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 .
Allowable Subject Matter
The indicated allowability of claims 1-5, 7 and 8 is withdrawn in view of the amendment to the independent claims that changes the scope of the invention. Rejections in light of the changes are presented below.
Response to Arguments
Applicant's arguments filed 11/26/25 have been fully considered but they are not persuasive.
Regarding the 35 U.S.C. 101 rejection of the claims, Applicant argues that the amended claims are not directed to a judicial exception because the claims involve a “machine learning based computation” which is a particular computer component, and that the claimed steps is a technical data processing method that is not limited to mere classification of information (Arguments, pg. 6 – pg. 7, fourth para.).
Examiner respectfully disagrees because the claims recite steps of inputting a menu name of a restaurant (i.e., a data gathering step), outputting a category name corresponding to the menu name (i.e., a data analysis/post solutional activity), performing morphological analysis to divide the inputted menu name into words and determine parts of speech (i.e., a data analysis/evaluation step), generating based on the morphological analysis result, a numerical feature vector representing presence or absence of the words. and classify the menu name into a category name corresponding thereto by executing a machine learning based computation (i.e., a data analysis step), and outputting the category name corresponding to the menu name (i.e., a post solutional step of providing results of analysis), corresponding to steps achievable by a human in using a pen and paper to analyze gathered text and record menu names in association with category names without significantly more.
The use of a machine learning based computation to generate a numerical feature vector and a classification as claimed as being performed by a processor/processing method/processing apparatus corresponds to analyzing data using a generic computer component to perform processing and corresponds to merely tying a generic computer component to the abstract idea of representing data (via vector representation) and classifying data.
Applicant further argues that the abstract idea is integrated into a practical application because of the execution of the steps using a machine learning based computation by quantifying the results of morphological analysis and inputting them into a machine learning model, thereby improving classification accuracy and processing efficiency that could not be achieved through conventional manual classification or simple dictionary collation, and further that the claims are analogous to court cases Enfish and McRO (Arguments, pg. 7, fifth para. – pg. 8).
Examiner respectfully disagrees as there is no evidence provided to account for the claims providing an improvement to the functioning of the computer implementing the steps nor to an improvement to other technology. The use of a machine learning based computation to generate a numerical feature vector and a classification corresponds to merely tying a generic computer component to the abstract idea of representing data and classifying data, and further does not solve a technical problem such as improving machine learning technology. Furthermore, using a machine learning model to achieve classification and processing efficiency better than a manual process does not improve computer functionality because the claimed invention itself is not making the computer faster or otherwise improved, rather, the computer is operating as a generic computer performing steps that would otherwise be performed with pen and paper (See Bancorp Services, L.L.C. v. Sun Life Assurance Co. of Canada (U.S.), 687 F.3d 1266, 1278 (Fed. Cir. 2012).
Also, unlike Enfish that provides evidence of achieved benefits over conventional database in providing improvements to the technology, the instant claims merely analyze data and provides results of analysis (by outputting data) without improving the technology or the functioning of the computer. Likewise, unlike McRO that provides evidence of how claimed rules enabled the automation of specific animation tasks that previously could not be automated when determining that the claims were directed to improvements in computer animation, the instant claims include steps that merely analyze data and provides results of analysis (by outputting data). There are no claimed rules that are applied to a specific task in the instant claims beyond tying the abstract idea of data analysis to a generic computer.
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 1-5, 7 and 8 are rejected under 35 U.S.C. 101 because the claimed invention is directed to the abstract idea of text analysis without significantly more. The claims 1, 7 and 8 recite steps of inputting a menu name of a restaurant (i.e., a data gathering step), outputting a category name corresponding to the menu name (i.e., a data analysis/post solutional activity), performing morphological analysis to divide the inputted menu name into words and determine parts of speech (i.e., a data analysis/evaluation step), generating based on the morphological analysis result, a numerical feature vector representing presence or absence of the words. and classify the menu name into a category name corresponding thereto by executing a machine learning based computation (i.e., a data analysis step), and outputting the category name corresponding to the menu name (i.e., a post solutional step of providing results of analysis), corresponding to steps achievable by a human in using a pen and paper to analyze gathered text and record menu names in association with category names, and as such, the steps correspond to the mental processes category of abstract ideas. This judicial exception is not integrated into a practical application because the claims are directed to an abstract idea with additional generic computer elements, where the generically recited computer elements (processor, processing method, processing apparatus, memory, medium, machine learning based computation) do not add a meaningful limitation to the abstract idea because they amount to simply implementing the abstract idea on a computer The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because steps “generating based on the morphological analysis result, a numerical feature vector representing presence or absence of the words. and classify the menu name into a category name corresponding thereto by executing a machine learning based computation” and “outputting the category name corresponding to the menu name” correspond to the well-understood, routine, conventional computer functions of “Gathering and analyzing information using conventional techniques and displaying the result” as well as “collecting information, analyzing it, and displaying certain results of the collection and analysis” as recognized by the court decisions listed in MPEP § 2106.05 and as provided by cited references Ihara and Bru (see PTO 892 form, 9/18/25).
The dependent claims 2-5 also recite mental processes and do not add significantly more than the abstract idea and are as such similarly rejected.
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 (i.e., changing from AIA to pre-AIA ) 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.
1. Claims 1-5, 7 and 8 are rejected under 35 U.S.C. 103 as being unpatentable over Ihara et al US 2018/0240204 A1 (“Ihara”) in view of Ochiai et al JP 2015090664 A (“Ochiai”) and Brun et al US 2016/0171386 A1 (“Brun”)
Per Claim 1, Ihara discloses an information processing apparatus comprising:
a memory storing instructions (para. [0034]); and
a processor configured to execute the instructions (para. [0034]) to:
input a menu name of a restaurant (para. [0060]);
perform morphological analysis to divide the inputted menu name into words (para. [0060]; para. [0062]);
and classify the menu name into a category name corresponding thereto (The processor 11 of the server apparatus 10 classifies these food or drink menus. For example, from the constituent words of “pasta” and “spaghetti” obtained through the classification by the morphological analysis (corresponding to “first words that are common words used in the names of food or drink menus”), the food or drink menus shown in Table 2 are classified as menus belonging to “pasta group” …, para. [0064]) and
output the category name corresponding to the menu name (para. [0082]; para. [0085]; para. [0093])
Ihara does not explicitly disclose to: generate based on the morphological analysis result, a numerical feature vector representing presence or absence of the words
However, this feature is taught by Ochiai (para. [0038]-[0039]; para. [0115]-[0116])
Ihara in view of Ochiai does not explicitly disclose to: perform morphological analysis to determine parts of speech or classify the menu name into a category name corresponding thereto by executing a machine learning based computation
However, this feature is taught by Brun;
perform morphological analysis to determine parts of speech (para. [0025]; para. [0043]; para. [0046]);
classify the menu name into a category name corresponding thereto by executing a machine learning based computation (para. [0054])
It would have been obvious to one of ordinary skill in the art before the effective filing of the invention to combine the teachings of Ochiai with the apparatus of Ihara in arriving at the missing features of Ihara, as well as to combine the teachings of Brun with the apparatus of Ihara in view of Ochiai in arriving at the missing features of Ihara in view of Ochiai, because such combination would have resulted in improving a user’s convenience by providing appropriate words (Ochiai, para. [0038]-[0039]; para. [0119]) as well as in providing aspect category and aspect term polarity mutual annotation for aspect-based sentiment analysis among multiple classification options available, while offering better recall (Brun, Abstract; para. [0054]).
Per Claim 2, Ihara in view of Ochiai and Brun discloses the information processing apparatus according to claim 1,
Ihara discloses wherein the processor is further configured to execute the instructions to specify that the category name corresponds to the inputted menu name in a case where the category name corresponding to the inputted menu name is stored in the memory (para. [0062]-[0064]).
Per Claim 3, Ihara in view of Ochiai and Brun discloses the information processing apparatus according to claim 1,
Ihara discloses wherein the processor is further configured to execute the instructions to extract nouns from the words obtained by dividing the inputted menu name and exclude specific words from the extracted nouns to execute the morphological analysis (The processor 11 of the server apparatus 10 creates aggregate data concerning the plurality of restaurant shops S, and performs a morphological analysis in, for example, a Japanese natural language process on each name of food or drink menu to thereby perform a logical computation process that classifies a character string of the name of each food or drink menu into a plurality of constituent words. For example, by performing a morphological analysis on “pasta with squid and zucchini,” “squid,” “zucchini,” and “pasta” are obtained as constituent words.…, para. [0060])
Brun discloses wherein the processor is further configured to execute the instructions to extract verbs from the words obtained by dividing the inputted menu name (para. [0043])
Per Claim 4, Ihara in view of Ochiai and Brun discloses the e information processing apparatus according to claim 1,
Ihara discloses wherein the processor is further configured to execute the instructions to store the category name for each type of business to which a location handling a menu corresponding to the inputted menu name belongs (para. [0052]; para. [0055]).
Per Claim 5, Ihara in view of Ochiai and Brun discloses the information processing apparatus according to claim 1,
Brun wherein the processor is configured to execute the instructions to specify the category name corresponding to the inputted menu name by means of a supervised learning method (Abstract; para. [0055]).
Per Claim 7, Ihara discloses an information processing method comprising:
inputting a menu name of a restaurant (para. [0060]);
performing morphological analysis to divide the inputted menu name into words (para. [0060]; para. [0062]);
and classifying the menu name into a category name corresponding thereto (The processor 11 of the server apparatus 10 classifies these food or drink menus. For example, from the constituent words of “pasta” and “spaghetti” obtained through the classification by the morphological analysis (corresponding to “first words that are common words used in the names of food or drink menus”), the food or drink menus shown in Table 2 are classified as menus belonging to “pasta group” …, para. [0064]) and
outputting the category name corresponding to the menu name (para. [0082]; para. [0085]; para. [0093])
Ihara does not explicitly disclose generating based on the morphological analysis result, a numerical feature vector representing presence or absence of the words
However, this feature is taught by Ochiai (para. [0038]-[0039]; para. [0115]-[0116])
Ihara in view of Ochiai does not explicitly disclose performing morphological analysis to determine parts of speech or classifying the menu name into a category name corresponding thereto by executing a machine learning based computation
However, these features are taught by Brun;
performing morphological analysis to determine parts of speech (para. [0025]; para. [0043]; para. [0046]);
classifying the menu name into a category name corresponding thereto by executing a machine learning based computation (para. [0054])
It would have been obvious to one of ordinary skill in the art before the effective filing of the invention to combine the teachings of Ochiai with the method of Ihara in arriving at the missing features of Ihara, as well as to combine the teachings of Brun with the method of Ihara in view of Ochiai in arriving at the missing features of Ihara in view of Ochiai, because such combination would have resulted in improving a user’s convenience by providing appropriate words (Ochiai, para. [0038]-[0039]; para. [0119]) as well as in providing aspect category and aspect term polarity mutual annotation for aspect-based sentiment analysis among multiple classification options available, while offering better recall (Brun, Abstract; para. [0054]).
Per Claim 8, Ihara discloses a non-transitory computer readable medium in which a program is stored, the program causing an information processing apparatus to execute:
inputting a menu name of a restaurant (para. [0060]);
performing morphological analysis to divide the inputted menu name into words (para. [0060]; para. [0062]);
and classifying the menu name into a category name corresponding thereto (The processor 11 of the server apparatus 10 classifies these food or drink menus. For example, from the constituent words of “pasta” and “spaghetti” obtained through the classification by the morphological analysis (corresponding to “first words that are common words used in the names of food or drink menus”), the food or drink menus shown in Table 2 are classified as menus belonging to “pasta group” …, para. [0064]) and
outputting the category name corresponding to the menu name (para. [0082]; para. [0085]; para. [0093])
Ihara does not explicitly disclose generating based on the morphological analysis result, a numerical feature vector representing presence or absence of the words
However, this feature is taught by Ochiai (para. [0038]-[0039]; para. [0115]-[0116])
Ihara in view of Ochiai does not explicitly disclose performing morphological analysis to determine parts of speech or classifying the menu name into a category name corresponding thereto by executing a machine learning based computation
However, this feature is taught by Brun;
performing morphological analysis to determine parts of speech (para. [0025]; para. [0043]; para. [0046]);
classifying the menu name into a category name corresponding thereto by executing a machine learning based computation (para. [0054])
It would have been obvious to one of ordinary skill in the art before the effective filing of the invention to combine the teachings of Ochiai with the medium of Ihara in arriving at the missing features of Ihara, as well as to combine the teachings of Brun with the medium of Ihara in view of Ochiai in arriving at the missing features of Ihara in view of Ochiai, because such combination would have resulted in improving a user’s convenience by providing appropriate words (Ochiai, para. [0038]-[0039]; para. [0119]) as well as in providing aspect category and aspect term polarity mutual annotation for aspect-based sentiment analysis among multiple classification options available, while offering better recall (Brun, Abstract; para. [0054]).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. See PTO 892 form.
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to OLUJIMI A ADESANYA whose telephone number is (571)270-3307. The examiner can normally be reached Monday-Friday 8:30-5:00pm.
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/OLUJIMI A ADESANYA/Primary Examiner, Art Unit 2658