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 .
DETAILED ACTION
Claims 1-12 are pending. Claims 1, 13, and 18 are independent.
Claims 2-11 depend from Claim 1.
This Application was published as U.S. 2024/0311562.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 15 Mar 2024 are in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Drawings
The drawings are objected to as failing to comply with 37 CFR 1.84(p)(5) because they include the following reference character(s) not mentioned in the description:
Fig 4 refers to “S107” which is not mentioned in the specification.
Corrected drawing sheets in compliance with 37 CFR 1.121(d), or amendment to the specification to add the reference character(s) in the description in compliance with 37 CFR 1.121(b) are required in reply to the Office action to avoid abandonment of the application. Any amended replacement drawing sheet should include all of the figures appearing on the immediate prior version of the sheet, even if only one figure is being amended. Each drawing sheet submitted after the filing date of an application must be labeled in the top margin as either “Replacement Sheet” or “New Sheet” pursuant to 37 CFR 1.121(d). If the changes are not accepted by the examiner, the applicant will be notified and informed of any required corrective action in the next Office action. The objection to the drawings will not be held in abeyance.
Claim Objections
Claims 6, 7, and 11 objected to because of the following informalities:
Claims refers to a “leaned model” which may be a typographical error, and applicant meant learned model, which is supported in the specification.
Appropriate correction is required.
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 6-9 and 11 rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
With regards to claim 6, 7, and 11:
Claims refer to a “leaned model” which is not mentioned in the specification. It is not clear what a leaned model is and what the difference is between a leaned model and model that is not leaned. Therefore, if the language of the claim is such that a person of ordinary skill in the art could not interpret the metes and bounds of the claim so as to understand how to avoid infringement, a rejection of the claim under 35 U.S.C. 112(b) or pre-AIA 35 U.S.C. 112, second paragraph, is appropriate. See IBSA Institut Biochimique, S.A. v. Teva Pharm. USA, Inc., 966 F.3d 1374, 1378-81, 2020 USPQ2d 10865 (Fed. Cir. 2020) For the purpose of examination, “leaned model” will be interpreted as learned model.
Dependent claims 8-9 are rejected based on their dependence to claim 7.
35 U.S.C. 112(f) 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.
This application includes one or more claim limitations that either use the “means for” format or while not using the word “means,” are nonetheless being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because the claim limitation(s) uses a generic placeholder that is coupled with functional language without reciting sufficient structure to perform the recited function and the generic placeholder is not preceded by a structural modifier.
Such claim limitation(s) are: the “obtaining unit,” “analysis processing unit,” “feature determining unit,” and “learning processing unit” in Claim 1 and its dependent Claims. These limitations are generic in the context of the art and don’t refer to any specific structure and only serve as placeholders for the structure that performs the associated function(s) without providing any information about what that structure is. MPEP 2181 I A says:
For a term to be considered a substitute for "means," and lack sufficient structure for performing the function, it must serve as a generic placeholder and thus not limit the scope of the claim to any specific manner or structure for performing the claimed function. It is important to remember that there are no absolutes in the determination of terms used as a substitute for "means" that serve as generic placeholders. The examiner must carefully consider the term in light of the specification and the commonly accepted meaning in the technological art. Every application will turn on its own facts.
Based on the ordinary skill in the art and description of functions of these components in the Specification, they refer to units that obtain documents, provide processing, determine features, and learn processing such as models or computers.
PLEASE NOTE: This is NOT a rejection. Please don’t address it as a rejection. If the Applicant does not agree with the INTERPRETATION, he may argue or amend to replace the terms interpreted under 112(f) with structural terms such as “model” or “processor” as appropriately supported by the Specification. In the alternative, he may let the interpretation stand if the intent was to include a means plus function limitation in the Claim.
The claims in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. The broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification when 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is invoked.
As explained in MPEP § 2181, subsection I, claim limitations that meet the following three-prong test will be interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph:
(A) the claim limitation uses the term “means” or “step” or a term used as a substitute for “means” that is a generic placeholder (also called a nonce term or a non-structural term having no specific structural meaning) for performing the claimed function;
(B) the term “means” or “step” or the generic placeholder is modified by functional language, typically, but not always linked by the transition word “for” (e.g., “means for”) or another linking word or phrase, such as “configured to” or “so that”; and
(C) the term “means” or “step” or the generic placeholder is not modified by sufficient structure, material, or acts for performing the claimed function.
Use of the word “means” (or “step”) in a claim with functional language creates a rebuttable presumption that the claim limitation is to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites sufficient structure, material, or acts to entirely perform the recited function.
Absence of the word “means” (or “step”) in a claim creates a rebuttable presumption that the claim limitation is not to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites function without reciting sufficient structure, material or acts to entirely perform the recited function.
Because these claim limitations are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, they are being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof.
If applicant does not intend to have this/these limitation(s) interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may: (1) amend the claim limitation(s) to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitation(s) recite(s) sufficient structure to perform the claimed function so as to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph.
Claim Rejections - 35 USC § 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 12 is 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) without significantly more. The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Independent claim 12 recite various limitations that, but for generic computer components (i.e. processing circuitry) can be performed in the human mind or with pen and paper, and are considered abstract ideas. The claims under the broadest reasonable interpretation cover the concept of obtaining data from a document, performing analysis on the data, determine a feature, determine a weight to perform machine learning, and delete the feature from the model. (See MPEP 2106.04(a)(2) III)
This judicial exception is not integrated into a practical application because the claims only recite elements in the form of “machine learning.” These elements are used to perform the claimed methods and steps, and are recited at a high-level of generality such that it amounts no more than mere instructions to apply the exception using a generic processing components. Accordingly, these additional elements do not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea.
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because they do not include subject matter that could not be performed by a human, as discussed above with respect to integration of the abstract idea into a practical application. The additional elements of using the generic processing elements to perform the claimed elements amount to no more than mere instructions to apply the exception using a generic computing component or can be considered insignificant extra solution activity. Mere instructions to apply an exception using a generic computing components cannot provide an inventive concept, and mere data gathering in conjunction with an abstract idea cannot provide an inventive concept. For the all the reasons stated above, the claims are not patent eligible.
Claim Rejections - 35 USC § 103
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, 6-9, and 12 are rejected under 35 U.S.C. 103 as being unpatentable over Takeda et al.(US2017/0358045 hereinafter Takeda) in view of Suzuki et al. (JP2020098388 hereinafter Suzuki) and Toutanova et al. (US2011/0144992 hereinafter Toutanova)
With regards to claim 1, Takeda teaches:
An information processing device, comprising: an obtaining unit configured to obtain document data; [Takeda Fig 1 teaches information processing device (100) that obtains document data (210) from memory]
an analysis processing unit configured to perform morphological analysis of the document data; [Takeda Fig 1 teaches analysis processing unit (100) that performs “morpheme analysis” (Par [0116])]
a feature determining unit configured to determine a feature in accordance with a result of the morphological analysis; and [Takeda Fig 1 teaches feature determining unit (100) that uses the result of “morpheme analysis on data (such as unknown data and the partial unknown data); and extract the object concepts corresponding to the content of the relevant data with reference to the above-mentioned database.” (Par [0116])]
a learning processing unit configured to perform machine learning to determine a weight of a morpheme in a model in accordance with the feature, the morpheme being obtained by the morphological analysis, and [Takeda Fig 6 teaches learning process for learning processing unit (100) (Par [0072]) which “learns weighting of data elements,” ”morpheme analysis,” and “extract the object concepts corresponding to the content of the relevant data with reference to the above-mentioned database.” (Par [0116])]
With regards to claim 1, Takeda fails to teach:
the model being either a linear model or generalized linear model,
wherein the learning processing unit performs processing of deleting, from input data of the model, the feature corresponding to the morpheme having the weight a value of which is determined to be smaller than, or equal to, a given threshold value.
With regards to claim 1, Suzuki teaches:
the model being either a linear model or generalized linear model, [Suzuki teaches “learning unit 46 executes the learning process using multiple regression analysis” (Page 4 lines 27-28) where logistic regression is a generalized linear model]
wherein the learning processing unit performs processing of deleting, from input data of the model, the feature corresponding to the morpheme having the weight a value of which is determined to be smaller than, or equal to, a given threshold value. [Suzuki teaches executing “morphological analysis or the like on the document” (Page 4 line 20) and “excludes, from among the keywords extracted by the word extraction unit 41, keywords having a weight
less than a predetermined value.” (page 4 line 40-41)
It would be obvious to one of ordinary skill at the time of applicant’s filing to combine the data analysis apparatus taught by Takeda with the extraction unit as taught by Suzuki. The motivation to combine the teachings of Takeda with Suzuki is because Suzuki teaches “extracts a keyword that appears for each item included in a plan of an existing product and a new product” (Page 4 lines 18-19) which increases the capabilities of the invention of Takeda to work for different client applications]
With regards to claim 1, Takeda in view of Suzuki fails to teach:
the model being a linear model
With regards to claim 1, Toutanova teaches:
the model being a linear model, [Toutanova teaches “log-linear model” and the “model includes a morpheme-context model, with one feature for each morpheme, and one feature for each morpheme context” (Par [0026])
It would be obvious to one of ordinary skill at the time of applicant’s filing to combine the data analysis apparatus and extraction unit as taught by Takeda and Suzuki with the log-linear model as taught by Toutanova. The motivation to combine the teachings of Takeda and Suzuki with Toutanova is because Toutanova teaches “the log-linear aspect allows the model to use flexible feature representations without concern for conditional independence” (Par [0015]) which increases the capabilities of the invention of Takeda and Suzuki to work with different features to increase client applications]
With regards to claim 3, Takeda in view of Suzuki and Toutanova teaches:
All the limitations of claim 1
wherein the learning processing unit
performs processing of evaluating the model, and, if performance of the model is determined to be lower than, or equal to, a predetermined level in the processing of evaluating, continues the machine learning while the feature determining unit changes a feature model to be used for determining the feature. [Takeda Fig 1 teaches “relation evaluation unit 120 calculates the score indicating the strength of the relation between the data elements of data included in the training data and the classification information” (Par [0046]) which measures performance of the model on a predetermined level, but can change the level by receiving “feedback on the judgment from the user via a user interface” (Par [0054]) and “recalculate the weight according to the newly obtained feedback on the judgment by the data analysis apparatus 100” (Par [0056]) in order to perform machine learning and make changes to the data and model]
With regards to claim 4, Takeda in view of Suzuki and Toutanova teaches:
All the limitations of claim 1
wherein the feature determining unit determines a metadata feature in accordance with metadata assigned to the document data, the metadata feature being a feature corresponding to the metadata, [Takeda teaches acquiring “metadata regarding the acquired digital information;” (Par [0103])]
and the learning processing unit performs the machine learning in accordance with the feature corresponding to the morpheme and the metadata feature. [Takeda teaches “data analysis system 1… updates a weighted parameter set … and updates the relation between morphemes and the digital information by using the weighted parameter set” (Par [0104])]
With regards to claim 6, Takeda in view of Suzuki and Toutanova teaches:
All the limitations of claim 1
further comprising
an inference processing unit configured to perform processing of inference target data that is the document data to be inferred, in accordance with a leaned model that is the model on which the learning processing unit has performed the machine learning, [Takeda teaches classification information which is combined with data, where classification information is “information indicating whether or not the data is related to the statements of claims and the text data in a description of a patent which the user wishes to invalidate” (Par [0032]) which is inference processing, and where the data is stored in memory unit (Fig 1 item 200) which stores “training data and a plurality of pieces of unknown data” (Par [0032]) where the training data is used to train the model and unknown data are “search targets” (Par [0035]) ]
wherein the inference processing unit
performs processing of outputting, as a score, probability data indicating probability that the inference target data is related to a given event. [Takeda teaches unknown data or search targets is data “data concerning which the data analysis system needs to estimate the “classification information” in the form of a “score.” (Par [0033])]
With regards to claim 7, Takeda in view of Suzuki and Toutanova teaches:
All the limitations of claim 1
further comprising
an inference processing unit configured to perform processing of inference target data that is the document data to be inferred, in accordance with a leaned model that is the model on which the learning processing unit has performed the machine learning, [Takeda teaches classification information which is combined with data, where classification information is “information indicating whether or not the data is related to the statements of claims and the text data in a description of a patent which the user wishes to invalidate” (Par [0032]) which is inference processing, and where the data is stored in memory unit (Fig 1 item 200) which stores “training data and a plurality of pieces of unknown data” (Par [0032]) where the training data is used to train the model and unknown data are “search targets” (Par [0035]) ]
wherein the inference processing unit performs processing of:
dividing the inference target data into a plurality of blocks in any given length; and outputting probability data for each of the plurality of blocks, the probability data being provided as a score and indicating a probability relevant to a given event. [Takeda teaches dividing the target data or “search target document into a plurality of pieces of partial unknown data and evaluates the possibility that each piece of the partial unknown data may fall under the invalid material or the prior art … the data analysis system integrates the score calculated for each piece of the partial unknown data” (Par [0025]
With regards to claim 8, Takeda in view of Suzuki and Toutanova teaches:
All the limitations of claim 7
wherein the inference processing unit
compares, for each of the plurality of blocks, the score and a threshold value independent of a genre of the inference target data, and determines a display mode of each block in accordance with a result of the comparison. [Takeda teaches “compares the feature quantity of the object data regarding which the tag is accepted, with the feature quantity of the data; updates a score of the data corresponding to a specified tag on the basis of the comparison result; and controls the order to display the data to be displayed on the basis of the updated score” (Par [0091]) where a basis of the comparison result includes a threshold value]
With regards to claim 9, Takeda in view of Suzuki and Toutanova teaches:
All the limitations of claim 7
wherein if a plurality of inference target data items are obtained as the document data to be inferred, the inference processing unit performs processing of:
calculating the score for each of the plurality of inference target data items; and [Takeda teaches “data analysis system integrates the score calculated for each piece of the partial unknown data” (Par [0025])]
outputting the score for each of the plurality of blocks for inference target data items included in the plurality of inference target data items and having relatively high scores. [Takeda Fig 4 teaches outputting the scores by “normalized rank (the rank by which the descending order of scores calculated for the unknown data is normalized within the range of 0 to 1) and the vertical axis represents a recall rate (an index indicative of comprehensiveness of the extracted data)” (Par [0062]
With regards to claim 12, Takeda teaches:
A method, for processing information, causing an information processing device to carry out steps of: obtaining document data; [Takeda Fig 1 teaches information processing device (100) that obtains document data (210) from memory]
performing morphological analysis on the document data; [Takeda Fig 1 teaches analysis processing unit (100) that performs “morpheme analysis” (Par [0116])]
determining a feature in accordance with a result of the morphological analysis; and [Takeda Fig 1 teaches feature determining unit (100) that uses the result of “morpheme analysis on data (such as unknown data and the partial unknown data); and extract the object concepts corresponding to the content of the relevant data with reference to the above-mentioned database.” (Par [0116])]
performing machine learning to determine a weight of a morpheme in a model in accordance with the feature, the morpheme being obtained by the morphological analysis,
and [Takeda Fig 6 teaches learning process for learning processing unit (100) (Par [0072]) which “learns weighting of data elements,” ”morpheme analysis,” and “extract the object concepts corresponding to the content of the relevant data with reference to the above-mentioned database.” (Par [0116])]
With regards to claim 12, Takeda fails to teach:
the model being either a linear model or a generalized linear model,
wherein the machine learning involves performing processing of deleting, from input data of the model, the feature corresponding to the morpheme having the weight a value of which is determined to be smaller than, or equal to, a given threshold value.
With regards to claim 12, Suzuki teaches:
the model being either a linear model or a generalized linear model, [Suzuki teaches “learning unit 46 executes the learning process using multiple regression analysis” (Page 4 lines 27-28) where logistic regression is a generalized linear model]
wherein the machine learning involves performing processing of deleting, from input data of the model, the feature corresponding to the morpheme having the weight a value of which is determined to be smaller than, or equal to, a given threshold value. [Suzuki teaches executing “morphological analysis or the like on the document” (Page 4 line 20) and “excludes, from among the keywords extracted by the word extraction unit 41, keywords having a weight less than a predetermined value.” (page 4 line 40-41)
It would be obvious to one of ordinary skill at the time of applicant’s filing to combine the data analysis apparatus taught by Takeda with the extraction unit as taught by Suzuki. The motivation to combine the teachings of Takeda with Suzuki is because Suzuki teaches “extracts a keyword that appears for each item included in a plan of an existing product and a new product” (Page 4 lines 18-19) which increases the capabilities of the invention of Takeda to work for different client applications]
Claim 2 is rejected under 35 U.S.C. 103 as being unpatentable over Takeda et al.(US2017/0358045), Suzuki et al. (JP2020098388), and Toutanova et al. (US2011/0144992 ) in further view of Abbott et al. (US2024/0347286 hereinafter Abbott)
With regards to claim 2, Takeda in view of Suzuki and Toutanova teaches:
All the limitations of claim 1
With regards to claim 2, Takeda in view of Suzuki and Toutanova fails to teach:
wherein the learning processing unit: is switchable between ON and OFF of ensemble learning of obtaining, as the model, a plurality of models to be used in combination in inference processing; and
performs processing of evaluating the model, and, if performance of the model is determined to be lower than, or equal to, a predetermined level, turns OFF the ensemble learning, and continues the machine learning.
With regards to claim 2, Abbott teaches:
wherein the learning processing unit: is switchable between ON and OFF of ensemble learning of obtaining, as the model, a plurality of models to be used in combination in inference processing; and [Abbott teaches machine learning controller (Fig 13 item 210) that uses “supervised learning, unsupervised learning, reinforcement learning, ensemble learning, active learning, transfer learning, or other suitable learning techniques for machine learning programs, algorithms, or models … [and] to switch between different machine learning programs, algorithms, or models;” (Par [0115]) where “activation switch (e.g., mechanical switch, electronic switch, UI element input(s) 290 of the power tool pack adapter 106) can selectively switch between an activated state and a deactivated state (Par [0114])]
performs processing of evaluating the model, and, if performance of the model is determined to be lower than, or equal to, a predetermined level, turns OFF the ensemble learning, and continues the machine learning. [Abbott teaches “to change output thresholds for a machine learning program, algorithms, or model” (Par [0115])]
It would be obvious to one of ordinary skill at the time of applicant’s filing to combine the data analysis apparatus and extraction unit as taught by Takeda, Suzuki, and Toutanova with the machine learning controller as taught by Abbott. The motivation to combine the teachings of Takeda, Suzuki, and Toutanova with Abbott is because Abbott teaches “the machine learning controller 210 may be a static machine learning controller, a self-updating machine learning controller, an adjustable machine learning controller, or the like” (Par [0117]) which increases the capabilities of the invention of Takeda, Suzuki, and Toutanova to work with different machine learning settings]
Potentially Allowable Subject Matter
Claim 5 and 10 objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
Claim 11 would be allowable if rewritten to overcome the rejection(s) under 35 U.S.C. 112, set forth in this Office action and to include all of the limitations of the base claim and any intervening claims.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Joseph J Yamamoto whose telephone number is (571)272-4020. The examiner can normally be reached M-F 1000-1800 EST.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Bhavesh Mehta can be reached at 571-272-7453. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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JOSEPH J. YAMAMOTO
Examiner
Art Unit 2656
/BHAVESH M MEHTA/Supervisory Patent Examiner, Art Unit 2656