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 .
Style
In this action unitalicized bold is used for claim language, while italicized bold is used for emphasis.
Information Disclosure Statement
All information disclosure statements were submitted prior to the first action and are incompliance with the provisions of 37 C.F.R. § 1.97. Accordingly, they have been considered.
Applicant Reply
“The claims may be amended by canceling particular claims, by presenting new claims, or by rewriting particular claims as indicated in 37 CFR 1.121(c). The requirements of 37 CFR 1.111(b) must be complied with by pointing out the specific distinctions believed to render the claims patentable over the references in presenting arguments in support of new claims and amendments. . . . The prompt development of a clear issue requires that the replies of the applicant meet the objections to and rejections of the claims. Applicant should also specifically point out the support for any amendments made to the disclosure. See MPEP § 2163.06. . . . An amendment which does not comply with the provisions of 37 CFR 1.121(b), (c), (d), and (h) may be held not fully responsive. See MPEP § 714.” MPEP § 714.02. Generic statements or listing of numerous paragraphs do not “specifically point out the support for” claim amendments. “With respect to newly added or amended claims, applicant should show support in the original disclosure for the new or amended claims. See, e.g., Hyatt v. Dudas, 492 F.3d 1365, 1370, n.4, 83 USPQ2d 1373, 1376, n.4 (Fed. Cir. 2007) (citing MPEP § 2163.04 which provides that a ‘simple statement such as ‘applicant has not pointed out where the new (or amended) claim is supported, nor does there appear to be a written description of the claim limitation ‘___’ in the application as filed’ may be sufficient where the claim is a new or amended claim, the support for the limitation is not apparent, and applicant has not pointed out where the limitation is supported.’)” MPEP § 2163(II)(A).
Non-Obvious Subject Matter under 35 U.S.C. § 103
Claims 2-4, 10-12, and 18-20 are non-obvious in view of the prior art. This is not an indication of allowability. Note that any amendments to these claims or to the claims from which they depend, may result in a new search and further consideration of this determination in view of new art.
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 1-20 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 pre-AIA the inventor(s), at the time the application was filed, had possession of the claimed invention.
Generally: separately listed claim elements are construed as distinct components, all claim terms must be given weight, and there is presumed to be a difference in meaning and scope when different words or phrases are used in separate claims. Since different term or phrases are presumed to differ in scope and each term or phrase in the claims must find clear support in the description, a description of a single element in the Specification may fail to support multiple claim terms. “[C]laims must ‘conform to the invention as set forth in the remainder of the specification and the terms and phrases used in the claims must find clear support or antecedent basis in the description so that the meaning of the terms in the claims may be ascertainable by reference to the description.’ 37 C.F.R. § 1.75(d)(1).” Phillips v. AWH Corp., 415 F.3d 1303, 1316 (Fed. Cir. 2005) (as cited in MPEP § 2111). Further, a lack of lack of detail in the Specification describing how a claimed result is achieved can support a finding that the Applicant was not in possession of the claimed invention at the time of filing, notwithstanding verbatim support. “It is not enough that one skilled in the art could write a program to achieve the claimed function because the specification must explain how the inventor intends to achieve the claimed function to satisfy the written description requirement. See, e.g., Vasudevan Software, Inc. v. MicroStrategy, Inc., 782 F.3d 671, 681-683, 114 USPQ2d 1349, 1356, 1357 (Fed. Cir. 2015) (reversing and remanding the district court’s grant of summary judgment of invalidity for lack of adequate written description where there were genuine issues of material fact regarding "whether the specification show[ed] possession by the inventor of how accessing disparate databases is achieved"). If the specification does not provide a disclosure of the computer and algorithm in sufficient detail to demonstrate to one of ordinary skill in the art that the inventor possessed the invention a rejection under 35 U.S.C. 112(a) or pre-AIA 35 U.S.C. 112, first paragraph, for lack of written description must be made.” MPEP § 2161.01(I). “An original claim may lack written description support when (1) the claim defines the invention in functional language specifying a desired result but the disclosure fails to sufficiently identify how the function is performed or the result is achieved[.] See Ariad Pharms., Inc. v. Eli Lilly & Co., 598 F.3d 1336, 1349-50 (Fed. Cir. 2010) (en banc). The written description requirement is not necessarily met when the claim language appears in ipsis verbis in the specification. ‘Even if a claim is supported by the specification, the language of the specification, to the extent possible, must describe the claimed invention so that one skilled in the art can recognize what is claimed. The appearance of mere indistinct words in a specification or a claim, even an original claim, does not necessarily satisfy that requirement.’” MPEP § 2163.03.
All independent claims substantially recite “generating, using the multiple statistical descriptors, an output which is personalised to the user device[.]” The Specification does not describe any mechanism by which multiple “statistical descriptors” are used generate an output personalized to a user device. While the Specification explains basic transfer learning, a common technique used to personalize models, nothing was found in the Specification describing how the use of multiple statistical descriptors would contribute to the creation of personalized models. Arguments based on verbatim language are unlikely to be convincing. If there is implicit support that has been overlooked, a citation thereto may be a way forward.
All dependent claims are rejected as containing the limitations of the claims from which they depend.
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 1-20 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 pre-AIA the applicant regards as the invention.
Generally: separately listed claim elements are construed as distinct components, that all claim terms must be given weight, there is presumed to be a difference in meaning and scope when different words or phrases are used in separate claims, and repeated and consistent descriptions in the specification indicate the proper scope of a claimed term. “[C]laims must ‘conform to the invention as set forth in the remainder of the specification and the terms and phrases used in the claims must find clear support or antecedent basis in the description so that the meaning of the terms in the claims may be ascertainable by reference to the description.’ 37 C.F.R. § 1.75(d)(1).” Phillips v. AWH Corp., 415 F.3d 1303, 1316 (Fed. Cir. 2005) (as cited in MPEP § 2111). Therefore, use of two different terms in the claims that both rely on the description of a single structure in the Specification may render at least one term indefinite because there is no way to determine which term should be construed in view of the description of the single structure.
All independent claims substantially recite “the pre-trained ML model has been trained to generate a distribution of embedded representations for an input . . . using the pre-trained model, a distribution of embedded representations for each of the plurality of samples . . . generating multiple statistical descriptors for the distribution of embedded representations[.]” It is not clear whether “the distribution of embedded representations” refers back to the first of second instance of “a distribution of embedded representations[.]” It is also unclear whether the second instance of “a distribution of embedded representations” introduces a new distribution of embedded representations, or if the second “distribution” was inadvertently written using the indefinite article.
All independent claims substantially recite “generating multiple statistical descriptors for the distribution of embedded representations, and generating, using the multiple statistical descriptors, an output which is personalised to the user device[.]” There is no way to determine an objective meaning for the claimed “statistical descriptors.” The Specification explains “statistical descriptors may comprise statistical moments which are statistics that measure something relative to the center of the values, for example average, variance, skewness and kurtosis.” Spec. ¶10. While the four “statistical moments” are terms of art, at least in the area of mathematics, the claims do not limit the statistical descriptors to statistical moments. This is problematic, because the use of a different term implies a different meaning. The term statistical descriptor must have a different scope and meaning than the term “statistical moment.” To the extent that the terms differ, the specification fails to provide any objective measure that would allow one of ordinary skill in the art to reasonably ascertain the meaning of this applicant invented term. Arguments that merely reference a statistical moment as providing a definition are unlikely to be convincing. If the invention relates to a statistical moment, using that term in the claims may be a way forward.
All independent claims substantially recite “generating, using the multiple statistical descriptors, an output which is personalised to the user device[.]” It is not clear whether the multiple statistical descriptors must be used to generate the output which is personalized to the user device by some other technique, or if the multiple statistical descriptors must cause the personalization of the output. Since there are two similarly reasonable but inconsistent ways of reading the claim, the claim language is indefinite.
Claims 17 recites “A non-transitory medium that stores one or more instructions executed by a controller of an electronic user device for the electronic user device to perform an operation, the operation comprising: . . . ” It is not clear whether the language “for the electronic user device to perform an operation” requires that the operation actually be performed when the instructions are executed.
All dependent claims are rejected as containing the limitations of the claims from which they depend.
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 a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) and the claims as a whole, considering all claim elements both individually and in combination, do not amount to significantly more.
Step 1: Is the claim to a process, machine, manufacture, or composition of matter?
All claims are found to be directed to one of the four statutory categories, unless otherwise indicated in this action.
Step 2A Prongs One and Two (Alice Step 1): According to Office guidance, claims that read on math do not recite an abstract idea at step 2A1, when the claims fail to refer to the math by name.1 The MPEP also equates “recit[ing] a judicial exception” with “state[ing]” or “describ[ing]” an abstract idea in the claims.2 Consistent with this guidance, an abstract idea may be first recited in a dependent claim even though the independent claims read on that abstract idea. Claim limitations which recite any of the abstract idea groupings set forth in the manual are found to be directed, as a whole, to an abstract idea unless otherwise indicated.3 The claims do not recite additional elements that integrate the abstract ideas into a practical application.4 To confer patent eligibility to an otherwise abstract idea, claims may recite a specific means or method of solving a specific problem in a technological field.5
Independent Claims
1. A method for personalising a machine learning, ML, model, on an electronic user device, the method comprising: (This is written as a non-limiting intended use.) obtaining a pre-trained ML model having a set of basic parameters, (This reads on use of a generic computer component used in the implementation of the abstract idea. To the extent that “obtaining” refers to data transmission, data transmission of a pre-trained model is extra-solution activity.) wherein the pre-trained ML model has been trained to generate a distribution of embedded representations for an input; (This reads on carrying out mathematical operations using generic computer components in the form of a generic model.) receiving at least one set of user data comprising a plurality of samples, wherein each set is associated with a particular problem; (Receiving data is extra-solution activity.) generating, using the pre-trained model, a distribution of embedded representations for each of the plurality of samples; (This reads on carrying out mathematical operations using generic computer components in the form of a generic model.) generating multiple statistical descriptors for the distribution of embedded representations, (This reads on carrying out mathematical operations using generic computer components in the form of a generic model.) and generating, using the multiple statistical descriptors, an output which is personalised to the user device, (This reads on carrying out mathematical operations using generic computer components in the form of a generic model. Further, claiming that the model is “personalized” is written as an intended use. While it makes sense that a model having received user data would have an output that is more closely related to the user, this does not limit the output to any particular structure or require specific operations to be performed. As such, the personalization of the output, is written as a non-limiting intended use. Further still, personalizing a model output reads on implementation of a mental process using generic computer components.) wherein there are fewer statistical descriptors than embedded representations. (This reads on a natural, mathematical relationship between embeddings and statistical descriptors.)
Independent claim 9 is rejected for the reasons given in the rejection of claim 1. The claim also recites “An electronic user device for personalising a machine learning, ML, model, comprising: a memory; and a processor configured to:” carry out the techniques of claim 1. This is merely an instruction to apply the judicial exception on a computer.
Independent claim 17 is rejected for the reasons given in the rejection of claim 1. The claim also recites “A non-transitory medium that stores one or more instructions executed by a controller of an electronic user device for the electronic user device to perform an operation, the operation comprising:” the techniques of claim 1. This is merely an instruction to apply the judicial exception on a computer.
Step 2B (Alice Step 2): The rejected claims do not recite additional elements that amount to significantly more than the judicial exception.
All additional limitations that do not integrate the claimed judicial exception into a practical application also fail to amount to significantly more, for the reasons given at step 2A2. All limitations found to be extra-solution activity at step 2A2 are found to be WURC, including limitations that read on mere data gathering, data storage, and data input/output/transfer. The claimed “obtaining a pre-trained ML model having a set of basic parameters,” and “receiving at least one set of user data comprising a plurality of samples, wherein each set is associated with a particular problem;” read on mere transmission of data, which is WURC. Should any other claim limitations be rejected at step 2A1 as extra-solution activity but omitted in the section directly above, it should be understood that such limitations are also found to be WURC at this step. Generic data input/output, storage, repetitive processing operations, and generic display of information and have been found to be generic WURC operations that do not transform the abstract idea into patent eligible subject matter, at the Alice step two analysis.6 Other aspects of generic computing have also been found to be WURC.7 Further, the description itself may provide support for a finding that claim elements are WURC. The analysis under § 112(a) as to whether a claim element is “so well-known that it need not be described in detail in the patent specification” is the same as the analysis as to whether the claim element is widely prevalent or in common use.8 Similarly, generic descriptions in the Specification of claimed components and features has been found to support a conclusion that the claimed components were conventional.9 Improvements to the relevant technology may support a finding that the claims include a patent eligible inventive concept. But some mechanism that results in any asserted improvements must be recited in the claim, and the Specification must provide sufficient details such that one of ordinary skill in the art would recognize the claimed invention as providing the improvement.10 This applies to the dependent claims below.
Dependent Claims:
2. The method of claim 1, wherein the multiple statistical descriptors include at least two statistical moments selected from average, variance, skewness and kurtosis. (This merely limits to specific mathematical relationships.)
3. The method of claim 2, wherein generating multiple statistical descriptors comprises generating three statistical moments which are average, variance and skewness. (This merely limits to specific mathematical relationships.)
4. The method of claim 2, wherein the multiple statistical descriptors include at least one other statistical measure. (This merely limits to specific mathematical relationships.)
5. The method of claim 1, wherein the pre-trained ML model includes a natural language processing ML model in the form of a tokenizer which generates embedded representations in the form of tokens from a text input. (This reads on merely converting language to vectors, which reads on a mathematical/mental process.)
6. The method of claim 5, further comprising generating a classification token as a first token in the distribution of embedded representations, and wherein outputting the multiple statistical descriptors includes outputting the classification token. (Generating and outputting a “token,” which reads on a vector, refers to outputting data, which is extra-solution activity and WURC. With respect to the generation of the token itself, this merely refers to the mathematical/mental operation of putting words into vector format.)
7. The method of claim 1, further comprising adding a plurality of adapter modules to the pre-trained ML model to create a local ML model wherein each adapter module has a set of adapter parameters. (Combining two models, in no particular way, reads on mathematical/mental processes. The inclusion of generic parameters merely reads on mathematical relationships within the model.)
8. The method of claim 7, further comprising: receiving multiple training sets each comprising a plurality of training samples, wherein each training set is associated with a particular problem; (Receiving training data is extra-solution activity and WURC.) personalising the local ML model using continual learning by selecting a training set; fixing the set of basic parameters; (This reads on using generic computer components and data to carry out the mental/mathematical process of personalizing a model.) using the selected training set to learn a set of adapter parameters for one adapter module in the plurality of adapter modules; and iterating the selecting, fixing and using for each training set. (This merely limits to a particular field of use defined by a different data environment, implemented using a generic training technique.)
For rejection of claims 10-16, see rejections of claims 2-8.
For rejection of claims 18-20, see rejections of claims 2-4.
All dependent claims are rejected as containing the material of the claims from which they depend.
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 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 1, 5-9, and 13-17 are rejected under 35 U.S.C. 103 as being unpatentable over Bellegarda (US 2022/0229985, different assignee) and Arora (DIRECTOR: Generator-Classifiers For Supervised Language Modeling, 2022).
1. A method for personalising a machine learning, ML, model, on an electronic user device, the method comprising: (This is written as a non-limiting intended use.) obtaining a pre-trained ML model having a set of basic parameters, wherein the pre-trained ML model has been trained to generate a distribution of embedded representations for an input; (“Systems and methods for updating a language model are provided. One example method includes, at an electronic device with one or more processors and memory, training a first language model using a training data set comprising user-generated and user-relevant data, and storing a reference version of the first language model including a first overall probability distribution.” Bellegarda Abstract. “Text prediction can be implemented using a language model initially trained using a static training corpus including a very large amount of text samples. In order to better reflect individual user idiosyncrasies and other evolving linguistic events, the language model is then updated (e.g., adapted) using actual user data produced on a device implementing the language model.” Bellegarda ¶3.) receiving at least one set of user data comprising a plurality of samples, wherein each set is associated with a particular problem; (“In order to better reflect individual user idiosyncrasies and other evolving linguistic events, the language model is then updated (e.g., adapted) using actual user data produced on a device implementing the language model.” Bellegarda ¶3.) generating, using the pre-trained model, a distribution of embedded representations for each of the plurality of samples; (Bellegarda teaches “For example, reference user language model 606 may predict one or more tokens using an output probability distribution Y over an underlying token vocabulary given particular previous tokens W (e.g., a particular input context, such as a partial sentence). The output probability distribution Y is drawn from the first overall probability distribution D, which represents all output probability distributions over all previous tokens (e.g., over all input contexts). . . . [0196] System 600 includes updating module 610, which receives as input reference user language model 606 and initial dynamic language model 608. Updating module 610 obtains dynamic language model 614, which includes a second overall probability distribution D′. For example, dynamic language model 614 may predict one or more tokens using an output probability distribution Y′ over an underlying token vocabulary given particular previous tokens W (e.g., a particular input context, such as a partial sentence).” Bellegarda does not expressly state that tokens are embedded.
Arora teaches “The second head is for classification and it also maps each token representation into a |V| dimensional vector using a linear layer. Then, however, it applies a sigmoid to obtain an independent binomial distribution2 for each word in the vocabulary V. Note that while tokens x1:t−1 are given as inputs and processed by the shared transformer core, the next token candidates for xt are encoded in the row vectors of the linear layer in the classifier head.” Arora P. 514.
It would have been obvious to one of ordinary skill in the art before the effective filing date to combine the teaching of Arora because using vectors/embeddings to represent tokens lets models attribute values to tokens during training.)
generating multiple statistical descriptors for the distribution of embedded representations, and (“For example, reference user language model 606 may predict one or more tokens using an output probability distribution Y over an underlying token vocabulary given particular previous tokens W (e.g., a particular input context, such as a partial sentence). The output probability distribution Y is drawn from the first overall probability distribution D, which represents all output probability distributions over all previous tokens (e.g., over all input contexts). . . . System 600 includes updating module 610, which receives as input reference user language model 606 and initial dynamic language model 608. Updating module 610 obtains dynamic language model 614, which includes a second overall probability distribution D′. For example, dynamic language model 614 may predict one or more tokens using an output probability distribution Y′ over an underlying token vocabulary given particular previous tokens W (e.g., a particular input context, such as a partial sentence). The output probability distribution Y′ is drawn from the second overall probability distribution D′, which represents all output probability distributions over all previous tokens (e.g., over all input contexts).” Bellegarda ¶195-196.) generating, using the multiple statistical descriptors, an output which is personalised to the user device, (“Text prediction can be implemented using a language model initially trained using a static training corpus including a very large amount of text samples. In order to better reflect individual user idiosyncrasies and other evolving linguistic events, the language model is then updated (e.g., adapted) using actual user data produced on a device implementing the language model.” Bellegarda ¶3.) wherein there are fewer statistical descriptors than embedded representations. (As explained in Bellegarda, the output probability distributions are only generated in response to words under evaluation by the model. See Bellegarda ¶¶195-196 above. However, each embedded representation, or token, corresponds to some part of a word in the language being evaluated by the model. See e.g. Bellegarda ¶188 (“each token includes (i.e., represents) one or more characters or one or more words (e.g., an individual character, a character sequence, a fragment of a word, a word, a fragment of a phrase, an entire phrase, a fragment of a sentence, an entire sentence, and the like), one or more phonemes (e.g., for speech recognition).”) Note that the model will have more total embedded tokens than tokens under evaluation using the probability distribution at a given point in time.)
5. The method of claim 1, wherein the pre-trained ML model includes a natural language processing ML model in the form of a tokenizer which generates embedded representations in the form of tokens from a text input. (“In some embodiments, the user training data set is parsed into tokens, which are basic processing units for predictive models, meaning that a predictive model, such as a language model, can accept previous tokens as input and predict one or more tokens based on the previous tokens. In some embodiments, each token includes (i.e., represents) one or more characters or one or more words (e.g., an individual character, a character sequence, a fragment of a word, a word, a fragment of a phrase, an entire phrase, a fragment of a sentence, an entire sentence, and the like), one or more phonemes (e.g., for speech recognition), or one or more spatial coordinates (e.g., for handwriting recognition). In some embodiments, the user training data set may be parsed into tokens representing sub-word fragments. For example, by parsing the user training data set into tokens representing sub-word fragments, a predictive model may effectively predict out-of-vocabulary (OOV) words built out of the predicted sub-word fragments, such as predicting the sub-word fragment “er” to complete the previous token sequence “superspread,” even if the emergent term “superspreader” is too new to be included in a particular underlying vocabulary or lexicon.” Bellegarda ¶188. As showing in the art cited in the rejection of claim 1, Arora teaches token embeddings. See Arora P. 514.)
6. The method of claim 5, further comprising generating a classification token as a first token in the distribution of embedded representations, and wherein outputting the multiple statistical descriptors includes outputting the classification token. (“Reference user language model 606 includes a first overall probability distribution D. For example, reference user language model 606 may predict one or more tokens using an output probability distribution Y over an underlying token vocabulary given particular previous tokens W (e.g., a particular input context, such as a partial sentence). The output probability distribution Y is drawn from the first overall probability distribution D, which represents all output probability distributions over all previous tokens (e.g., over all input contexts).” Bellegarda ¶195.)
7. The method of claim 1, further comprising adding a plurality of adapter modules to the pre-trained ML model to create a local ML model wherein each adapter module has a set of adapter parameters. (“Training module 602 trains a user language model 604 using the user training data set. . . . In some embodiments, the training module 602 periodically re-trains the user language model 604. For example, as additional data is added to the user training data set (e.g., as described above), training module 602 can re-train user language model 604 using the expanded data set.” Bellegarda ¶194.)
8. The method of claim 7, further comprising: receiving multiple training sets each comprising a plurality of training samples, wherein each training set is associated with a particular problem; personalising the local ML model using continual learning by selecting a training set; (“In some embodiments, the training module 602 periodically re-trains the user language model 604. For example, as additional data is added to the user training data set (e.g., as described above), training module 602 can re-train user language model 604 using the expanded data set.” Bellegarda ¶194. Each training data set is associated with the particular problem of tailoring a language model to the manner in which the user speaks.) fixing the set of basic parameters; (As best understood, “fixing the set of basic parameters” refers to updating the parameters of the model based on the new data. See Bellegarda ¶194 cited above. See also Spec. ¶6-7 (“The set of basic parameters may also be termed the set of original model parameters or the set of original model weights which are learned during the original training.”)) using the selected training set to learn a set of adapter parameters for one adapter module in the plurality of adapter modules; (“training module 602 can re-train user language model 604 using the expanded data set.” Bellegarda ¶194. The term “adapter parameters” reads on weights of the adapted model. This interpretation is based on the similarity between “adapter parameters” and “basic parameters” which are described as “original model weights.” See Spec. P7.) and iterating the selecting, fixing and using for each training set. (“the training module 602 periodically re-trains the user language model 604. For example, as additional data is added to the user training data set (e.g., as described above), training module 602 can re-train user language model 604 using the expanded data set.” Bellegarda ¶194. Retraining a model based on “added” data teaches selecting, fixing reads on updating weights of the model, which would be understood by one of ordinary skill as part of the training taught in Bellegarda. The claimed “using” the selected data to learn “adapter parameters” reads on updating parameters. See Bellegarda ¶194.)
For rejection of claims 9 and 13-16, see rejections of claims 1 and 5-8.
For rejection of claims 17, see rejections of claims 1.
Conclusion
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PAUL M. KNIGHT
/PAUL M KNIGHT/
Primary Examiner, Art Unit 2148
1 This distinction between claims which read on math and claims which recite an abstract idea is based on official USPTO Guidance. The 2019 Subject Matter Eligibility (SME) Examples instructs examiners that a claim reciting “training the neural network” where the background describes training as “using stochastic learning with backpropagation which is a type of machine learning algorithm that uses the gradient of a mathematical loss function to adjust the weights of the network” “does not recite any mathematical relationships, formulas, or calculations.” See 2019 SME Example 39, PP. 8-9 (emphasis added). In this example, the plain meaning of “training the neural network” read in light of the disclosure reads on backpropagation using the gradient of a mathematical loss function. See MPEP § 2111.01. In contrast, the 2024 SME Examples instructs examiners that a claim reciting “training, by the computer, the ANN . . . wherein the selected training algorithm includes a backpropagation algorithm and a gradient descent algorithm” does recite an abstract idea because “[t]he plain meaning of [backpropagation algorithm and gradient descent algorithm] are optimization algorithms, which compute neural network parameters using a series of mathematical calculations.” 2024 PEG Example 47, PP. 4-6. The Memorandum of August 4, 2025; Reminders on evaluating subject matter eligibility of claims under 35 U.S.C. 101, P. 3 also directs examiners that “training the neural network” recited in Example 39 merely “involve[s] . . . mathematical concepts” and contrasts claim 2 of example 47 as “referring to [specific] mathematical calculations by name[.]” (Emphasis added.)
2 “For instance, the claims in Diehr . . . clearly stated a mathematical equation . . . and the claims in Mayo . . . clearly stated laws of nature . . . such that the claims ‘set forth’ an identifiable judicial exception. Alternatively, the claims in Alice Corp. . . . described the concept of intermediated settlement without ever explicitly using the words ‘intermediated’ or ‘settlement.’” MPEP § 2106.04(II)(A).
3 “By grouping the abstract ideas, the examiners’ focus has been shifted from relying on individual cases to generally applying the wide body of case law spanning all technologies and claim types. . . . If the identified limitation(s) falls within at least one of the groupings of abstract ideas, it is reasonable to conclude that the claim recites an abstract idea in Step 2A Prong One.” MPEP § 2106.04(a). See also MPEP 2104(a)(2).
4 Step 2A prongs one and two are evaluated individually, consistent with the framework in the MPEP. Evaluation of relationships between abstract ideas and additional elements in one location promotes clarity of the record.
5 “In short, first the specification should be evaluated to determine if the disclosure provides sufficient details such that one of ordinary skill in the art would recognize the claimed invention as providing an improvement. The specification need not explicitly set forth the improvement, but it 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. Second, if the specification sets forth an improvement in technology, the claim must be evaluated to ensure that the claim itself reflects the disclosed improvement. That is, the claim includes the components or steps of the invention that provide the improvement described in the specification. . . . It should be noted that while this consideration is often referred to in an abbreviated manner as the ‘improvements consideration,’ the word ‘improvements’ in the context of this consideration is limited to improvements to the functioning of a computer or any other technology/technical field, whether in Step 2A Prong Two or in Step 2B.” MPEP 2106.04(d)(1). See also Koninklijke KPN N.V. v. Gemalto M2M GmbH, 942 F.3d 1143, 1150-1152 (Fed. Cir. 2019).
6 See MPEP § 2106.05(d)(II) listing operations including “receiving or transmitting data,” “storing and retrieving data in memory,” and “performing repetitive calculations” as WURC. “The claims at issue do not require any nonconventional computer, network, or display components, or even a non-conventional and non-generic arrangement of known, conventional pieces, but merely call for performance of the claimed information collection, analysis, and display functions on a set of generic computer components and display devices.” Elec. Power Grp., LLC v. Alstom S.A., 830 F.3d 1350, 1355 (Fed. Cir. 2016) (emphasis added, internal quotes omitted).
7 “But ‘[f]or the role of a computer in a computer-implemented invention to be deemed meaningful in the context of this analysis, it must involve more than performance of 'well-understood, routine, [and] conventional activities previously known to the industry.’ Content Extraction, 776 F.3d at 1347-48 (quoting Alice, 134 S. Ct at 2359). Here, the server simply receives data, ‘extract[s] classification information . . . from the received data,’ and ‘stor[es] the digital images . . . taking into consideration the classification information.’ See ‘295 patent, col. 10 ll. 1-17 (Claim 17). . . . These steps fall squarely within our precedent finding generic computer components insufficient to add an inventive concept to an otherwise abstract idea. Alice, 134 S. Ct. at 2360 (‘Nearly every computer will include a 'communications controller' and a 'data storage unit' capable of performing the basic calculation, storage, and transmission functions required by the method claims.’); Content Extraction, 776 F.3d at 1345, 1348 (‘storing information’ into memory, and using a computer to ‘translate the shapes on a physical page into typeface characters,’ insufficient confer patent eligibility); Mortg. Grader, 811 F.3d at 1324-25 (generic computer components such as an ‘interface,’ ‘network,’ and ‘database,’ fail to satisfy the inventive concept requirement); Intellectual Ventures I, 792 F.3d at 1368 (a ‘database’ and ‘a communication medium’ ‘are all generic computer elements’); BuySAFE v. Google, Inc., 765 F.3d 1350, 1355 (Fed. Cir. 2014) (‘That a computer receives and sends the information over a network—with no further specification—is not even arguably inventive.’).” TLI Commc'ns LLC v. AV Auto., LLC, 823 F.3d 607, 614 (Fed. Cir. 2016), Emphasis Added.
8 “The analysis as to whether an element (or combination of elements) is widely prevalent or in common use is the same as the analysis under 35 U.S.C. 112(a) as to whether an element is so well-known that it need not be described in detail in the patent specification. See Genetic Techs. Ltd. v. Merial LLC, 818 F.3d 1369, 1377, 118 USPQ2d 1541, 1546 (Fed. Cir. 2016) (supporting the position that amplification was well-understood, routine, conventional for purposes of subject matter eligibility by observing that the patentee expressly argued during prosecution of the application that amplification was a technique readily practiced by those skilled in the art to overcome the rejection of the claim under 35 U.S.C. 112, first paragraph)[.]” MPEP § 2106.05(d)(I).
9 “Similarly, claim elements or combinations of claim elements that are routine, conventional or well-understood cannot transform the claims. (Citing BSG Tech LLC v. BuySeasons, Inc., 899 F.3d 1281, 1290-1291 (Fed. Cir. 2018)). When the patent's specification ‘describes the components and features listed in the claims generically,’ it ‘support[s] the conclusion that these components and features are conventional.’ Weisner v. Google LLC, 51 F.4th 1073, 1083-84 (Fed. Cir. 2022); see also Beteiro, LLC v. DraftKings Inc., 104 F.4th 1350, 1357-58 (Fed. Cir. 2024).” Broadband iTV, Inc. v. Amazon.com, Inc., 113 F.4th 1359 (Fed. Cir. 2024)
10 “If it is asserted that the invention improves upon conventional functioning of a computer, or upon conventional technology or technological processes, a technical explanation as to how to implement the invention should be present in the specification. That is, the disclosure must provide sufficient details such that one of ordinary skill in the art would recognize the claimed invention as providing an improvement. The specification need not explicitly set forth the improvement, but it 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.” MPEP § 2106.05(a).
