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 .
This communication is in response to Amendment filed on November 19, 2025. Claims 1, 2, and 4-14 are pending. Claims 1, 2, 4-8, 10 and 11 are amended. Claims 12-14 are newly added.
Response to Arguments
Referring to the 35 USC 112(b) rejections of claims 4, 6, and 7, Applicant’s amendments to the claims are acknowledged. However, the amendments to the claims now raise new 35 USC 112(b) issues as addressed below.
Referring to the 35 USC 101 rejection of claims 1-11, as amended, Applicant’s arguments are acknowledged, however are not found persuasive. Applicant argues that the claims are patent eligible under 101 because they recite a practical application for the judicial exception, specifically, that the claims recite the added limitation of ‘displaying the data classified into a same cluster in a different manner from the data classified in other clusters’. However, Examiner submits that the mere display of cluster data in a different manner than data displayed in other clusters is considered insignificant extra-solution activity similar to Electric Power Group, LLC v. Alstom S.A. (Selecting information, based on types of information and availability of information in a power-grid environment, for collection, analysis and display) and as such, does not recite significantly more than the judicial exception. Furthermore, the displaying of stored information within the cluster is a well understood, routine and conventional function similar to Versata Dev. Group, Inc. v. SAP Am., Inc. (Storing and retrieving information in memory) and OIP Techs.
Claims 1, 8 and 10 have been further amended to incorporate the limitations previously recited in claim 3, which were also rejected under 101.
As such, the claims, as amended, remain rejected under 101 for the reasons addressed above and further in view of the new grounds of rejection.
Applicant’s arguments with respect to claims 1, 2, and 4-14, as amended, have been considered but are moot in view of the new grounds of rejection.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 9/17/2025 is being considered by the examiner.
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, 2 and 4-14 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Claims 1, 8 and 10 recite generate a plurality of clusters by classifying data to be labeled through unsupervised learning; search for common points of the data included in each generated cluster; and output information on the searched common points for each cluster, wherein the one or more processors is further configured to execute the instructions to: extract features based on sensor values included in the data to be labeled; calculate contribution of the sensor values to the features, wherein a relationship between the sensor values of the data to be labeled and the features is expressed in a linear form, and a weight of the sensor values included in the linear form is considered as the contribution; output the sensor value with a highest contribution as a common point; and display the data classified into a same cluster in a different manner from the data classified in other clusters.
The limitations of generating a plurality of clusters of data by classifying data, and the searching, outputting and extracting steps as drafted, are processes that, under their broadest reasonable interpretation, cover performance of the limitations in the mind but for the recitation of generic computer components. That is, other than reciting “one or more processors” (in claim 1) nothing in the claim elements precludes the steps from practically being performed in the mind. For example, but for the “by one or more processors’ language (claim 1), generating clusters by classifying data, searching for common points, outputting information on the searched common points, extracting features based on sensor values included in the data to be labeled, and outputting the sensor value with a highest contribution as a common point, in the context of these claims encompasses the user performing the generating, searching, extracting and outputting the results mentally or using pen and paper.
The calculation of contribution of the sensor values to the features, wherein a relationship between the sensor values of the data to be labeled and the features is expressed in a linear form, and a weight of the sensor values included in the linear form is considered as the contribution limitation is considered a mathematical calculation and falls under the “mathematical concepts” category of abstract ideas. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind or includes mathematical calculation but for the recitation of generic computer components, then it falls within the “Mental Processes’ and ‘Mathematical Concepts’ groupings of abstract ideas. Accordingly, the claims recite an abstract idea.
This judicial exception is not integrated into a practical application. The claims recite the additional step of displaying the data classified into a same cluster in a different manner from the data classified in other clusters.
The displaying step is recited at a high level of generality (i.e. as a general means of displaying data in clusters in a different formats) and considered as insignificant extra-solution activity.
The combination of this additional step is no more than mere instructions to apply the exception using generic computer components (i.e. the one or more processors). Accordingly, even in combination, this additional step does not integrate the abstract idea into a practical application because it does not impose 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. As discussed above with respect to integration of the abstract idea into a practical application, the one or more processors is a generic computer processor which performs the displaying step. Furthermore, this function is similar to those found by the courts to be well- understood, routine, and conventional when claimed in a merely generic manner (e.g., at a high level of generality) or as insignificant extra-solution activity, namely the displaying step is similar to Electric Power Group, LLC v. Alstom S.A. (selecting information, based on types of information and availability of information in a power-grid environment, for collection, analysis and display).
As such, the displaying step is well understood, routine and conventional activity performed by generic computer components. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. The claims are not patent eligible.
Claims 2, 5, 6, 7, 9, and 11 depend from claims 1, 8 and 10 and thus include all the limitations of claims 1, 8 and 10. Therefore claims 2, 5, 6, 7, 9, and 11 recite the same abstract idea of "mental process" and “mathematical concepts”.
Claims 2, 5, 6, 7, 9, and 11 furthermore recite: [Claims 2, 9 and 11] extract features of each data included in the generated clusters; and search for the common points of the features extracted for each data within the cluster; [Claim 5] label and output information indicating the common point searched within each cluster for the data to be labeled; and [Claim 6] output multiple common points searched within each cluster according to a degree of commonality; and [Claim 7] output the common points with a highest degree of commonality as labeling candidates in a ranking format up to a predetermined rank, which are mental steps that can also be performed in the human mind.
Claims 2, 5, 6, 7, 9, and 11 do not include any additional elements that would integrate the judicial exception into a practical application or teach significantly more than the judicial exception. As such, claims 2, 5, 6, 7, 9, and 11 are not patent eligible.
Claim 4 depends from claim 3 and thus includes all the limitations of claim 3, therefore claim 4 recites the same idea of "mental process" and “mathematical concepts”.
This judicial exception is not integrated into a practical application. Claim 4 furthermore recites: displaying the contribution of each sensor within each cluster.
The displaying step is recited at a high level of generality (i.e. as a general means of displaying calculated data and is considered insignificant extra-solution activity similar to Electric Power Group, LLC v. Alstom S.A. (Selecting information, based on types of information and availability of information in a power-grid environment, for collection, analysis and display) and as such, does not recite significantly more than the judicial exception.
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. Furthermore, the displaying of stored information within the cluster is a well understood, routine and conventional function similar to Versata Dev. Group, Inc. v. SAP Am., Inc. (Storing and retrieving information in memory) and OIP Techs. As such, claim 4 is not patent eligible.
Claims 12-14 depend from claims 1, 8 and 10 and thus include all the limitations of claims 1, 8 and 10, therefore claims 12-14 recite the same abstract idea of “mental process” and “mathematical concepts”.
This judicial exception is not integrated into a practical application. Claims 12-14 furthermore recite: that: the displaying the data classified into the same cluster in the different manner from the data classified in other clusters comprises displaying the data in at least one of different colors or different symbols.
This displaying limitation is recited at a high level of generality and is considered insignificant extra-solution activity as addressed in the displaying step in claims 1, 8 and 10, from which the claims depend.
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As addressed in claims 1,8 and 10, the displaying step is similar to Electric Power Group, LLC v. Alstom S.A. (selecting information, based on types of information and availability of information in a power-grid environment, for collection, analysis and display).
As such, the displaying step is well understood, routine and conventional activity performed by generic computer components. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. The claims are not patent eligible.
To expedite a complete examination of the instant application, the claims rejected under 35 U.S.C. 101 (nonstatutory} above are further rejected as set forth below in anticipation of applicant amending these claims to place them within the four statutory categories of the invention.
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, 2 and 4-14 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 applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claims 1, 8 and 10 recite the limitation "display the data classified into a same cluster" in lines 12, 11 and 12 of the respective claims. There is insufficient antecedent basis for this limitation in the claims. It is unclear as to the distinction of the term ‘same cluster’ over the term ‘other clusters’ found in the limitation.
Claims 1, 8 and 10 recite the limitation "display.. the data classified in other clusters" in lines 12-13, 11-12 and 12-13 of the respective claims. There is insufficient antecedent basis for this limitation in the claims. It is unclear as to the distinction of the term ‘same cluster’ over the term ‘other clusters’ found in the limitation.
All claims depending from the aforenoted claims are also rejected by virtue of their dependencies.
Due to the 35 USC 112 rejections, the claims have been examined as best understood by the Examiner.
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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 1, 2, and 4-14 are rejected under 35 U.S.C. 103 as being unpatentable over US Patent 8,423,551 issued to Ben-Artzi et al (hereafter Ben-Artzi), in view of US Patent 8,543,577 issued to Ben-Artzi et al (hereafter Ben-Artzi ‘577), in view of US 2022/0389282 by Orbach et al (hereafter Orbach), and further in view of US 2022/0037033 by Srinivasan et al (hereafter Srinivasan).
Referring to claim 1, Ben-Artzi discloses a labeling assistance system [data analysis system, Fig 1, 5] comprising:
a memory storing instructions [memory 520, Fig 5]; and
one or more processors configured to execute the instructions [processor 510, Fig 5] to:
generate a plurality of clusters by classifying data to be labeled [wherein clusters of resources are formed to identify topics included in the set of relevant resources, Fig 3, element 304, col. 5, lines 62-64];
search for common points of the data included in each generated cluster [each cluster is analyzed to identify a topic associated with the cluster, Fig 3, element 304, col 6, lines 5-17; metrics such as the number of resources associated with a topic are determined by summing the number of resources in each cluster that have a common label, wherein multiple clusters can be associated with a single topic, col. 4, lines 15-25; Fig 4, element 410]; and
output information on the searched common points for each cluster [report is prepared for display based on the determined metrics, Fig 3, element 310; Fig 4, element 410, 416], wherein the one or more processors is further configured to execute the instructions to:
extract features based on values included in the data to be labeled [metric such as a frequency (number of resources included in each topic) associated with each topic is determined by counting the number of resources in each topic/cluster, col. 7, lines 29-32, Fig 4, element 410; metrics determined also include average number of page views, relative importance and average sentiment score, Fig 4, elements 411-414 and corresponding portions of specification];
calculate contribution of the values to the features, wherein a relationship between the values of the data to be labeled and the features is expressed, and a weight of the values is considered as the contribution [wherein a cluster analysis engine 112 can use algorithms to analyze the resources contained in each cluster and generate a label for the cluster, the algorithms including a term frequency-inverse document frequency (TF-IDF) algorithm can be applied to determine the word, phrase or combination of topic feature(s) that have the highest term frequency-inverse document frequency scores and use the topic feature(s) are the label for each cluster, col. 4, lines 1-8; Examiner submits that the (TF-IDF) algorithm is a weighted calculation and as such reads on the claimed: weight of the values included as the contribution]; and
output the value with a highest contribution as a common point [the highest term frequency-inverse document frequency scored topic feature is used as the label for each cluster, col. 4, lines 1-8].
Referring to claim 1, while Ben-Artzi discloses all of the above claimed subject matter and also discloses that various clustering algorithms can be used by clustering engine 110 [col. 3, lines 22-23], it remains silent as to: the clustering being performed through unsupervised learning; that the data to be labeled includes sensor data; that the TF-IDF algorithm is expressed specifically in a linear form, and displaying the data classified into a same cluster in a different manner from the data classified in other clusters.
Ben-Artzi ‘577 discloses applying an unsupervised clustering model to retrieved documents [Fig 3A, element 308, col. 10, lines 4-18].
Ben Artzi and Ben-Artzi ‘577 are analogous art because they are directed to the same field of endeavor- clustering of data. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the cluster algorithm used by Ben-Artzi to include the unsupervised clustering model of Ben-Artzi ‘577 because it would achieve predictable results.
The ordinary skilled artisan would have been motivated to make this modification because the unsupervised clustering model of Ben-Artzi ‘577 further refines the clustering algorithm used by the clustering engine of Ben-Artzi.
Still referring to claim 1, while Ben-Artzi/Ben-Artzi ‘577 discloses all of the above claimed subject matter and also discloses that various clustering algorithms can be used by clustering engine 110 [Ben-Artzi col. 3, lines 22-23], it remains silent as to: the data to be labeled including sensor data; that the TF-IDF algorithm is expressed specifically in a linear form, and displaying the data classified into a same cluster in a different manner from the data classified in other clusters.
Srinivasan discloses a DENSVAR clustering algorithm [para 4] that generates clusters of data based on monitoring camera feeds provided or input to the user devices to indicate density of traffic [para 42]. Srinivasan also discloses that data input to the clustering device 110 could include temperature or weather data generated by various sensors, with each sensor being associated with a particular geographic region [para 47]. Srinivasan furthermore discloses that the weather data is used to generate weather condition clusters presented via a geographic map, wherein the weather density clusters may be used to represent different weather features related to each cluster (e.g. one color for a cluster associated with clear weather and a different color for clusters related to stores, etc.) [para 47].
Ben Artzi, Ben-Artzi ‘577 and Srinivasan are analogous art because they are directed to the same field of endeavor- clustering of data. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the cluster algorithm used by Ben-Artzi to include the DENSVAR clustering algorithm of Srinivasan because it would achieve predictable results.
The ordinary skilled artisan would have been motivated to make this modification because the DENSVAR clustering algorithm of Srinivasan further refines the clustering algorithm used by the clustering engine of Ben-Artzi.
Furthermore with respect to claim 1, while Ben-Artzi/Ben-Artzi ‘577/Srinivasan discloses all of the above claimed subject matter and also discloses using a TF-IDF algorithm (which is a calculation of a weight score) as a clustering algorithm that can be used by clustering engine 110 [Ben-Artzi, wherein a cluster analysis engine 112 can use algorithms to analyze the resources contained in each cluster and generate a label for the cluster, the algorithms including a term frequency-inverse document frequency (TF-IDF) algorithm can be applied to determine the word, phrase or combination of topic feature(s) that have the highest term frequency-inverse document frequency scores and use the topic feature(s) are the label for each cluster, col. 4, lines 1-8; Examiner submits that the (TF-IDF) algorithm is a weighted calculation and as such reads on the claimed: weight of the values included as the contribution]. However, it remains silent as to the TF-IDF algorithm being expressed specifically in a linear form.
Orbach discloses calculation of a TD-IDF score value which is a weight 230A that is expressed in a formula linear format as seen in Eq. 1 [see para 73-77].
Ben Artzi, Ben-Artzi ‘577, Srinivasan and Orbach are analogous art because they are directed to the same field of endeavor- clustering of data. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the TF-IDF algorithm of Ben-Artzi to include expressing the TF-IDF relationship in a linear format because it would achieve predictable results.
The ordinary skilled artisan would have been motivated to make this modification because the expression of the TF-IDF score in a linear form in Orbach provides further refinement to the TF-IDF algorithm used by Ben-Artzi.
Referring to claim 8, the limitations of the claim are similar to those of claim 1 in the form of a method [Ben-Artzi, Abstract]. As such, claim 8 is rejected for the same reasons as claim 1.
Referring to claim 10, the limitations of the claim are similar to those of claim 1 in the form of a computer readable medium storing a program [Ben-Artzi, memory 520, storage device 530, Fig 5]. As such, claim 10 is rejected for the same reasons as claim 1.
Referring to claims 2, 9, and 11, Ben-Artzi/ Ben-Artzi ‘577/Srinivasan/Orbach discloses extract features of each data included in the generated clusters; and search for the common points of the features extracted for each data within the cluster [metrics are determined that are associated with each topic of each cluster, Fig 4, element 410-414 and corresponding portions of specification; metrics include the number of resources (frequency) associated with a topic are determined by summing the number of resources in each cluster that have a common label, wherein multiple clusters can be associated with a single topic, col. 4, lines 15-25].
Referring to claim 4, Ben-Artzi/ Ben-Artzi ‘577/Srinivasan/Orbach discloses displaying the contribution of each sensor within each cluster [Ben-Artzi, Fig 4, element 416; see Fig 2; Srinivasan, each sensor is associated with a particular geographic location, para 47].
Referring to claim 5, Ben-Artzi/ Ben-Artzi ‘577/Srinivasan/Orbach discloses labeling and outputting information indicating the common point searched within each cluster for the data to be labeled [Ben-Artzi, label is generated for the cluster, wherein topic features with the highest term frequency-inverse document frequency scores are used as the label for each cluster, col. 4, lines 1-8].
Referring to claim 6, Ben-Artzi/ Ben-Artzi ‘577/Srinivasan/Orbach discloses outputting multiple common points searched within each cluster according to a degree of commonality [Ben-Artzi, topic features with the highest term frequency-inverse document frequency scores are used as the label for each cluster, col. 4, lines 1-8].
Referring to claim 7, Ben-Artzi/ Ben-Artzi ‘577/Srinivasan/Orbach discloses outputting the common points with the highest degree of commonality as labeling candidates in a ranking format up to a predetermined rank [Ben-Artzi, Ben-Artzi, topic features with the highest term frequency-inverse document frequency scores are used as the label for each cluster, col. 4, lines 1-8].
Referring to claims 12-14, Ben-Artzi/ Ben-Artzi ‘577/Srinivasan/Orbach discloses that the displaying the data classified into the same cluster in the different manner from the data classified in other clusters comprises displaying the data in at least one of different colors or different symbols [Srinivasan, weather density clusters may be used to represent different weather features related to each cluster (e.g. one color for a cluster associated with clear weather and a different color for clusters related to stores, etc., para 47].
Conclusion
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 CHERYL M SHECHTMAN whose telephone number is (571)272-4018. The examiner can normally be reached on M-F: 10am-6:30pm.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Amy Ng can be reached on 571-270-1698. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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CHERYL M SHECHTMANPatent Examiner
Art Unit 2164
/C.M.S/
/MARK E HERSHLEY/Primary Examiner, Art Unit 2164