the rejection of claim 1.
2A Prong 2: wherein at least one of the first segment of the client devices or the second segment of the client devices receives the digital content recommendation. (a field of use and technological environment MPEP 2106.05(h))
2B: wherein at least one of the first segment of the client devices or the second segment of the client devices receives the digital content recommendation. (a field of use and technological environment MPEP 2106.05(h))
Regarding claim 11,
Step 1: Claim 11 recites a system comprising: a memory component; and a processing device coupled to the memory component, the processing device to perform operations. Therefore, it is directed to the statutory category of a machine.
2A Prong 1:
generating a first latent vector representation of a first segment of the client devices (mental process of observation – generating a representation of data involves selecting and combining representative values from a vector, which does not require a computer component, also can be directed to a mathematical calculation, because the generation of latent vectors is performed using a variational autoencoder which is composed of math functions such as transformation and activation functions)
generating a second latent vector representation of a second segment of the client devices the second latent vector representing concatenated categorical and numerical data of the second segment of the client devices; (mental process of observation – generating a representation of data involves selecting and combining representative values from a vector, which does not require a computer component, also can be directed to a mathematical calculation, because the generation of latent vectors is performed using a variational autoencoder which is composed of math functions such as transformation and activation functions)
computing a change vector based on a difference between the first latent vector representation and the second latent vector representation in a latent space of the one or more machine learning model; and (a mathematical concept – calculating a difference between the first vector and the second vector)
determining an effect of an treatment on a third segment of the client devices by combining the change vector with a third latent vector representation in the latent space for the third segment of the client devices; (a mathematical concept – calculating the sum of the difference between the first vector and the second vector and the third vector)
generating an indication of an effect on the third segment of the client devices based on the combined change vector with the third latent vector representation
2A Prong 2:
A system comprising: a memory component; and a processing device coupled to the memory component, the processing device to perform operations comprising: (mere instructions to apply an exception using a generic computer component MPEP 2106.05(f))
receiving, via a network, input data describing interactions of client devices included in a group of client devices, the input data including categorical and numerical data; (insignificant extra-solution activity MPEP 2106.05(g)(iii) of gathering statistics)
generating … using an encoder of one or more machine learning models (mere instructions to apply an exception using a generic computer component MPEP 2106.05(f))
generating … using an encoder of the one or more machine learning model; (mere instructions to apply an exception using a generic computer component MPEP 2106.05(f))
generating … using a decoder of the one or more machine learning model; (mere instructions to apply an exception using a generic computer component MPEP 2106.05(f))
The additional elements as disclosed above alone or in combination do not integrate the judicial exception into practical application as they are mere insignificant extra solution activity, combination of generic computer functions that are restricted to field of use are implemented to perform the disclosed abstract idea above.
2B:
A system comprising: a memory component; and a processing device coupled to the memory component, the processing device to perform operations comprising: (mere instructions to apply an exception using a generic computer component MPEP 2106.05(f))
receiving, via a network, input data describing interactions of client devices included in a group of client devices, the input data including categorical and numerical data; (indicated as an insignificant extra-solution activity MPEP 2106.05(g)(iii) in Step 2A Prong 2. Therefore, the limitation is re-evaluated in Step 2B as well understood, routine and conventional activity MPEP 2106.05(d)(II)(i) of transmitting or receiving data over a network)
generating … using an encoder of one or more machine learning models (mere instructions to apply an exception using a generic computer component MPEP 2106.05(f))
generating … using an encoder of the one or more machine learning model; (mere instructions to apply an exception using a generic computer component MPEP 2106.05(f))
generating … using a decoder of the one or more machine learning model; (mere instructions to apply an exception using a generic computer component MPEP 2106.05(f))
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 and usage of elements that are restricted to field of use that are implemented to perform the disclosed abstract idea above.
Claim 12 is a system claim having similar limitation to claim 4. Therefore, claim 12 is rejected under the same rationale as of claim 4 above.
Regarding claim 13,
Step 1: A machine, as above.
2A Prong 1: Incorporates the rejection of claim 11.
2A Prong 2: wherein the one or more machine learning models include a machine learning model is trained using a binary cross-entropy loss for categorical data included in the input data. (mere instructions to apply an exception using a generic computer component MPEP 2106.05(f) – conventional machine learning model training process)
2B: wherein the one or more machine learning models include a machine learning model is trained using a binary cross-entropy loss for categorical data included in the input data. (mere instructions to apply an exception using a generic computer component MPEP 2106.05(f) – conventional machine learning model training process)
Regarding claim 14,
Step 1: A machine, as above.
2A Prong 1: Incorporates the rejection of claim 11.
2A Prong 2: wherein the one or more machine learning models include a machine learning model trained using a mean squared loss for numerical data included in the input data. (mere instructions to apply an exception using a generic computer component MPEP 2106.05(f) – conventional machine learning model training process)
2B: wherein the one or more machine learning models include a machine learning model trained using a mean squared loss for numerical data included in the input data. (mere instructions to apply an exception using a generic computer component MPEP 2106.05(f) – conventional machine learning model training process)
Regarding claim 15,
Step 1: A machine, as above.
2A Prong 1: Incorporates the rejection of claim 11.
2A Prong 2: wherein one or more machine learning models include a machine learning model trained on the input data without an indication of a second version of the application. (mere instructions to apply an exception using a computer MPEP 2106.05(f) – generic training process of a model)
2B: wherein one or more machine learning models include a machine learning model trained on the input data without an indication of a second version of the application. (mere instructions to apply an exception using a computer MPEP 2106.05(f) – generic training process of a model)
Regarding claim 16,
Step 1: Claim 16 recites a non-transitory computer-readable storage medium storing executable instructions, which when executed by a processing device, cause the processing device to perform operations. Therefore, it is directed to the statutory category of a machine.
2A Prong 1:
representing the categorical data and the numerical data as a concatenated vector by batch normalizing the categorical data; (mathematical concept – converting the data to a vector representation by normalizing the data)
generating a first latent vector representation of a first segment of the client devices (mental process of observation – generating a representation of data involves selecting and combining representative values from a vector, which does not require a computer component, also can be directed to a mathematical calculation, because the generation of latent vectors is performed using a variational autoencoder which is composed of math functions such as transformation and activation functions)
generating a second latent vector representation of a second segment of the client devices based on the concatenated vector the second latent vector representing concatenated categorical and numerical data of the second segment of the client devices; (mental process of observation – generating a representation of data involves selecting and combining representative values from a vector, which does not require a computer component, also can be directed to a mathematical calculation, because the generation of latent vectors is performed using a variational autoencoder which is composed of math functions such as transformation and activation functions)
computing a change vector based on a difference between the first latent vector representation and the second latent vector representation in a latent space of the machine learning model; (a mathematical concept – calculating the difference between the first vector and the second vector)
determining an effect of a treatment on a third segment of the client devices by combining the change vector with a third latent vector representation in the latent space for the third segment of the client devices; and (a mathematical concept – calculating the sum of the difference between the first vector and the second vector and the third vector)
generating an indication of an effect of a treatment on a third segment of the client devices by decoding the change vector combined with the third latent vector representation
2A Prong 2:
A non-transitory computer-readable storage medium storing executable instructions, which when executed by a processing device, cause the processing device to perform operations comprising: (mere instructions to apply an exception using a generic computer component MPEP 2106.05(f))
receiving, via a network, input data describing interactions of client devices included in a group of client devices, the input data includes categorical data and numerical data; (insignificant extra-solution activity MPEP 2106.05(g)(iii) of gathering statistics)
generating using an encoder of one or more machine learning models (mere instructions to apply an exception using a generic computer component MPEP 2106.05(f))
generating … using an encoder of the one or more machine learning models (mere instructions to apply an exception using a generic computer component MPEP 2106.05(f))
generating using a decoder of the one or more machine learning models (mere instructions to apply an exception using a generic computer component MPEP 2106.05(f))
The additional elements as disclosed above alone or in combination do not integrate the judicial exception into practical application as they are mere insignificant extra solution activity, combination of generic computer functions that are restricted to field of use are implemented to perform the disclosed abstract idea above.
2B:
A non-transitory computer-readable storage medium storing executable instructions, which when executed by a processing device, cause the processing device to perform operations comprising: (mere instructions to apply an exception using a generic computer component MPEP 2106.05(f))
receiving, via a network, input data describing interactions of client devices included in a group of client devices, the input data includes categorical data and numerical data; (indicated as an insignificant extra-solution activity MPEP 2106.05(g)(iii) in Step 2A Prong 2. Therefore, the limitation is re-evaluated in Step 2B as well understood, routine and conventional activity MPEP 2106.05(d)(II)(i) of transmitting or receiving data over a network)
generating using an encoder of one or more machine learning models (mere instructions to apply an exception using a generic computer component MPEP 2106.05(f))
generating … using an encoder of the one or more machine learning models (mere instructions to apply an exception using a generic computer component MPEP 2106.05(f))
generating using a decoder of the one or more machine learning models (mere instructions to apply an exception using a generic computer component MPEP 2106.05(f))
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 and usage of elements that are restricted to field of use that are implemented to perform the disclosed abstract idea above.
Regarding claim 17,
non-transitory computer-readable storage medium storing executable instructions, which when executed by a processing device, cause the processing device to perform operations 2A Prong 1: Incorporates the rejection of claim 16.
2A Prong 2: wherein the one or more machine learning models include a variational autoencoder. (mere instructions to apply an exception using a generic computer component MPEP 2106.05(f))
2B: wherein the one or more machine learning models include a variational autoencoder. (mere instructions to apply an exception using a generic computer component MPEP 2106.05(f))
Claim 18 is a non-transitory computer-readable storage medium claim having similar limitation to claim 3. Therefore, claim 18 is rejected under the same rationale as claim 3 above.
Claim 19 is a non-transitory computer-readable storage medium claim having similar limitation to claim 6. Therefore, claim 19 is rejected under the same rationale as claim 6 above.
Claim 20 is a non-transitory computer-readable storage medium claim having similar limitation to claim 7. Therefore, claim 18 is rejected under the same rationale as claim 7 above.
Claim Rejections - 35 USC § 103
Amended claims were received on 06/19/2026. 35 U.S.C. 103 rejection have been withdrawn.
Response to Arguments
Response to Arguments under 35 U.S.C. 101
Arguments: Applicant asserts that the claims as amended are not directed to abstract ideas because (a) Claim 1 as amended recite specific technical operations that cannot practically be performed in the human mind, and the Examiner’s characterization of the claimed encoder and decoder operations are “mental processes” reflect precisely the type of overbroad, high-level generality that Desjardins cautions against, (b) the claims as amended reflect disclosed improvements of disclosed in the instant specification [0022], and (c) Claims 11 and 16 have been amended and as amended, are allowable under 35 U.S.C. 101.
Examiner’s Response: Examiner respectfully disagrees. Regarding (a), the examiner notes that the applicant misunderstood the 35 U.S.C. 101 rejection. The examiner agrees that the encoder and decoder operations cannot be performed in the human mind. However, the rejection treated the encoder and decoder operations as insignificant extra-solution activity MPEP 2106.05(f) of mere instructions to apply an exception using generic computer components. The limitations of ‘embedding the categorical data’, ‘normalizing the embedded categorical data’, and ‘generating one or more concatenated vectors by concatenating a result’ are mental processes as ‘embedding’, ‘normalizing’ and ‘concatenating’ are data processing methods that can be done with the aid of pen and paper. While the Board concluded in Desjardins that "adjust the first values of the plurality of parameters to optimize performance of the machine learning model on the second machine learning task while protecting performance of the machine learning model on the first machine learning task" is not the identified mathematical calculation, the reason why the Board came to this conclusion is because the limitation constitutes an improvement to how the machine learning itself operates. Therefore, whether the cited limitation recites an improvement to how the machine learning itself operates is crucial to the patent eligibility of the limitation.
The examiner now moves on to the argument (b) to evaluate whether the cited limitation recites an improvement. Regarding (b), ‘computing, by the processing device, a change vector based on a difference between the first latent vector representation and the second latent vector representation … determining, an effect of a treatment on the third segment of the client devices by combining the change vector with the third latent vector representation’ of the limitations that Applicant cites as being allegedly reflective of a technological improvement are in fact parts of the judicial exception. Insofar as the judicial exception cannot provide the inventive concept, MPEP 2106.05(I), and insofar as the only additional elements of the claim recite generic neural network implementation of abstract idea of computing a difference between dataset, combining the difference to another dataset to generate recommendation and/or insignificant extra-solution activity that is well-understood, routine and conventional, the claims do not reflect any technological improvement that may be recited in the specification.
Accordingly, the arguments regarding claim 1 are not persuasive. Similarly, the arguments regarding claims 11 and 16 are not persuasive. Therefore, the arguments regarding claims 2-10, 12-15 and 17-20 depend from independent claims 1, 11 and 16 are not persuasive.
Response to Arguments under 35 U.S.C. 103
Amended claims were received on 06/19/2026. 35 U.S.C. 103 rejection have been withdrawn.
Conclusion
Claims 1-20 have been searched, but have not been rejected with respect to any prior art statues.
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Chu et al. “Treatment effect prediction with adversarial deep learning using electronic health records” (This prior art is pertinent because it discloses predicting treatment effect based on latent space representations of input data)
Lotfollahi et al., “scGen predicts single-cell perturbation responses” (This prior art is pertinent because it discloses calculating differences between latent spaces between two different groups, and then applying the latent space differences to a third group)
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 JUN KWON whose telephone number is (571)272-2072. The examiner can normally be reached Monday – Friday 8:00AM – 5:00PM ET.
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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/JUN KWON/Examiner, Art Unit 2127
/ABDULLAH AL KAWSAR/Supervisory Patent Examiner, Art Unit 2127
