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. If Applicant is aware of any related domestic or foreign applications citing prior art, those office actions and copies of associated prior art are hereby requested.
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).
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, 3-8, and 10-22 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 computer system for generating training data used for training a model configured to perform a logical inference, the computer system comprising: (This merely recites generic computer components used for implementing the mental process of logical inference.) at least one computer, wherein the computer system holds argument data representing an argument that leads to a conclusion proposition from a plurality of premise propositions, each of the conclusion proposition and the premise propositions is stored as a logical expression, (This reads on using generic computer components for implementing the mental process of logical inference. Storing data is extra-solution activity. It is not clear now storing a propositions “as a logical expression.” Arranging the propositions as a “logical expression” reads on a mental process.) and the at least one computer (The “at least one computer” is recited as carrying out various abstract mental/mathematical processes below. Using a generic computer to implement abstract mental/mathematical processes is a mere instruction to apply an exception using a computer. Use of “the at least one computer” to implement various abstract ideas is repeated in the dependent claims as well. In the interest of brevity this determination is not repeated, but it should be understood that generic use of a computer to implement abstract mental/mathematical is determined to be a mere instruction to apply an exception in all cases below in this claim set.) generates, using the argument data, a proof tree that is tree structure data whose leaf node is the premise proposition and root node is the conclusion proposition, (This reads on a mental/mathematical process.) searches for, using the proof tree, the argument data whose conclusion proposition is a premise proposition of the plurality of premise propositions of the argument data or the argument data whose premise proposition is the conclusion proposition of the argument data generates proof data by combining a plurality of the proof trees based on a result of the search, (This reads on a mental/mathematical process.) the proof data representing a proof tree structure that leads to the conclusion proposition by repeating the argument a plurality of times converts the proof data into a text expressed as a language expression, (This reads on mathematical/mental process. The expression of the text by a computer reads on mere data output using generic computing components. Merely outputting the data is extra-solution activity.) and generates the training data by outputting sentences assigned to notes of the proof tree. (Generating sentences “assigned to nodes of the proof tree” reads on a mental/mathematical process.)
Independent claim 8 is rejected for the reasons given in the rejection of claim 1.
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 independent claims recite “the computer system holds . . . each of the conclusion proposition and the premise propositions is stored as a logical expression[.]” Both holding and storing, as recited, read on mere data storage, 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:
3. The computer system according to claim 1, wherein the logical expression is described using a variable representing a proposition, or the variable and a logical symbol, (This reads on a mental/mathematical process. To the extent that “describe[ing]” is interpreted as referring to data display and output, this is directed to extra solution activity which is also WURC. Support for the finding that this is WURC is found in the corresponding paragraph above.) the computer system manages a template in which a logical expression including the logical symbol is associated with a sentence, (This reads on a mental/mathematical process.) and the at least one computer: for a logical expression of the proof data that includes only the variable, outputs the variable as a sentence, (Data display and output this is directed to extra solution activity which is also WURC. Support for the finding that this is WURC is found in the corresponding paragraph above.) converts the logical expression including the logical symbol of the proof data into a sentence using the template, (This reads on a mental/mathematical process.) converts the variable included in the sentence obtained by converting the logical expression into a character string, (This reads on a mental/mathematical process.) and generates the text including sentences obtained by converting a plurality of the logical expressions included in the proof data. (This reads on a mental/mathematical process.)
4. The computer system according to claim 1, wherein the at least one computer receives or determines a generation condition of the proof data from a user, (Data input/output this is extra solution activity which is also WURC. Support for the finding that this is WURC is found in the corresponding paragraph above.) and generates the proof data based on the generation condition of the proof data. (This reads on a mental/mathematical process.)
5. The computer system according to claim 4, wherein the at least one computer receives information related to a resource amount of the computer system from the user, (Data input/output and display are directed to extra solution activity which is also WURC. Support for the finding that this is WURC is found in the corresponding paragraph above.) determines the generation condition of the proof data based on the resource amount of the computer system, wherein the generation condition includes at least one of an upper limit value of a depth of the proof tree or an upper limit value of leaf nodes included in the proof tree, (This reads on a mental/mathematical process. The upper limit values merely limit the mental process.) and presents the determined generation condition of the proof data. (Data input/output and display are directed to extra solution activity which is also WURC. Support for the finding that this is WURC is found in the corresponding paragraph above.)
6. The computer system according to claim 4, wherein the at least one computer receives, from the user, information related to data handled by the model trained using the training data, (Data input/output and display are directed to extra solution activity which is also WURC. Support for the finding that this is WURC is found in the corresponding paragraph above.) analyzes the data handled by the model trained using the training data, determines the generation condition of the proof data based on a result of the analysis, wherein the generation condition includes at least one of an upper limit value of a depth of the proof tree or an upper limit value of leaf nodes included in the proof tree, (This reads on a mental/mathematical process. The upper limit values merely limit the mental process.) and presents the determined generation condition of the proof data. (Data input/output and display are directed to extra solution activity which is also WURC. Support for the finding that this is WURC is found in the corresponding paragraph above.)
7. The computer system according to claim 1, wherein at least one of training processing of generating the model configured to execute any task using the training data and task execution processing using the model is executed. (This reads on generic “task execution processing” using a model. Executing a task using a model reads merely utilizing generic computer components (i.e. generic models) in their ordinary capacity with an instruction to implement the above recited abstract mental/mathematical processes.)
For rejections of claims 9-14, see rejections of claims 2-7.
15. (New) The computer system according to claim 1, wherein the at least one computer assigns an unprocessed flag to each proof tree generated from the argument data, registers the proof tree in a proof tree list, and upon completing a search for connectable proof trees, assigns a processed flag to the proof tree. (Assigning a flag to trees in a list reads implementing a mental process using generic computer components.)
16. (New) The computer system according to claim 1, wherein the at least one computer searches for a connectable proof tree by at least one of: searching for a proof tree whose leaf node is a root node of a target proof tree; or searching for a proof tree whose root node is a leaf node of the target proof tree. (The claimed “searching for” a particular proof tree reads on a mental process, using generic computing components.)
17. (New) The computer system according to claim 1, wherein the computer system stores a translation template database that stores entries including a template ID, a logical expression including logical symbols, and a translated sentence in which the logical expression is expressed in a language. (This reads on merely storing data, which is extra-solution activity and WURC.)
18. (New) The computer system according to claim 1, wherein the at least one computer converts proposition variables included in sentences of the proof data into character strings using a dictionary stored in the computer system. (Using a “dictionary” to convert proposition variables into “character strings” reads on a mental process, implemented using generic computer components.)
19. (New) The computer system according to claim 1, wherein the at least one computer deletes, from a proof tree list, a proof tree whose number of argument steps is 1. (Deleting a proof tree from a list reads on a mental process, implemented using conventional computer components.)
20. (New) The computer system according to claim 1, wherein the at least one computer accesses an external database to check whether contents of a sentence are correct, and when the contents of the sentence are not correct, controls not to output the sentence as a sentence to be included in the training data. (Verifying whether the contents of a sentence are correct reads on a mental process. The mere sending of data back and forth is extra-solution activity and WURC.)
21. (New) The computer system according to claim 1, wherein the model is a neural network. (This merely recites application of the mental process using conventional computing components.)
22. (New) The computer system according to claim 1, wherein the argument data is described using an inference rule for leading to the conclusion proposition from the premise proposition. (This merely limits the mental process.)
All dependent claims are rejected as containing the material of the claims from which they depend.
Claim Interpretation
The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph:
An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
None of the limitation in this claims set are interpreted as means plus function claims or step plus function claims, notwithstanding the use of “step” before reciting various operations in claims 8 and 10-14. First, while language reciting “means” followed by functional language generally triggers a presumption that language should be construed under § 112f, the term “step for” (not “step” alone) followed by functional language triggers the presumption with respect to step-plus-function claims. See MPEP § 2181. (“Examiners will apply 35 U.S.C. 112(f) to a claim limitation that uses the term "means" or generic placeholder associated with functional language, unless that term is (1) preceded by a structural modifier, defined in the specification as a particular structure or known by one skilled in the art, that denotes the type of structural device (e.g., "filters"), or (2) otherwise modified by sufficient structure or material for achieving the claimed function. Similarly, examiners will apply 35 U.S.C. 112(f) to a claim limitation that uses the term "step for" unless that term is modified by sufficient acts for performing the claimed function.”) Second, the operations recited after the “steps” of claim 8 are not “purely functional language.” See MPEP § 2181. Specifically, “a first step of searching for” characterizes the operation of “searching” as a step. (Note here that “step of searching for” does not recite “step for searching for” so this does not recite a “step for” but properly pairs the correct preposition (“for”) with the verb “search” in describing the target of a search.) Third, searching is within the capabilities of a generic computer. Merely referencing a generic computer provides sufficient structure for implementing the operation of searching. Therefore, even if the presumption were triggered, the claims recite operations and not functional language, and there is sufficient structure for carrying out those operations. For the foregoing reasons, no claim language is interpreted under § 112(f). If Applicant intended to invoke §112f, using “means” or “step for” without structural modifiers may have this effect. (This is not a suggestion.) In the interest of compact prosecution, it is suggested, that any amendments avoid the use of “step” or “means” unless the claims are meant to be construed under § 112(f).
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 1, 3-8, and 10-22 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.
This set of claims uses multiple similar sounding terms, or terms which are described similarly in the Specification. This is confusing because the use of different terms implies distinct claim elements in domestic claim interpretation, so it is not clear whether very similar terms or terms which are described in similar ways in the Specification are meant to refer to the same claim element, or to different claim elements. Examiner has attempted to identify all such terms. Note that any terms determined to be indefinite in the independent claims must also be amended in the dependent claims, to overcome the following rejections.
Claims 1 and 8 substantially recite “each of the conclusion proposition and the premise propositions is stored as a logical expression[.]” It is not clear whether the “logical expression” refers to the combination of a premise and a conclusion, or if each of the premise and conclusion, individually is “stored as a logical expression[.]” While the former makes more sense in the context of the invention, the latter seems more reasonable based on the claim language (“each . . . is stored as a logical expression[.]”)
Claims 1 and 8 substantially recite: “the at least one computer using the argument data, a proof tree that is tree structure data whose leaf node is the premise proposition and root node is the conclusion proposition, searches for, using the proof tree, the argument data whose conclusion proposition is a premise proposition of the plurality of premise propositions of the argument data or the argument data whose premise proposition is the conclusion proposition of the argument data[.]” Based on this claim language, the computer “using the argument data . . . searches for . . . the argument data[.]” It is not clear what operations are required by use of argument data to search for itself. Further, “using the proof tree” is recited twice in relation to the search. It is not clear if the proof tree is used in more than one way, used twice, if the search tree is used only once but the importance of the tree warranted the double inclusion of this term, or if this is merely a drafting error. In any case, the scope of this limitation is unclear.
Claims 1 and 8 substantially recite “the at least one computer . . . converts the proof data into a text expressed as a language expression, and generates the training data by outputting sentences assigned to nodes of the proof tree.” It is not clear whether the “proof data” the “text expressed as a language expression” the “training data” or the “sentences” are structurally different from one another. Simply put, “proof data” and “training data” can refer to “text expressed as a language expression” or “sentences” so there is no clear distinction between the terms. The Specification mentions “proof data” only once. See Spec. p. 5. No other explanation or mention of the claimed “proof data” is found in the specification. The “training data” is also not defined in the Specification. Further, the Specification explains that “[t]he text output unit 132 generates the training data based on the text of the target proof tree (step S402).” In other words, a unit for outputting text generates training data, implying that the training data is the text expressed as a language expression.” The proof data could refer to the proof tree, but the Specification appears to describe using the proof tree to create training data, not converting the proof tree into training data. In any case, reference to the Specification tends to support the determination that this language is indefinite.
Claims 1 and 8 substantially recite “generates proof data by combining a plurality of proof trees based on a result of the search, the proof data representing a proof tree structure that leads to the conclusion proposition by repeating the argument a plurality of times[.]”First, it is not clear whether or not the “proof data” refers to a group of proof trees, or if this refers to some other data that is created by a group of trees. Second, it is not clear what is meant by “repeating the argument a plurality of times” in the context of the proof tree. This could refer to a multi-step argument, consistent with Fig. 17, or could refer to merely repeating the same arguments and asserting “proof.” The former is more consistent with the description and the latter sounds implausible, but the claims explicitly recite “generat[ing] proof . . . by repeating the argument.” While potentially persuasive to the human mind, it is not clear how “repeating the argument a plurality of times” would logically lead to “generating proof data” in the realm of machine learning. Since it is not clear which is meant, the claim language is indefinite.
Claims 1 and 8 substantially recite “the computer holds . . . a conclusion/premise proposition(s) . . . each of the conclusion proposition and the premise propositions is stored as a logical expression [.]” Whether or not the proposition is stored as a logical expression is subjective. Further, in the context of data held within a computer it is not clear what is meant by storing a proposition as a logical expression. Generally, data is stored using various types of media that dictate the way the data is stored. The language here purports to require a specific way in which the computer holds the data, without actually indicating how the data is stored. It is not clear whether or not some change to the propositions is required for them to be stored as logical expressions in a computer. There is simply no clear way to give this language weight, rendering the language subjective and therefore indefinite. See MPEP § 2173.05b.
Claims 1 and 8 substantially recite “a proof tree that is tree structure data whose leaf node is the premise proposition . . . the argument data whose conclusion proposition is a premise proposition of the plurality of premise propositions [.],” The languge “the premise proposition” has no antecedent basis. It is not clear whether “the premise proposition” is part of “a plurality of premise propositions” or if this is a separate premise proposition. Further, “a premise proposition” implies a new premise proposition, but could reasonably also refer back to the earlier recited “the premise proposition.” Since it is not clear how many premise propositions are being recited or how they must relate to one another, the claim language is indefinite. Similarly, “the premise propositions” appears to refer back to “a plurality of premise propositions” based on context. But the terms are different, indicating they refer to different claim elemetns (i.e. different groups of premise propositions.) It is suggested that, consistent with basic domestic claim interpretation rules, the same term be used if referencing the same claim elements, or clearly different term (i.e. first/second premise) be used if referencing different claim elements.
Claims 3 and 10 substantially recite “the at least one computer . . . outputs the variable as a sentence [.]” It is not clear if the output “a variable” or “a sentence.” The two seem inconsistent and it is not clear which of the two is meant. The following limitation contributes to this uncertainty by reciting converting “the logical expression” into “a sentence.” (“converts the logical expression including the logical symbol of the proof data into a sentence using the template[.]”) Presumably, the two recitations of “a sentence” refer to different outputs, though that is also unclear. More importantly, it appears that both the “outputs of the variable” and “the logical expression” are converted into either one sentence or into two separate sentences but there is no way to objectively determine which is meant. These claims also recite “converts the variable included in the sentence obtained by converting the logical expression into a character string, and generates the text including sentences obtained by converting a plurality of the logical expressions included in the proof data.” This language also appears to contribute to creation of the sentence, but it is not clear how. The language “converts the variable included in the sentence obtained by converting the logical expression into a character string” appears to recite conversion of something within a sentence into a character string. Since a sentence is already a character string, the claimed conversion appears to be non-sensical. Given the lack of any plausible meaning for this language, it is indefinite under the statute. The generation step, as best understood generates (text of) the sentence recited in the earlier limitations, by converting logical expressions. While this alone is clear, it seems redundant with the previously claimed subject matter making it unclear whether some other operation leads to generation of the sentence or if this merely reiterates that a sentence is created from logical expressions.
Claims 4 and 11 substantially recite substantially recite “the at least one computer receives or determines a generation condition of the proof data from a user[.]” Claims 5 and 12 substantially recite a “computer system according to claim 4, wherein the at least one computer . . . determines the generation condition of the proof data based on the resource amount of the computer system.” Claims 6 and 14 substantially recite “determines the generation condition of the proof data based on a result of the analysis[.]” It is not clear whether the computer receives the generation condition of the proof data, or if the computer determines the generation condition of the proof data. Note that the language “determines a generation condition . . . from a user” is itself ambiguous because this must be distinguished from receiving a generation condition, but it is not clear how the scope of determining from a user different from the scope of receiving from a user. Since claims 5 and 6 are within the scope of claim 4, but seem to be inconsistent with claim 4, claims 4-6 (and claims 11-13) are indefinite.
Claim 15 recites “The computer system according to claim 1, wherein the at least one computer assigns an unprocessed flag to each proof tree generated from the argument data, registers the proof tree in a proof tree list, and upon completing a search for connectable proof trees, assigns a processed flag to the proof tree.” It is not clear whether the “completing a search for connectable proof trees” must involve “the proof tree” that is assigned the processed flag. The assignment of a “processed flag” to “the proof tree” implies that the proof tree was processed. But the claim only requires assignment of the flag “upon completing a search for connectable proof trees.” Further, it is somewhat unclear whether all proof trees in the search are assigned a flag, or if the flag is only assigned to one of the proof trees.
Claim 18 recites: “The computer system according to claim 1, wherein the at least one computer converts proposition variables included in sentences of the proof data into character strings using a dictionary stored in the computer system.” The Specification only explains the claimed “dictionary” once in the Specification. “The translation unit 131 converts the proposition variables included in the sentence into character strings using the dictionary 122 (step S303). For example, when the sentence is "when x is A, it is B", the translation unit 131 converts "x", "A", and "B" into character strings, and generates a sentence "if the apple is red, it is delicious". A plurality of sentences having different meanings may be generated from one sentence.” The form of the “dictionary” is unclear from this description. Generally, a dictionary is a reference that associates words with their meanings. It is not clear how the claimed operations would relate to a dictionary, as that term is ordinarily used and the Specification does not provide any alternative meaning for the term that would allow one of ordinary skill in the art to ascertain the objective meaning of the term.
Claim 20 recites “wherein the at least one computer accesses an external database to check whether contents of a sentence are correct, and when the contents of the sentence are not correct, controls not to output the sentence as a sentence to be included in the training data.” There is no objective meaning for “correct.” See MPEP § 2173.05(b). This could refer to a sentence that follows logical rules, is limited to factually correct statements, is grammatically or stylistically “correct.” Without any measure, there is no way to know which of these, for example, is meant.
All dependent claims are rejected as containing the limitations 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, 3-4, 7-8, 10-11, 14 16-19, and 21-22 are rejected under 35 U.S.C. 103 as being unpatentable over Betz (Critical Thinking for Language Models, 2020) and Tfjord (ProofWriter: Generating Implications, Proofs, and Abductive Statements over Natural Language 2021).
1. A computer system for generating training data used for training a model configured to perform a logical inference, the computer system comprising: at least one computer, (“This section describes the construction of a synthetic corpus of natural language arguments used for training and evaluating GPT-2.” Betz p. 4. “1The corpus as well as the source code used to generate it will be released at https://github.com/debatelab/aacorpus.” Betz p. 4, footnote 1. Note that the release of source code would be understood by one of ordinary skill as running on a generic computer including a processor, memory, and storage. “[I]n considering the disclosure of a reference, it is proper to take into account not only specific teachings of the reference but also the inferences which one skilled in the art would reasonably be expected to draw therefrom.” MPEP § 2144.01. See also In re Preda, 401 F.2d 825, 826 (C.C.P.A. 1968), applying this reasoning in the context of anticipation.) wherein the computer system holds argument data representing an argument that leads to a conclusion proposition from a plurality of premise propositions, each of the conclusion proposition and the premise propositions is stored as a logical expression, (“The corpus is built around eight simple, deductively valid syllogistic argument schemes (top row in Figure 1.)” Betz p. 4. See also Betz Fig. 1 showing, for instance the inclusion of generalized modus ponens. Note further, the inclusion of hypothetical syllogism 1, showing the use of a plurality of premise propositions (Fx[Wingdings font/0xE0]Gx and Gx[Wingdings font/0xE0]Hx) leading to a conclusion (F[Wingdings font/0xE0]Hx).) and the at least one computer: generates, using the argument data, a proof tree that is tree structure data whose leaf node is the premise proposition and root node is the conclusion proposition, searches for, using the proof tree, the argument data whose conclusion proposition is a premise proposition of the plurality of premise propositions of the argument data or the argument data whose premise proposition is the conclusion proposition of the argument data generates proof data by combining a plurality of the proof trees based on a result of the search, the proof data representing a proof tree structure that leads to the conclusion proposition by repeating the argument a plurality of times, (“In step 1, the argument scheme, which serves as formal template for the natural language argument, is chosen. In step 2, each sentence in the formal scheme (premises and conclusion) is individually replaced by a natural language pattern in accordance with a randomly chosen template. For example, the formula “for all x, Fx [Wingdings font/0xE0] Gx” might be replaced by any of the following natural language sentence schemes: • “Every F is a G.” • “Whoever is a F is also a G.” • “Being a G is necessary for being a F.” • “If someone is a F, then they are a G.”* Some of these patterns are not used for training, but are reserved for generating an out-of-domain test dataset (e.g., the template marked with an asterisk in the above list).” Betz p. 4-5. See also Betz Figure 2, noting that the green box associated with the arrow leaving step 2 shows the “argument data” as limited by the claim language.
The previously cited art does not explicitly teach the tree structure claimed above.
Tfjord teaches “Facts in the proof are one of three types: known facts fi 2 F, negated facts fnaf that cannot be proven (false under negation-as-failure (NAF)), and facts fconc that are the conclusions of rules. fi and fnaf are leaf nodes of the proof, while the fconc are intermediate nodes within the proof.” Tfjord p. 3. Note that fconc refers to conclusions of rules (i.e. the outcome of applying rules to facts (premises).) See also Tfjord Fig. 2 showing conclusions as root nodes (i.e. the tree flows from facts/premises to conclusions. Note that Tfjord Fig. 2 shows a plurality of trees combined into n-hop proofs. “Figure 2: ProofWriter iteratively generates 1-step implications and their proofs, and adds implications back into [] the context for deeper reasoning. The stepwise proof fragments are assembled into full proofs of N-hop conclusions.” Tfjord Fig. 2.))
It would have been obvious to one of ordinary skill in the art before the effective filing date to combine the teaching of Tfjord because this is part of a method that improves the ability of a language model to generate logical explanations consistent with their outputs. See Tafjord p. 1. (“In particular, iterating a 1-step implication generator results in proofs that are highly reliable, and represent actual model decisions (rather than post-hoc rationalizations).”))) converts the proof data into a text expressed as a language expression, (In step 3, the entity- and property-placeholders in the resulting argument scheme are replaced argument-wise with names and predicates from a domain. We hence obtain an instance of the formal argument scheme as premise-conclusion list. Each domain provides hundreds of entity-names, which can be paired with different binary predicates to create thousands of different unary predicates.” Betz p. 5. See also Betz Fig. 2.) and generates the training data by outputting sentences assigned to nodes of the proof tree. (“In step 4, the premises of the natural language argument are randomly re-ordered. In step 5, the premise-conclusion list is packed into a text paragraph by adding an argument intro, framing the premises, and adding an inference indicator.” Betz p. 5. See also Betz Fig. 2. “Following this pipeline, we generate natural language instances of each formal argument scheme, thus creating: 1. a training set of argumentative texts, based on the default domains and templates (TRAIN)[.]” Betz p. 6.)
3. The computer system according to claim 1, wherein the logical expression is described using a variable representing a proposition, or the variable and a logical symbol, the computer system manages a template in which a logical expression including the logical symbol is associated with a sentence, (“Natural language instances of the argument schemes can be created by means of a first-order logic domain (with names and predicates) and natural language templates for the formal schemes. In order to obtain a large variety of realistic natural language arguments, we have devised • a multi-stage templating process with • alternative templates at each stage[.]” Betz p. 4.) and the at least one computer outputs: for a logical expression of the proof data that includes only the variable, outputs the variable as a sentence, (See Betz Fig 2, output of Step 3. See also rejection of claim 1 citing step 3 of the technique of Betz.) converts the logical expression including the logical symbol of the proof data into a sentence using the template, (See Betz Fig 2, output of Step 3. See also rejection of claim 1 citing step 3 of the technique of Betz.) converts the variable included in the sentence obtained by converting the logical expression into a character string, (See Betz Fig 2, output of Step 3. See also rejection of claim 1 citing step 3 of the technique of Betz.) and generates the text including sentences obtained by converting a plurality of the logical expressions included in the proof data. (See Betz Fig 2, output of Step 3 and description of step 3 on page 5. See also rejection of claim 1 citing step 3 of the technique of Betz. It is noted that all four of these limitations appear to be directed to creating text sentences from logical relationships. As best understood, these limitations recite use of “proof data” (i.e. the inputs/arguments/outputs) to generate text sentences, do the same using a template, do the same while emphasizing the use of a “variable” and “text” within the sentence, and to the same emphasizing “logical expressions (arguments) within the text data. All of this is addressed in the rejection of claim 1.
4. The computer system according to claim 1, wherein the at least one computer receives or determines a generation condition of the proof data from a user, and generates the proof data based on the generation condition of the proof data. ((“In step 3, the entity- and property-placeholders in the resulting argument scheme are replaced argument-wise with names and predicates from a domain. We hence obtain an instance of the formal argument scheme as premise-conclusion list. Each domain provides hundreds of entity-names, which can be paired with different binary predicates to create thousands of different unary predicates. The following example predicates illustrate the domains used in this study: Female Relatives . . . Male Relatives . . . Football Fans . . .” Betz p. 5.)
7. The computer system according to claim 1, wherein at least one of training processing of generating the model configured to execute any task using the training data and task execution processing using the model is executed. (See Betz, Section 4.2)
For rejection of claim 8, see rejection of claim 1.
For rejections of claims 10-11 and 14, see rejections of claims 3-4 and 7, respectively.
16. (New) The computer system according to claim 1, wherein the at least one computer searches for a connectable proof tree by at least one of: searching for a proof tree whose leaf node is a root node of a target proof tree; or searching for a proof tree whose root node is a leaf node of the target proof tree. (See Tafjord Fig. 2.)
17. (New) The computer system according to claim 1, wherein the computer system stores a translation template database that stores entries including a template ID, a logical expression including logical symbols, and a translated sentence in which the logical expression is expressed in a language. (“Natural language instances of the argument schemes can be created by means of a first-order logic domain (with names and predicates) and natural language templates for the formal schemes.” Betz p. 4. See also Betz Fig. 1 showing a template of various translations including labels (“template I.D.”) and logical symbols.)
18. (New) The computer system according to claim 1, wherein the at least one computer converts proposition variables included in sentences of the proof data into character strings using a dictionary stored in the computer system. (Betz teaches “In step 2, each sentence in the formal scheme (premises and conclusion) is individually replaced by a natural language pattern in accordance with a randomly chosen template. For example, the formula “8xFx ! Gx” might be replaced by any of the following natural language sentence schemes: • “Every F is a G.” • “Whoever is a F is also a G.” • “Being a G is necessary for being a F.” • “If someone is a F, then they are a G.”*” Betz p. 4. Note that the Specification does not use “dictionary” consistent with its conventional meaning, while failing to offer any alternative meaning for the term.)
19. (New) The computer system according to claim 1, wherein the at least one computer deletes, from a proof tree list, a proof tree whose number of argument steps is 1. (“Each dataset contains questions whose answers require reasoning up to depths D (D = 0, 1, 2, 3, 5). Thus, for example, all questions in D0 are lookup questions, requiring no inference.” Tafjord pp. 5-6. Tafjord does not expressly teach that a proof tree with 1 argument step is deleted. It would have been obvious to one of ordinary skill in the art before the effective filing date to delete a one step tree as an instance of deletion of an unnecessary structure. See MPEP § 2144.04. Note that the reference teaches that a lookup table is used in such a case, because no inference, and by implication not tree, is required.)
21. (New) The computer system according to claim 1, wherein the model is a neural network. (Betz teaches “This paper takes a first step towards a critical thinking curriculum for neural autoregressive language models.” Betz Abstract. Note that Tafjord also teach a neural network. See e.g. Tafjort Abstract.)
22. (New) The computer system according to claim 1, wherein the argument data is described using an inference rule for leading to the conclusion proposition from the premise proposition. (See Betz figs. 1 and 2.)
Claims 5-6 and 12-13 are rejected under 35 U.S.C. 103 as being unpatentable over Betz, Tfjord, and Yang (US 2022/0269835)
5. The computer system according to claim 4, wherein the at least one computer receives information related to a resource amount of the computer system from the user, (“We hence obtain an instance of the formal argument scheme as premise-conclusion list. Each domain provides hundreds of entity-names, which can be paired with different binary predicates to create thousands of different unary predicates. The following example predicates illustrate the domains used in this study: Female Relatives . . . Male Relatives . . . Football Fans . . .” Note that the number of domains is “related to resource amount” because more domains may require more training data.) determines the generation condition of the proof data based on the resource amount of the computer system, wherein the generation condition includes at least one of an upper limit value of a depth of the proof tree or an upper limit value of leaf nodes included in the proof tree, and presents the determined generation condition of the proof data. (“We train and evaluate three compact versions of GPT-2 with 117M, 345M and 762M parameters respectively using the implementation from Wolf et al. (2019). We note that all of these models fall short of the full-scale model with 1542M parameters.” Betz Section 4. While this implies consideration of resources when choosing the size of a model, this is not expressly taught in the previously cited art. Tafjord teaches limiting trees to a depth of 5. See Tafjord pp. 5-6. (“Each dataset contains questions whose answers require reasoning up to depths D (D = 0, 1, 2, 3, 5). Thus, for example, all questions in D0 are lookup questions, requiring no inference.”) While one of ordinary skill in the art would understand that limiting the depth of the tree would have the effect of reducing resource requirements, the combination of references does not clearly suggest determining the depth of the proof tree (i.e. determining the size of some aspect of the model) based on a resource amount of the system.
Yang teaches application of various resource considerations when selecting a particular model configuration: “[0038] A performance metrics set as used herein may be a set of metrics used to evaluate the performance of different machine learning models that may be chosen by a user (e.g., model accuracy, power consumption, latency, memory size, and the like).” Yang ¶38. “At 710: the user 410 may choose on the user interface 610 targeted objective (e.g., accuracy, latency, cost, memory, and the like) that should be prioritized when executing on a hardware platform.” Yang ¶72. “At 730: the user 410 may select candidate machine learning models the user 410 wants to deploy according to the user 410 input data including the targeted objective.” Yang ¶74. See also Fig. 7 showing step 730 to include “Search by Hardware” as a parameter for selecting the machine learning model. “At 760: the resource prediction twin 420 may compare each of the selected candidate machine learning models according to the targeted objective. Further, the resource prediction twin 420 may suggest a machine learning model that was optimally designed to achieve the targeted objective under the constraints without running each of the selected candidate machine learning models with the input data.” Yang ¶76.
It would have been obvious to one of ordinary skill in the art before the effective filing date to combine the teaching of Yang because customizing a model based on resources allows customization of models for a given scenario, thereby reducing either training time or resources and potentially improving perceived user experience.)
6. The computer system according to claim 4, wherein the at least one computer receives, from the user, information related to data handled by the model trained using the training data, analyzes the data handled by the model trained using the training data, determines the generation condition of the proof data based on a result of the analysis, wherein the generation condition includes at least one of an upper limit value of a depth of the proof tree or an upper limit value of leaf nodes included in the proof tree, and presents the determined generation condition of the proof data. (See rejection of claim 5.)
For rejections of claims 12-13, see rejections of claims 5-6.
Claim 15 is rejected under 35 U.S.C. 103 as being unpatentable over Betz, Tfjord, and Powell (US 2007/002424).
15. (New) The computer system according to claim 1, wherein the at least one computer assigns an unprocessed flag to each proof tree generated from the argument data, registers the proof tree in a proof tree list, and upon completing a search for connectable proof trees, assigns a processed flag to the proof tree. (Tafjord teaches “Iterative: We first train a model to generate a single 1-step implication (theory [Wingdings font/0xE0] implication + 1-step-proof), where the implication follows from a single rule application. Then at test time, we apply this model iteratively, adding each implication to the theory and repeating until no more implications can be found (i.e., exhaustive forward-chaining). The proof for any given implication can then be assembled from the 1-step-proof fragments (Figure 2).” Tafjord section 3.6. See also Tafjord section D.2
The previously cited art does not expressly assigning a processed flag to a tree.
Powell teaches “The confirmed read flag 210 indicates whether the tag has previously been read. If the confirmed read flag 210 has been set (i.e., indicating that the tag has already been read), tag 102 transitions to dormant state 302. If the confirmed read flag is not set, tag 102 transitions to tree traversal state 312.” Powell ¶49.
It would have been obvious to one of ordinary skill in the art before the effective filing date to combine the teaching of Powell because this helps the system keep track of what has been read, and thereby to avoid duplicating work.)
Claim 20 is rejected under 35 U.S.C. 103 as being unpatentable over Betz, Tfjord, and Chorev (A Practical Guide to Data Cleaning, 2021).
20. (New) The computer system according to claim 1, wherein the at least one computer accesses an external database to check whether contents of a sentence are correct, and when the contents of the sentence are not correct, controls not to output the sentence as a sentence to be included in the training data. (“This section describes the construction of a synthetic corpus of natural language arguments used for training and evaluating GPT-2.1 The corpus is built around eight simple, deductively valid syllogistic argument schemes (top row in Figure 1). These base schemes have been chosen because of their logical simplicity as well as their relevance in critical thinking and argument analysis (Feldman, 2014; Bowell and Kemp, 2014; Brun and Betz, 2016). Each of these eight base schemes is manually varied in specific ways to create further valid variants.” Betz p. 4. “Natural language instances of the argument schemes can be created by means of a first-orderlogic domain (with names and predicates) and natural language templates for the formal schemes.” Betz p. 4. “In step 2, each sentence in the formal scheme (premises and conclusion) is individually replaced by a natural language pattern in accordance with a randomly chosen template. For example, the formula “8xFx ! Gx” might be replaced by any of the following natural language sentence schemes: • “Every F is a G.” • “Whoever is a F is also a G.” • “Being a G is necessary for being a F.” • “If someone is a F, then they are a G.”* Some of these patterns are not used for training, but are reserved for generating an out-of-domain test dataset (e.g., the template marked with an asterisk in the above list).” Betz p. 5.
Betz does not explicitly teach that incorrect sentences are removed from the dataset.
Removing bad data is well known in machine learning. As explained in Chorev “Outlier detection is somewhat complex. It requires a deeper understanding of what the data should look like, and when entries should be ignored because they are inaccurate. Imagine you have a real estate dataset and an extra digit was added to the price of a property. While this kind of error is very easy to make, it can greatly and negatively affect the model’s learning ability. The first measure in detecting unwanted outliers is to explore the ranges and possibilities for numerical and categorical data entries, like a negative number as the price of a car is definitely an unwanted outlier. Additionally, algorithms for outlier detection or anomaly detection such as KNN or Isolation Forest can be used to automatically detect and remove outliers.” Chorev P. 2.
It would have been obvious to one of ordinary skill in the art before the effective filing date to combine the teaching of Chorev, and the teaching of the reference would have motivated one of ordinary skill in the art to remove incorrect data from the training set, to improve model accuracy.
None of the cited references explicitly state that an “external database” is accessed to verify the correctness of the training data. However, changing the location where the data is verified for correctness is a mere rearrangement of parts that does not alter the function of the invention. Similar to a switch on a machine, changing the location of the verification that training data is correct, does not alter the function of the invention. See MPEP § 2144.04(VI)(c). Further, nothing in the Specification indicates that use of an external database is critical to the invention.)
Response to Arguments
The arguments filed 06/24/2026 are not persuasive.
Rejections under § 112(a)
All rejections under this section are withdrawn in response to claim amendments.
Rejections under § 112(b)
The arguments state that the claims have been amended in response to the rejections under this section. Various sections of the Specification have been cited in the remarks, but applicant does not argue the proper claim interpretation.
Rejections under § 101
Applicant states that a proof tree is too complex for the human mind, and is therefore inconsistent with a finding that claims including the tree read on a mental process. See Rem. 14. See Spec. Fig. 7A showing trees consisting of only three nodes. See also Spec. Fig. 17 showing arguments that would be associated with multiple connected trees.
Applicant asserts a technological improvement based on pages 20-21 of the Specification. Notable absent is any description of any specific technological improvement.
Applicant cites language in the Claims and Specification before asserting an unnamed “technological improvement in the field of machine learning training data generation.” Rem 15. The improvement, according to Applicant, is “generating non-biased, complex training data.” Left unexplained is the connection between the claimed techniques and the elimination of bias from training data. It is also unclear why “complex” training data is an improvement, or what metric is used to determine complexity of training data.
If there is a specific operation or set of operations that result in a benefit to machine learning, clearly articulating how the claimed techniques result in the improvement may result in the withdrawal of all rejections under this section. It is noted that significant effort was put into the most recently filed arguments and amendments. It is submitted however, that clear concise arguments explaining how the claimed techniques result in a specific technological improvement in a practitioner’s own words, may be more persuasive than quoting large volumes of text from a translated specification. Likewise, asserting similarities between superficially explained technological improvements and facts of well-known cases is less convincing than an improvement tied to claimed techniques, that is clearly articulated.
Rejections under § 102
There are rejections under this section.
Rejections under § 103
Applicant states that Tafjord’s proof structure is fundamentally different from the claimed invention because its conclusion facts are intermediate nodes. This argument is inconsistent with both the teaching of Tafjord and with the claims. Note that both the claims and the reference combine trees that output conclusions. Both have conclusions as intermediate nodes. Both have conclusions as final nodes. See rejection above.
Applicant asserts that the claimed “repeating the argument a plurality of times” is not taught in the prior art. Rem. 23. It is not clear what this language means in the context of the invention, making a proper response difficult. See In re Steele, 305 F.2d 859, 134 USPQ 292 (CCPA 1962). This language could refer to the use of multiple trees together, or to repeating the same operations. Neither is clearly more reasonable than the other.
Applicant argues that the individual references do not teach claims rejected over the combined teachings.
Conclusion
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.
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PAUL M. KNIGHTExaminerArt Unit 2148
/PAUL M KNIGHT/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).
Prosecution Insights