Notice of Pre-AIA or AIA Status
1. 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
2. This Office Action is in response to the filing with the office dated 01/07/2026.
Claims 1, 6, 7 and 8 have been amended. Claims 2 ,3, 4 and 5 have been cancelled. Claims 1, 7 and 8 are independent claims. Claims 1, 6, 7 and 8 are presented for examination.
Priority
3. Applicant’s claim for the benefit of a prior-filed Japanese Patent Application No. 2024-022680 filed on 02/19/2024 is acknowledged by the examiner.
Response to amendment/arguments
4. Applicant’s amendment with respect to the claims 1-7 under 35 U.S.C. § 112(f) have been fully considered. As a result the claim interpretation has been withdrawn.
5. Applicant’s arguments with respect to the rejection of claims under 35 U.S.C. § 101 as 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, have been fully considered. However, Examiner respectfully disagrees with the applicant’s argument. See response to arguments section. The rejection has been maintained.
6. Applicant’s arguments with respect to the rejection of claims under 35 U.S.C. § 102 (a)(i) and 103(a) have been fully considered but are moot in view of the new grounds of rejection, thus necessitated the new ground of rejection as 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).
Response to Arguments regarding 101 rejection
7. Regarding Applicant’s arguments on page 7 regarding claim 1 states “Even if the claims were directed to an abstract idea, they integrate it into a practical application as they provide an improvement to computer functionality by enabling simultaneous visualization of three types of clustering relationships (temporal, inter-query relevance, inter- cluster relevance) in a single integrated display. The specification explains that "visualization of search queries in time series makes it possible to use the search queries for review of customer journey analysis" and that "generating the plurality of clusters and description content in time series enables appropriate visualization of classification results." This represents a specific improvement in how computers visualize multi-dimensional clustering data-a technical solution to the technical problem of representing complex multi-factor relationships in a comprehensible visual format”.
Examiner respectfully disagrees with the applicant because, clustering different types and displaying using a computer, covers performance of the limitation in the mind but for the recitation of generic computer components. There is, nothing in the claim element precludes the steps from practically being performed by a human mentally or with pen and paper. Additionally, the mere nominal recitation of a generic computer components, or a programmed computer or a generic display to cluster in multiple steps does not take the claim limitation out of the mental processes grouping. These limitations, at the high level of generality as drafted, would encompass a user to cluster the queries which are relevant to the search query with in a time period and then output only the clusters that are highly relevant, which is mentally performable as an evaluation or judgement. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
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.
8. Claims 1-8 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more. Determining whether claims are statutory under 35 U.S.C. 101 involves a two-step analysis. Step 1 requires a determination of whether the claims are directed to the statutory categories of invention. Step 2 requires a determination of whether the claims are directed to a judicial exception without significantly more. Step 2 is divided into two prongs, with the first prong having a part 1 and part 2. See MPEP 2106; See 2019 Revised Patent Subject Matter Eligibility Guidance (2019 PEG).
Pursuant to Step 1, claims 1, 6 recites a processing device which are directed to the statutory category of a machine. Claim 8 recites a non-transitory computer readable storage medium, which are directed to a manufacture.
Pursuant to Step 2A, part 1, claims are analyzed to determine whether they are directed to an abstract idea. Under the 2019 PEG, claims are deemed to be directed to an abstract idea if they fall within one of the enumerated categories of (a) mathematical concepts, (b) certain methods of organizing human activity, and (c) mental processes. Here, claims 1, 7 and 8 are directed to an abstract idea categorized under mental processes. Courts consider a mental process if it “can be performed in the human mind, or by a human using a pen and paper.” MPEP 2016(a)(2)(III). Courts also consider a mental process as one that can be performed in the human mind and is merely using a computer as a tool to perform the concept. MPEP 2016(a)(2)(III)(C)(3). Claim 1 recites the information processing device comprising: a specifying unit that specifies search queries of a plurality of users who performs a predetermined action; a processor that generates classification of the search queries and description content of each of a plurality of clusters by inputting, to a model having learned to output a character string following a character string input to the model, information regarding the search queries specified by the specifying unit and a directive, the directive for clustering the search queries into the plurality of clusters in consideration of relevance among search queries and outputting description content describing each of the clusters on a basis of search queries classified into each of the clusters; Performs clustering; output the results. Claim 1 recites, “specifies search queries …”, “generating…”, “performing…”, “output…” are recited at a high level of generality and do not place meaningful limits on the abstract idea which is a task that can be performed by a human with the use of the computer as a tool. These limitations are essentially steps of generating and manipulating data at a high level of generality, which can be performed by a person using a computer as a tool.
Pursuant to Step 2A, part 2, claims are analyzed to determine whether the recited abstract idea is integrated into a practical application. In this case, as explained above, claims 1, 7 and 8merely recite a mental process. The limitations “generating…” is amental process. While claims 1, 7 and 8 recite additional components in the form of “device”, “processor”, these components are recited at a high level of generality, which do not add meaningful limits on the recited abstract idea to integrate it into a practical application by providing an improvement to the functioning of a computer or technology, implementing the abstract idea with a particular machine or manufacture that is integral to the claim, effecting a transformation or reduction of a particular article to a different state or thing, nor applying the abstract idea in some meaningful way beyond linking its use to computer technology. See 2019 PEG. The additional elements “specifies search queries …”, “output…”,”model”, amount to mere data gathering steps which are insignificant extra-solution activity. Combination of these additional elements is no more than mere instructions to apply the exception using series of steps and outputting the result of the mental process. Accordingly, even in combination, the additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea.
Pursuant to Step 2B, claims are analyzed to determine whether they recite significantly more than the abstract idea. In other words, it is determined whether the claims provide an inventive concept. In this case, claims 1, 7 and 8 do not recite limitations that amount to significantly more than the abstract idea. The limitations are steps involving processes that can be practically performed by a human with the aid of pen and paper, or as explained above, using a computer as a tool to perform the concept. For example, a The “specifies search queries …”, “generating…”, “output…” elements that were identified as insignificant extra-solution activity as mere data gathering when re-evaluated still does not provide significantly more. Considering the additional elements in combination and the claim as a whole does not change the analysis, and does not amount to significantly more. Thus the claims are abstract.
Claim 6 recites, “…inputs, to the model, as the directive…” is an additional elements/ insignificant extra-solution activity of a data gathering process. is a process, that under broadest reasonable interpretation, covers performance of the limitation in the mind. There is, nothing in the claim element precludes the steps from practically being performed by a human mentally or with pen and paper and likewise do not provide "significantly more" than the abstract idea for similar reasons as the independent claim.
Response to Arguments regarding 103 rejection
9. Regarding Applicant’s arguments on page 7 regarding claim 1 states “The present claims recite (1) a trained machine learning model with character string learning capability that generates description content; (2) a directive for multi-factor clustering with inter-cluster relationship analysis; and (3) time-series visualization with superimposed description content, none of which is taught or suggested by the cited documents”.
Examiner respectfully disagrees with the applicant and maintains the rejection as TSUBOUCHI et al teaches, “(1) a trained machine learning model with character string learning capability that generates description content” (Paragraph [0122] discloses, categorizing/ classifying of search queries and grouping/ clustering the queries within each time period by a trained model which is taught in Paragraph [0201]),
“(2) a directive for multi-factor clustering with inter-cluster relationship analysis” (Paragraph [0036], [0043] discloses, grouping the queries into groups based on different search queries such as “hospital stay”, “ritual visit”, and “weight” as the search queries having high relativity with the reference query “newborn baby” one month before the reference time and date and are positioned based on the similarity score.
TSUBOUCHI et al fails to explicitly teach, “(3) time-series visualization with superimposed description content”.
OZAKI et al teaches, “(3) time-series visualization with superimposed description content” (Paragraph [0008] discloses, displaying the plurality of clusters and description content/ group ID by transmitting the description content/ group ID according to relative search dates and time with respect to the reference date and time. Also see [0049]).
Therefore TSUBOUCHI et al in view of OZAKI et al teach, the argued limitation and the rejection is maintained.
Claim Rejections - 35 U.S.C. § 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.
10. Claims 1, 7 and 8 are rejected under 35 U.S.C. 103 as being unpatentable over TSUBOUCHI; Kota (US 20210357416 A1) in view of OZAKI; Ryota (US 20200201858 A1).
Regarding independent claim 1, TSUBOUCHI; Kota (US 20210357416 A1) teaches, an(Paragraph [0029] that the operator OP wants to specify the needs of such users from the history of the search queries of the users who have input the search query “newborn baby” at certain time and date. For example, it is conceivable that the operator OP wants to know what search query(is) has been input around (for example, 30 to 60 days after) reference time and date by the user who has input the search query “newborn baby” at the reference time and date (for example, 2020 Mar. 19). Also see [0035]-[0036] discloses, generating a list of search queries based on the relevance of the search queries);
the processor further configured to generate classification of the search queries and description content of each of a plurality of clusters by inputting, to a trained machine learning model having learned to output a character string following a character string input to the model (Paragraph [0122] discloses, categorizing/ classifying of search queries and grouping/ clustering the queries within each time period. Also see [0201], [0236]), information regarding the search queries specified by the processor and a directive, the directive for clustering the search queries into the plurality of clusters in consideration of time series indicated by the time-series information and relevance among the search queries indicated by the relevance information, (Paragraph [0007] According to one aspect of the subject matter described in this disclosure, a determination device includes (ii) a categorization unit configured to categorize the search queries input in a predetermined period among the search queries into a plurality of categories), performing clustering in consideration of relevance among the clusters that have been classified such that clusters having related purposes of action are positioned proximate to each other, (Paragraph [0036], [0043] discloses, grouping the queries into groups based on different search queries such as “hospital stay”, “ritual visit”, and “weight” as the search queries having high relativity with the reference query “newborn baby” one month before the reference time and date and are positioned based on the similarity score (see [0039]) ((based on specification Paragraph [0023] when the purpose of action is set to “skiing”, the information processing device 100 specifies “ski wear” and “poles” related to this purpose of action (for example, supplies for skiing) as those having relevance. Therefore, Examiner interprets “hospital stay”, “ritual visit”, and “weight” as related purpose of action having relevance to the reference query “newborn baby”). Also see [0143]-[0145]), and outputting description content describing each of the clusters on a basis of the search queries classified into each of the clusters (Paragraph [0080] displays different categories/ clusters based on the classified search queries).
TSUBOUCHI et al fails to explicitly teach, and the processor further configured to provide information for displaying the plurality of clusters clustered by the trained machine learning model and the description content by transmitting information for superimposing and displaying each piece of description content in association with its respective cluster on a time-series visualization graph that positions each cluster according to relative search dates and times with respect to the reference date and time.
OZAKI; Ryota (US 20200201858 A1) teaches, and the processor further configured to provide information for displaying the plurality of clusters clustered by the trained machine learning model and the description content by transmitting information for superimposing and displaying each piece of description content in association with its respective cluster on a time-series visualization graph that positions each cluster according to relative search dates and times with respect to the reference date and time (Paragraph [0008] discloses, displaying the plurality of clusters and description content/ group ID by transmitting the description content/ group ID according to relative search dates and time with respect to the reference date and time. Also see [0049]);
OZAKI et al also teaches, and outputting description content describing each of the clusters on a basis of the search queries classified into each of the clusters (Paragraph [0050] output the search events based on the group/ cluster relevance or the integration relevance).
Therefore it would have been obvious to one of the ordinary skill in the art before the effective filing date of the claimed invention, to have modified the teachings of TSUBOUCHI et al by providing information for displaying the plurality of clusters clustered by the trained machine learning model and the description content by transmitting information for superimposing and displaying each piece of description content in association with its respective cluster on a time-series visualization graph that positions each cluster according to relative search dates and times with respect to the reference date and time, as taught by OZAKI et al (Paragraph [0008]).
One of the ordinary skill in the art would have been motivated to make this modification, by doing so, it would address the advantages of specifying search actions included in an identical search event more accurately, in a case of extracting occurred search action as an identical search event, for searching target information, as compared with a case of using only a relevance between search actions as taught by OZAKI et al (Paragraph [0006]).
Regarding independent claim 7, TSUBOUCHI; Kota (US 20210357416 A1) teaches, Anindicating time series of searches in which the search queries have been used and relevance information indicating relevance of the search queries, with a date and time when the predetermined action has been performed used as a reference date and time (Paragraph [0029] that the operator OP wants to specify the needs of such users from the history of the search queries of the users who have input the search query “newborn baby” at certain time and date. For example, it is conceivable that the operator OP wants to know what search query(is) has been input around (for example, 30 to 60 days after) reference time and date by the user who has input the search query “newborn baby” at the reference time and date (for example, 2020 Mar. 19). Also see [0035]-[0036] discloses, generating a list of search queries based on the relevance of the search queries).
generating, by the processor, classification of the search queries and description content of each of a plurality of clusters by inputting, to a trained machine learning model having learned to output a character string following a character string input to the model (Paragraph [0122] discloses, categorizing/ classifying of search queries and grouping/ clustering the queries within each time period. Also see [0201]), information regarding the search queries specified in the specifying step and a directive, the directive for clustering the search queries into the plurality of clusters in consideration of(Paragraph [0007] According to one aspect of the subject matter described in this disclosure, a determination device includes (ii) a categorization unit configured to categorize the search queries input in a predetermined period among the search queries into a plurality of categories), performing clustering in consideration of relevance among the clusters that have been classified such that clusters having related purposes of action are positioned proximate to each other(Paragraph [0036], [0043] discloses, grouping the queries into groups based on different search queries such as “hospital stay”, “ritual visit”, and “weight” as the search queries having high relativity with the reference query “newborn baby” one month before the reference time and date and are positioned based on the similarity score (see [0039]) ((based on specification Paragraph [0023] when the purpose of action is set to “skiing”, the information processing device 100 specifies “ski wear” and “poles” related to this purpose of action (for example, supplies for skiing) as those having relevance. Therefore, Examiner interprets “hospital stay”, “ritual visit”, and “weight” as related purpose of action having relevance to the reference query “newborn baby”). Also see [0143]-[0145]), and outputting description content describing each of the clusters on a basis of the search queries classified into each of the clusters (Paragraph [0080] displays different categories/ clusters based on the classified search queries).
TSUBOUCHI et al fails to explicitly teach, and providing, by the processor, information for displaying the plurality of clusters clustered by the trained machine learning model and the description content by transmitting information for superimposing and displaying each piece of description content in association with its respective cluster on a time-series visualization graph that positions each cluster according to relative search dates and times with respect to the reference date and time.
OZAKI; Ryota (US 20200201858 A1) teaches, and providing, by the processor, information for displaying the plurality of clusters clustered by the trained machine learning model and the description content by transmitting information for superimposing and displaying each piece of description content in association with its respective cluster on a time-series visualization graph that positions each cluster according to relative search dates and times with respect to the reference date and time (Paragraph [0008] discloses, displaying the plurality of clusters and description content/ group ID by transmitting the description content/ group ID according to relative search dates and time with respect to the reference date and time. Also see [0049]);
OZAKI et al also teaches, and outputting description content describing each of the clusters on a basis of the search queries classified into each of the clusters (Paragraph [0050] output the search events based on the group/ cluster relevance or the integration relevance).
Therefore it would have been obvious to one of the ordinary skill in the art before the effective filing date of the claimed invention, to have modified the teachings of TSUBOUCHI et al by providing information for displaying the plurality of clusters clustered by the trained machine learning model and the description content by transmitting information for superimposing and displaying each piece of description content in association with its respective cluster on a time-series visualization graph that positions each cluster according to relative search dates and times with respect to the reference date and time, as taught by OZAKI et al (Paragraph [0008]).
One of the ordinary skill in the art would have been motivated to make this modification, by doing so, it would address the advantages of specifying search actions included in an identical search event more accurately, in a case of extracting occurred search action as an identical search event, for searching target information, as compared with a case of using only a relevance between search actions as taught by OZAKI et al (Paragraph [0006]).
Regarding independent claim 8, TSUBOUCHI; Kota (US 20210357416 A1) teaches, a non-transitory computer readable storage medium having stored therein an information processing program that causes a computer to execute: specifying, by a processor, search queries of a plurality of users who performs a predetermined action and that specifies time-series information indicating time series of searches in which the search queries have been used and relevance information indicating relevance of the search queries, with a date and time when the predetermined action has been performed used as a reference date and time (Paragraph [0029] that the operator OP wants to specify the needs of such users from the history of the search queries of the users who have input the search query “newborn baby” at certain time and date. For example, it is conceivable that the operator OP wants to know what search query(is) has been input around (for example, 30 to 60 days after) reference time and date by the user who has input the search query “newborn baby” at the reference time and date (for example, 2020 Mar. 19). Also see [0035]-[0036] discloses, generating a list of search queries based on the relevance of the search queries);
generating, by the processor, classification of the search queries and description content of each of a plurality of clusters by inputting, to a trained machine learning model having learned to output a character string following a character string input to the model (Paragraph [0122] discloses, categorizing/ classifying of search queries and grouping/ clustering the queries within each time period. Also see [0201]), information regarding the search queries specified in the specifying step and a directive, the directive for clustering the search queries into the plurality of clusters in consideration of time series indicated by the time-series information and relevance among the search queries indicated by the relevance information (Paragraph [0007] According to one aspect of the subject matter described in this disclosure, a determination device includes (ii) a categorization unit configured to categorize the search queries input in a predetermined period among the search queries into a plurality of categories), performing clustering in consideration of relevance among the clusters that have been classified such that clusters having related purposes of action are positioned proximate to each other (Paragraph [0036], [0043] discloses, grouping the queries into groups based on different search queries such as “hospital stay”, “ritual visit”, and “weight” as the search queries having high relativity with the reference query “newborn baby” one month before the reference time and date and are positioned based on the similarity score (see [0039]) ((based on specification Paragraph [0023] when the purpose of action is set to “skiing”, the information processing device 100 specifies “ski wear” and “poles” related to this purpose of action (for example, supplies for skiing) as those having relevance. Therefore, Examiner interprets “hospital stay”, “ritual visit”, and “weight” as related purpose of action having relevance to the reference query “newborn baby”). Also see [0143]-[0145]) , and outputting description content describing each of the clusters on a basis of the search queries classified into each of the clusters (Paragraph [0080] displays different categories/ clusters based on the classified search queries).
TSUBOUCHI et al fails to explicitly teach, and providing, by the processor, information for displaying the plurality of clusters clustered by the trained machine learning model and the description content by transmitting information for superimposing and displaying each piece of description content in association with its respective cluster on a time-series visualization graph that positions each cluster according to relative search dates and times with respect to the reference date and time.
OZAKI; Ryota (US 20200201858 A1) teaches, and providing, by the processor, information for displaying the plurality of clusters clustered by the trained machine learning model and the description content by transmitting information for superimposing and displaying each piece of description content in association with its respective cluster on a time-series visualization graph that positions each cluster according to relative search dates and times with respect to the reference date and time (Paragraph [0008] discloses, displaying the plurality of clusters and description content/ group ID by transmitting the description content/ group ID according to relative search dates and time with respect to the reference date and time. Also see [0049]);
OZAKI et al also teaches, and outputting description content describing each of the clusters on a basis of the search queries classified into each of the clusters (Paragraph [0050] output the search events based on the group/ cluster relevance or the integration relevance).
Therefore it would have been obvious to one of the ordinary skill in the art before the effective filing date of the claimed invention, to have modified the teachings of TSUBOUCHI et al by providing information for displaying the plurality of clusters clustered by the trained machine learning model and the description content by transmitting information for superimposing and displaying each piece of description content in association with its respective cluster on a time-series visualization graph that positions each cluster according to relative search dates and times with respect to the reference date and time, as taught by OZAKI et al (Paragraph [0008]).
One of the ordinary skill in the art would have been motivated to make this modification, by doing so, it would address the advantages of specifying search actions included in an identical search event more accurately, in a case of extracting occurred search action as an identical search event, for searching target information, as compared with a case of using only a relevance between search actions as taught by OZAKI et al (Paragraph [0006]).
11. Claim 6 are rejected under 35 U.S.C. 103 as being unpatentable over TSUBOUCHI; Kota (US 20210357416 A1) in view of OZAKI; Ryota (US 20200201858 A1)and in further view of 坪内 孝太 (JP 6990757 B1) hereafter referred as 757.
Regarding dependent claim 6, TSUBOUCHI et al and OZAKI et al teach, the information processing device according to claim 1.
TSUBOUCHI et al further teaches, wherein the processor inputs, to the trained machine learning model, as the directive, the directive instructing to perform clustering in consideration of the time series indicated by the time-series information of a period specified by a user and a predetermined period specified by the processor and relevance indicated by the relevance information (Paragraph [0007] According to one aspect of the subject matter described in this disclosure, a determination device includes (ii) a categorization unit configured to categorize the search queries input in a predetermined period among the search queries into a plurality of categories. Clustering by a trained model is taught in Paragraph [0182]),
TSUBOUCHI et al and OZAKI et al fails to explicitly teach, and, in a case where description content in the period specified by the user and description content in the predetermined period specified by the processor are different from each other in the period specified by the user, instructing to perform at least one of output of an alert or correction.
757 teaches, and, in a case where description content in the period specified by the user and description content in the predetermined period specified by the processor are different from each other in the period specified by the user, instructing to perform at least one of output of an alert or correction (Page 5, Paragraph 1, discloses, correcting the relevance degree based on the extracted search query. Also see Page 8, Paragraph 6 (related paragraphs of correction unit)).
Therefore it would have been obvious to one of the ordinary skill in the art before the effective filing date of the claimed invention, to have modified the teachings of TSUBOUCHI et al by providing and, in a case where description content in the period specified by the user and description content in the predetermined period specified by the processor are different from each other in the period specified by the user, instructing to perform at least one of output of an alert or correction, as taught by 757 (page 3, Paragraph 4, Page 7, Paragraphs 3, 4).
One of the ordinary skill in the art would have been motivated to make this modification, by doing so enhances search relevance, improves the user experience, and boosts search engine optimization (SEO) performance.
Closest Prior Art
12. The prior art made of record and not relied upon is considered pertinent to the applicant’s disclosure.
CHIGUSA; Kentarou (US 20170132638 A1) teaches, A relevant information acquisition method and apparatus include characteristic terms whereby each of the cases is extracted, and relevance among cases is detected based on the extracted characteristic terms of each of the cases and the conversation history documents of other cases. The respective cases are classified into a plurality of clusters, which are an aggregate of high relevance cases, labels assigned to the clusters and representative cases are determined, characteristic terms of the inquiry text are extracted, the cases that may become a reference are acquired based on the extracted characteristic terms and the conversation history document of each of the cases, one or more clusters to which each of the acquired cases belongs are identified, and the labels of each of the identified clusters and at least a part of the conversation history document of the representative cases are categorized and displayed (Abstract).
13. Examiner has pointed out particular references contained in the prior arts of record in the body of this action for the convenience of the applicant. Although the specified citations are representative of the teachings in the art and are applied to the specific limitations within the individual claim, other passages and Figures may apply as well. It is respectfully requested from the applicant, in preparing the response, to consider fully the entire references as potentially teaching all or part of the claimed invention, as well as the context of the passage as taught by the prior arts or disclosed by the examiner. It is noted that any citation to specific pages, columns, figures, or lines in the prior art references any interpretation of the references should not be considered to be limiting in any way. A reference is relevant for all it contains and may be relied upon for all that it would have reasonably suggested to one having ordinary skill in the art. In re Heck, 699 F.2d 1331-33, 216 USPQ 1038-39 (Fed. Cir. 1983) (quoting In re Lemelson, 397 F.2d 1006, 1009, 158 USPQ 275, 277 (CCPA 1968))).
Conclusion
Applicant’s amendments/Arguments necessitated new grounds of rejection as presented in this office action. 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 extension fee 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 date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to SUMAN RAJAPUTRA whose telephone number is (571) 272-4669. The examiner can normally be reached between 8:00 AM - 5:00 PM.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Tony Mahmoudi (571) 272-4078 can be reached. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/S. R./
Examiner, Art Unit 2163
/ALEX GOFMAN/Primary Examiner, Art Unit 2163
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