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 .
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to the abstract idea of data analysis without significantly more. The claims 1, 19 and 20 recite steps of accessing, by the data processing system from one or more hardware storage devices, a plurality of data vectors representing a plurality of text segments (i.e., a data gathering step), clustering, by the data processing system, the plurality of data vectors into one or more clusters (i.e., a data gathering/analysis step), determining a semantic topic of each of the one or more clusters, wherein determining the semantic topic of each of the one or more clusters comprises, for each of the one or more clusters: parsing, by a parser of the data processing system, fields of the data vectors of the cluster, determining, based on the parsing, a first word representing the cluster, determining a first value representing a frequency of the first word in a training data set, comparing the first value to a threshold value, and at least one of: responsive to determining that the first value is less than the threshold value, identifying the first word as a semantic topic of the cluster, or responsive to determining that the first value is greater than or equal to the threshold value, identifying another word as the semantic topic of the cluster (i.e., a data analysis/evaluation step), generating, by the data processing system, a data structure representing the semantic topic of each of the one or more clusters (i.e., a data analysis/evaluation step) and storing, by the data processing system in the one or more hardware storage devices, the data structure (i.e., a data analysis/evaluation step), corresponding to steps achievable by a human in manually/mentally gathering data, analyzing the data and storing the results of the analysis, and as such, corresponds 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 (data processing system, system, processor, memory, computer-readable medium) 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, by the data processing system, a data structure representing the semantic topic of each of the one or more clusters” and “storing, by the data processing system in the one or more hardware storage devices, the data structure” correspond to well-understood, routine, conventional computer functions of analyzing data as well as storing and retrieving information in/from memory as recognized by the court decisions listed in MPEP § 2106.05 and as provided by cited reference Nakayama (PTO 892 form).
The dependent claims also recite mental processes and do not add significantly more than the abstract idea and are as such similarly rejected.
Claim Rejections - 35 USC § 102
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 the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
1. Claims 1-3, 5, 6, 8, 9, 11, 19 and 20 are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Nakayama et al US 2024/0070398 A1 (“Nakayama”)
Per claim 1, Nakayama discloses a method performed by a data processing system, the method comprising:
accessing, by the data processing system from one or more hardware storage devices, a plurality of data vectors representing a plurality of text segment (para. [0068]; the clustering execution module 104 acquires a plurality of first sentences from a plurality of first comments, and acquires a feature vector of each of the plurality of first sentences, para. [0069]);
clustering, by the data processing system, the plurality of data vectors into one or more clusters (fig. 7; the clustering execution module 104 executes the k-means clustering based on the feature vector of each of the plurality of first sentences…., para. [0070]; FIG. 7 is a diagram for illustrating an example of clusters. In the example of FIG. 7, three clusters of clusters C1 to C3 are illustrated …, para. [0071])
determining a semantic topic of each of the one or more clusters, wherein determining the semantic topic of each of the one or more clusters comprises (the topic word acquisition module 105 acquires, for each cluster C, words included in the first sentences belonging to this cluster as topic words …, para. [0078]), for each of the one or more clusters:
parsing, by a parser of the data processing system, fields of the data vectors of the cluster (the topic word acquisition module 105 acquires, for each cluster C, words included in the first sentences belonging to this cluster as topic words …, para. [0078]),
determining, based on the parsing, a first word representing the cluster (para. [0078]; the topic word acquisition module 105 may acquire, as the topic word, a candidate word having an appearance frequency equal to or higher than a threshold value…., para. [0082]),
determining a first value representing a frequency of the first word in a training data set (An appearance frequency of each of the candidate words W1, W2, and the like is calculated by the topic word acquisition module …, para. [0072]),
comparing the first value to a threshold value (para. [0082]), and at least one of:
responsive to determining that the first value is less than the threshold value, identifying the first word as a semantic topic of the cluster (fig. 7; Meanwhile, the candidate word W2 appears only in the cluster C1, and hence the appearance frequency based on the TF-IDF method is high … and hence is acquired as the topic word of the cluster C1…., para. [0084], low appearance frequency as implying frequency less than threshold), or
responsive to determining that the first value is greater than or equal to the threshold value, identifying another word as the semantic topic of the cluster (fig. 7; The candidate word W1 uniformly appears in the clusters C1 to C3 …The candidate word W1 is not a word which represents each of the clusters C1 to C3 but merely a word generally used when a certain comment about the service is input, and hence is not acquired as the topic word, para. [0083]; para. [0084]);
generating, by the data processing system, a data structure representing the semantic topic of each of the one or more clusters (para. [0047]; para. [0070]-[0071]); and
storing, by the data processing system in the one or more hardware storage devices, the data structure (fig. 6; para. [0047]).
Per claim 2, Nakayama discloses the method of claim 1, wherein determining the first word representing the cluster comprises: determining that the cluster is associated with a first subset of the data vectors, and determining that the first word appears most frequently from among the words in the first subset of the data vectors (. The clustering execution module 104 executes the clustering so that sentences similar to one another in feature vector belong to the same cluster…., para. [0070]-[0071]; para. [0081]).
Per claim 3, Nakayama discloses the method of claim 2, wherein identifying another word as the semantic topic of the cluster comprises: determining that a second word appears second most frequently from among the words in the first subset of the data vectors, determining a second value representing a frequency of the second word in the training data set, comparing the second value to the threshold value (para. [0078]-[0079]; para. [0082]), and
at least one of: responsive to determining that the second value is less than the threshold value, identifying the second word as a semantic topic of the cluster (fig. 7; para. [0084]), or
responsive to determining that the second value is greater than or equal to the threshold value, identifying another word as the semantic topic of the cluster (fig. 7; para. [0083]-[0084]).
Per claim 5, Nakayama discloses the method of claim 1, wherein identifying another word as the semantic topic of the cluster comprises: re-clustering the plurality of data vectors into one or more second clusters (fig. 7; para. [0071]); and
determining the semantic topic of each of the one or more second clusters (fig. 7; para. [0071]).
Per claim 6, Nakayama discloses the method of claim 1, wherein clustering the plurality of data vectors into the one or more clusters comprises: clustering the plurality of data structures based on similarities between the plurality of data structures (para. [0044]).
Per claim 8, Nakayama discloses the method of claim 1, further comprising generating the plurality of data vectors based on the plurality of text segments (para. [0068]-[0069]).
Per claim 9, Nakayama discloses the method of claim 8, wherein generating the plurality of data vectors comprises at least one of: tokenizing the plurality of text segments, lemmatizing the plurality of text segments, or filtering the plurality of text segments (para. [0061]-[0063]).
Per claim 11, Nakayama discloses the method of claim 1, wherein each of the text segments represents a respective first user's satisfaction with one or more products or services (para. [0025]; para. [0027]).
Per claim 19, Nakayama discloses a system, comprising:
at least one processor (para. [0020]); and
a memory communicatively coupled to the at least one processor, the memory storing instructions which, when executed by the at least one processor, cause the at least one processor to (para. [0020]) perform operations comprising:
accessing, from one or more hardware storage devices, a plurality of data vectors representing a plurality of text segments (para. [0068]; the clustering execution module 104 acquires a plurality of first sentences from a plurality of first comments, and acquires a feature vector of each of the plurality of first sentences, para. [0069]);
clustering the plurality of data vectors into one or more clusters (fig. 7; the clustering execution module 104 executes the k-means clustering based on the feature vector of each of the plurality of first sentences…., para. [0070]; FIG. 7 is a diagram for illustrating an example of clusters. In the example of FIG. 7, three clusters of clusters C1 to C3 are illustrated …, para. [0071])
determining a semantic topic of each of the one or more clusters, wherein determining the semantic topic of each of the one or more clusters comprises (the topic word acquisition module 105 acquires, for each cluster C, words included in the first sentences belonging to this cluster as topic words …, para. [0078]), for each of the one or more clusters:
parsing, by a parser, fields of the data vectors of the cluster (the topic word acquisition module 105 acquires, for each cluster C, words included in the first sentences belonging to this cluster as topic words …, para. [0078]),
determining, based on the parsing, a first word representing the cluster (para. [0078]; the topic word acquisition module 105 may acquire, as the topic word, a candidate word having an appearance frequency equal to or higher than a threshold value…., para. [0082]),
determining a first value representing a frequency of the first word in a training data set (An appearance frequency of each of the candidate words W1, W2, and the like is calculated by the topic word acquisition module …, para. [0072]),
comparing the first value to a threshold value (para. [0082]), and at least one of:
responsive to determining that the first value is less than the threshold value, identifying the first word as a semantic topic of the cluster (fig. 7; Meanwhile, the candidate word W2 appears only in the cluster C1, and hence the appearance frequency based on the TF-IDF method is high … and hence is acquired as the topic word of the cluster C1…., para. [0084], low appearance frequency as implying frequency less than threshold), or
responsive to determining that the first value is greater than or equal to the threshold value, identifying another word as the semantic topic of the cluster (fig. 7; The candidate word W1 uniformly appears in the clusters C1 to C3 …The candidate word W1 is not a word which represents each of the clusters C1 to C3 but merely a word generally used when a certain comment about the service is input, and hence is not acquired as the topic word, para. [0083]; para. [0084]);
generating, a data structure representing the semantic topic of each of the one or more clusters (para. [0047]; para. [0070]-[0071]); and
storing, in the one or more hardware storage devices, the data structure (fig. 6; para. [0047]).
Per claim 20, Nakayama discloses one or more non-transitory computer-readable media storing instructions which, when executed by at least one processor, cause the at least one processor to perform operations comprising:
accessing, from one or more hardware storage devices, a plurality of data vectors representing a plurality of text segments (para. [0068]; the clustering execution module 104 acquires a plurality of first sentences from a plurality of first comments, and acquires a feature vector of each of the plurality of first sentences, para. [0069]);
clustering the plurality of data vectors into one or more clusters (fig. 7; the clustering execution module 104 executes the k-means clustering based on the feature vector of each of the plurality of first sentences…., para. [0070]; FIG. 7 is a diagram for illustrating an example of clusters. In the example of FIG. 7, three clusters of clusters C1 to C3 are illustrated …, para. [0071])
determining a semantic topic of each of the one or more clusters, wherein determining the semantic topic of each of the one or more clusters comprises (the topic word acquisition module 105 acquires, for each cluster C, words included in the first sentences belonging to this cluster as topic words …, para. [0078]), for each of the one or more clusters:
parsing, by a parser, fields of the data vectors of the cluster (the topic word acquisition module 105 acquires, for each cluster C, words included in the first sentences belonging to this cluster as topic words …, para. [0078]),
determining, based on the parsing, a first word representing the cluster (para. [0078]; the topic word acquisition module 105 may acquire, as the topic word, a candidate word having an appearance frequency equal to or higher than a threshold value…., para. [0082]),
determining a first value representing a frequency of the first word in a training data set (An appearance frequency of each of the candidate words W1, W2, and the like is calculated by the topic word acquisition module …, para. [0072]),
comparing the first value to a threshold value (para. [0082]), and at least one of:
responsive to determining that the first value is less than the threshold value, identifying the first word as a semantic topic of the cluster (fig. 7; Meanwhile, the candidate word W2 appears only in the cluster C1, and hence the appearance frequency based on the TF-IDF method is high … and hence is acquired as the topic word of the cluster C1…., para. [0084], low appearance frequency as implying frequency less than threshold), or
responsive to determining that the first value is greater than or equal to the threshold value, identifying another word as the semantic topic of the cluster (fig. 7; The candidate word W1 uniformly appears in the clusters C1 to C3 …The candidate word W1 is not a word which represents each of the clusters C1 to C3 but merely a word generally used when a certain comment about the service is input, and hence is not acquired as the topic word, para. [0083]; para. [0084]);
generating, a data structure representing the semantic topic of each of the one or more clusters (para. [0047]; para. [0070]-[0071]); and
storing, in the one or more hardware storage devices, the data structure (fig. 6; para. [0047]).
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.
2. Claim 4 is rejected under 35 U.S.C. 103 as being unpatentable over Nakayama in view of Jayaraman US 2020/0349199 A1 (“Jayaraman”)
Per claim 4, Nakayama discloses the method of claim 1,
Nakayama does not explicitly disclose for at least one of the one or more clusters: responsive to determining that the first value is greater than or equal to the threshold value, determining the semantic topic of the cluster was not found, and wherein the data structure representing that semantic topic of the cluster was not found
However, this feature is taught by Jayaraman (para. [0234]-[0235])
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 Jayaraman with the method of Nakayama in arriving at the missing features of Nakayama, because such combination would have resulted in providing insights into this textual information in databases that otherwise would be impossible to determine in an accurate or concise fashion (Jayaraman, para. [0234]-[0235]).
3. Claim 10 is rejected under 35 U.S.C. 103 as being unpatentable over Nakayama in view of Kumar et al US 7,899,871 B1 (“Kumar”)
Per claim 10, Nakayama discloses the method of claim 8,
Nakamura does not explicitly disclose wherein generating the plurality of data vectors comprises determining a term frequency-inverse document frequency (TF-IDF) of each of the text segments
However, this feature is taught by Kumar (col. 21, ln 52-67)
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 Kumar with the method of Nakayama in arriving at the missing features of Nakayama, because such combination would have resulted in determining unique characteristics of the text segments (Kumar, col. 21, ln 52-67).
4. Claims 14 is rejected under 35 U.S.C. 103 as being unpatentable over Nakayama in view of Ben-Artzi et al US 8,423,551 B1 (“Ben-Artzi”)
Per claim 14, Nakayama discloses the method of claim 11, wherein the training data set comprises a plurality of additional text segments (para. [0044]; para. [0081]),
Nakamura does not explicitly disclose wherein each of the additional text segments represents a respective additional user's satisfaction with the one or more products or services
However, this feature is taught by Ben-Artzi (col. 3, ln 15-22)
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 Ben-Artzi with the method of Nakayama in arriving at the missing features of Nakayama, because such combination would have resulted in discovering topics for use in improving/determining public perception and/or improving a product, service or public relations (Ben-Artzi, col. 3, ln 15-22; col. 8, ln 2-6)
5. Claims 12 and 13 are rejected under 35 U.S.C. 103 as being unpatentable over Nakayama in view of Cavestro et al US 2008/0027893 A1 (“Cavestro” - IDS)
Per claim 12, Nakayama discloses the method of claim 11,
Nakamura does not explicitly disclose wherein each of the text segments is received in response to a user satisfaction survey regarding the one or more products or services
However, this feature is taught by Cavestro (para. [0020])
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 Cavestro with the method of Nakayama in arriving at the missing features of Nakayama, because such combination would have resulted in enriching structured and/or unstructured data in a database thereby permits subsequent analysis of the data using common information analysis techniques (Cavestro, para. [0018]).
Per claim 13, Nakayama discloses the method of claim 11,
Nakamura does not explicitly disclose wherein the user satisfaction survey comprises: a first prompt for a numerical score representing a user's satisfaction with the one or more products or services, and a second prompt for textual input
However, this feature is taught by Cavestro (para. [0034]; Table 3)
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 Cavestro with the method of Nakayama in arriving at the missing features of Nakayama, because such combination would have resulted in enriching structured and/or unstructured data in a database thereby permits subsequent analysis of the data using common information analysis techniques (Cavestro, para. [0018]).
6. Claims 15 and 16 are rejected under 35 U.S.C. 103 as being unpatentable over Nakayama in view of Keng et al US 2015/0127653 A1 (“Keng” - IDS)
Per claim 15, Nakayama discloses the method of claim 1,
Nakamura does not explicitly disclose wherein each of the text segments represents respective first user's social media content.
However, this feature is taught by Keng (para. [0034]; para. [0036])
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 Keng with the method of Nakayama in arriving at the missing features of Nakayama, because such combination would have resulted in implementing strategies to target specific needs of individual "segments" (Keng, para. [0025]).
Per claim 16, Nakayama in view of Keng discloses the method of claim 15,
Keng discloses wherein the training data set comprises a plurality of additional text segments, wherein each of the additional text segments represents a respective additional user's social media content (Abstract; para. [0034])
7. Claims 7, 17 and 18 are rejected under 35 U.S.C. 103 as being unpatentable over Nakayama in view of Ulain et al US 2019/0156946 A1 (“Ulain” - IDS)
Per claim 7, Nakayama discloses the method of claim 1,
Nakamura does not explicitly disclose wherein clustering the plurality of data vectors into the one or more clusters comprises: clustering the plurality of data structures based on similarities between the plurality of data structures using non-negative matrix factorization
However, this feature is taught by Ulain (Abstract; para. [0021]; para. [0035])
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 Ulain with the method of Nakayama in arriving at the missing features of Nakayama, because such combination would have resulted in improving clinical trial processes (Ulain, para. [0011])
Per claim 17, Nakayama discloses the method of claim 1,
Nakamura does not explicitly disclose wherein each of the text segments represents respective electronic medical record
However, this feature is taught by Ulain (para. [0012]; para. [0019]-[0023])
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 Ulain with the method of Nakayama in arriving at the missing features of Nakayama, because such combination would have resulted in improving clinical trial processes (Ulain, para. [0011])
Per claim 18, Nakayama in view of Ulain discloses the method of claim 17,
Ulain discloses wherein the training data set comprises a plurality of additional text segments, wherein each of the additional text segments represents a respective additional electronic medical record (para. [0012]; para. [0023]).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. See PTO 892 form.
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.
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, Richemond Dorvil can be reached at 571-272-7602. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/OLUJIMI A ADESANYA/Primary Examiner, Art Unit 2658