DETAILED ACTION
Remarks
This communication is in response to the amendment/arguments filed on June 17, 2026 has been fully considered. The rejection is made final. Claims 1-20 are pending for examination.
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
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.
Examiner Notes
Examiner cites particular columns and line numbers in the references as applied to the claims below 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 that, in preparing responses, the applicant fully consider the references in entirety as potentially teaching all or part of the claimed invention, as well as the context of the passage as taught by the prior art or disclosed by the examiner.
The examiner requests, in response to this Office action, supports are shown for language added to any original claims on amendment and any new claims. That is, indicate support for newly added claim language by specifically pointing to page(s) and line no(s) in the specification and/or drawing figure(s). This will assist the examiner in prosecuting the application.
When responding to this office action, Applicant is advised to clearly point out the patentable novelty which he or she thinks the claims present, in view of the state of the art disclosed by the references cited or the objections made. He or she must also show how the amendments avoid such references or objections See 37 CFR 1.111(c).
Response to Amendment
Applicant’s arguments/amendment filed on June 17, 2026, with respect to the rejection(s) of amended claim(s) 1-20 under 35 U.S.C. § 101 have been fully considered and are NOT persuasive. Therefore, the rejection has been maintained.
Response to Arguments
Applicant's arguments filed June 17, 2026 have been fully considered but they are not persuasive.
Applicant argues on page 13 that “In the present case, the claims recite "an additional element reflect[ing] an improvement in the functioning of a computer," by reciting increasing a speed of a processor providing an answer to the query associated with the unstructured information by performing a search of the structured information.”, is acknowledged but not deemed to be persuasive.
Step 2A, Prong Two Under MPEP § 2106.04(d), Integration of the Judicial Exception into a Practical Application Limitations that are indicative of integration into a practical application under MPEP § 2106.04(d) include:
Improvements to the functioning of a computer, or to any other technology or technical field - see MPEP 2106.04(d)(1), 2106.05(a);
This limitation of claim 1 here is clearly directed to performing tasks to be completed upon the occurrence of an event and retrieving user-specified information. The focus of the claim here is not an improvement in computers as tools, but on certain independent abstract ideas that use computers as tools. The specification paragraphs [0057] and [0058] purports an improvement that using a conventional mechanism, but the claims do not focus on how that mechanism leads to an improvement in the technology of the computer system. The specification does not distinguish between the invention and conventional solutions. The specification contains no disclosure of an improvement to the functionality of the computer itself. The focus should be on improving the tool (i.e., computer), rather than merely showing that an abstract idea is beneficially improvement on a computer. The claims should be directed to that improvement. In Enfish court asked whether the focus of the claims is on the specific asserted improvement in computer capabilities (i.e., the self-referential table for a computer database), or instead on a process that qualifies as an “abstract idea” for which computers are invoked merely as a tool. Therefore, the present claims are directed to patent ineligible subject matter.
Applicant argues on pages 11-12 that “a two-step process, using an artificial intelligence and the input, wherein the two-step process includes performing the task associated with the multiple records to obtain multiple outputs and structuring the multiple records as a consequence of performing the task”, is acknowledged but not deemed to be persuasive.
Shukla [0005], [0034] discloses a two step process of generating an annotated structured dataset from unstructured patient data of a plurality of patients. … classifying selected unclassified unstructured text fragments according to the medical classification term, and classifying non-selected unclassified unstructured text fragments as not satisfying the medical classification term, and iterating the searching, and/or the presenting of the subset, until no unclassified unstructured text fragments are obtained by the search engine (i.e., step one) … annotated structured dataset is created by the classification of unclassified unstructured text fragments into the medical classification term (i.e., step two). Shukla [0034-0035] discloses that the annotated structured dataset is created by the classification of unclassified unstructured text fragments into the medical classification term. The process is iterated for each medical classification term, optionally one medical classification term at a time. The single medical classification term may include a search set of semantically similar terms with common meaning. … the annotated dataset is used as a training dataset for training an artificial intelligence model for classifying previously unseen unstructured text fragments of previously unseen patient data into one or more of the medical classification terms (i.e., structuring the multiple records as a consequence of performing the task). Shukla [0055] “the process includes steps 200 through 206. These steps are assumed to be performed by the machine learning-based troubleshooting system 112A utilizing the document parsing module 114A, the recursive information extraction module 116A, and the troubleshooting action recommendation module 118A, or by the machine learning-based document processing service 112B utilizing the document parsing module 114B, the recursive information extraction module 116B and the document summary generation module 118B”. Shukla [0057] discloses “INPUT comprises a call or chat support log, the Doc2Vec model may be trained on a set of historical call or chat support logs (e.g., 100,000 call logs)” (i.e., providing the input to the artificial intelligence). Therefore, Shukla teaches the above argued limitation of claim 1.
Claim Objections
Claim 1 is objected to because of the following informalities: recitation of “performing the task, by: performing the task by:” in lines 13-14 may needs to change to “performing the task by:”.
The recitation of “increase a speed of a processor providing an answer to the query” in line 26 is a speculation by the Applicant and is not supported by the Applicant’s specification. Therefore, “increase a speed of a processor” is not considered by the Examiner during prosecution.
Claims 8 and 14 have the similar limitation and needs to changed and considered.
Appropriate correction is required.
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 an abstract idea without significantly more.
Regarding independent Claims 1, 8, and 14:
Step 1 Analysis:
Claim 1 recites “A non-transitory computer-readable storage medium”, therefore the claim is a manufacture.
Claim 8 recites “A method…”, the claim recites a series of steps and therefore is process.
Claim 14 recites “A system …”; therefore, the claim is a machine.
Step 2A Prong One Analysis: The claim, under the broadest reasonable interpretation, recites limitations directed to an abstract idea, including mental processes – concepts performed in the human mind (including an observation, evaluation, judgment, opinion), but for the recitation of mere instructions to apply an exception language. In particular, the following limitations are directed to an abstract idea:
obtaining multiple records from a database,
wherein a record among the multiple records includes multiple properties,
wherein a property among the multiple properties includes a large document, and
wherein the large document is unstructured or semi-structured; receiving an input indicating a task to perform associated with the multiple records;
performing, a two-step process, using an artificial intelligence and the input, wherein the two-step process includes performing the task associated with the multiple records to obtain multiple outputs and structuring the multiple records as a consequence of performing the task, by: performing the task by:
providing the input to the artificial intelligence;
obtaining from the artificial intelligence the multiple outputs, wherein an output among the multiple outputs includes structured information not included in the multiple records;
structuring the multiple records as the consequence of performing the task by storing the output including the structured information not included in the multiple records as multiple particular properties associated with the multiple records;
receiving a query associated with the unstructured information; and
increasing a speed of a processor providing an answer to the query associated with the unstructured information structured information.
These limitations are a process that, under their broadest reasonable interpretation, covers performance of the limitation in the mind, but for the recitation of generic computer components. That is, other than reciting “processor” and “computer-readable medium”, nothing in the claim element precludes the steps from practically being performed in a human mind or with the aid of pen and paper. For example, the “obtaining”, “receiving”, “performing”, “providing” and “storing” in the context of this claim encompasses a user mentally, and with the aid of pen and paper writing the changes down on a sheet of paper and examine the list to determine the relevant ones. For example, a human being can gather information from various information sources such newspaper, various documents of interest etc. (i.e., obtaining records from database). A human being can look for something (i.e., input) into the gathered information and receive an output (i.e., receive input, analyze data and receive output). A human being can analyze gathered information and search for relevant information to produce and send an answer based on the relevant portion of the document.
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.
Step 2A - Prong Two: Integrated into a Practical Application
The judicial exception is not integrated into a practical application. In particular, the additional steps: the “obtaining”, “receiving”, “performing”, “providing” and “storing” steps mount to data gathering which are considered to be insignificant extra-solution activity (see MPEP 2106.05(g)), and the “perform using artificial intelligence”, “provide input to the artificial intelligence” and “obtain from the artificial intelligence” steps are considered as a mere instruction to apply an exception to perform an existing process on a generic computer and/or no more than an idea of a solution or outcome on a generic computer (see MPEP 2106.05(f)). Accordingly, 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, thus fail to integrate the abstract idea into a practical application. See MPEP 2106.05(g).
Step 2B: Claim provides an Inventive Concept
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. The insignificant extra-solution activities identified above, which include the data-gathering and the step of “obtaining”, “receiving”, “performing”, “providing” and “storing” are recognized by the courts as well-understood, routine, and conventional activities when they are claimed in a merely generic manner (e.g., at a high level of generality) or as insignificant extra-solution activity (see MPEP 2106.05(d)(II)). For these reasons, there is no inventive concept in the claim, and thus it is ineligible.
The claim does 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.
Regarding claim 2. The non-transitory, computer-readable storage medium of claim 1, comprising instructions to: receive the input indicating the task, wherein the task comprises at least one of: summarizing the record to obtain the output including a summary; extracting indicated information from the record to obtain the output including the indicated information; generating a document based on the large document to obtain the output including the document, wherein the document describing content of the record is smaller than the record; and enable access to the database based on the property, thereby enabling an efficient understanding of content of the large document without consuming the large document.
The judicial exception is not integrated into a practical application. In particular, this additional limitation mounts to data gathering which is considered to be insignificant extra solution activity (see MPEP 2106.05(g)), and does not amount to significantly more than the above-identified judicial exception.
Regarding claim 3. The non-transitory, computer-readable storage medium of claim 2, wherein the document includes a social media post.
The judicial exception is not integrated into a practical application. In particular, this additional limitation mounts to data gathering which is considered to be insignificant extra solution activity (see MPEP 2106.05(g)), and does not amount to significantly more than the above-identified judicial exception.
Regarding claim 4. The non-transitory, computer-readable storage medium of claim 1, comprising instructions to: prior to performing the task on the multiple records in the database, obtain a subset of the multiple records, wherein the subset of the multiple records includes fewer records than the multiple records; perform the task on the subset of the multiple records to obtain a subset of results, wherein performing the task on the subset of the multiple records is faster than performing the task associated with the multiple records; provide the subset of results to a user to inspect; receive from the user a modification to the input to obtain a modified input; and perform, using the artificial intelligence and the modified input, the task associated with the multiple records to obtain the output.
The judicial exception is not integrated into a practical application. In particular, this additional limitation mounts to data gathering which is considered to be insignificant extra solution activity (see MPEP 2106.05(g)), and does not amount to significantly more than the above-identified judicial exception.
Regarding claim 5. The non-transitory, computer-readable storage medium of claim 1, comprising instructions to: receive the input indicating the task, wherein the task comprises generating a document describing content of the record, and wherein the document describing the content of the record is smaller than the record; and enable access to the database based on the property, thereby enabling an efficient understanding of content of the large document without consuming the large document.
The judicial exception is not integrated into a practical application. In particular, this additional limitation mounts to data gathering which is considered to be insignificant extra solution activity (see MPEP 2106.05(g)), and does not amount to significantly more than the above-identified judicial exception.
Regarding claim 6. The non-transitory, computer-readable storage medium of claim 1, comprising instructions to: receive the input indicating the task, wherein the task includes creating a summary, extracting information from the record, or generating a document based on the large document; receive a second input including a natural language input further specifying type of information to obtain from the record; and based on the natural language input and the task, perform the task.
The judicial exception is not integrated into a practical application. In particular, this additional limitation mounts to data gathering which is considered to be insignificant extra solution activity (see MPEP 2106.05(g)), and does not amount to significantly more than the above-identified judicial exception.
Regarding claim 7. The non-transitory, computer-readable storage medium of claim 1, comprising instructions to: determine whether the record in the database is modified after the task is performed; upon determining that the record in the database is modified after the task is performed, automatically repeat a performance of the task to obtain a second output; and store the second output as the property in the database.
The judicial exception is not integrated into a practical application. In particular, this additional limitation mounts to data gathering which is considered to be insignificant extra solution activity (see MPEP 2106.05(g)), and does not amount to significantly more than the above-identified judicial exception.
Regarding claims 9-13 and 15-20, these claims have similar limitations of claims 2-7 and do not provide any additional elements that when considered individually or as an ordered combination, amount to significantly more than the abstract idea identified.
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 of this title, 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 set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied 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.
Claims 1, 2, 4-15 and 17-20 are rejected under 35 U.S.C. 103 as being unpatentable over Shukla et al. (US Patent Publication No. 2022/0082095 A1, ‘Shukla’, hereafter) in view of Barkan et al. (US Patent Publication No. 2021/0225466 A1, ‘Barkan’, hereafter).
Regarding claim 1. Shukla teaches a non-transitory, computer-readable storage medium comprising instructions recorded thereon, wherein the instructions when executed by at least one data processor of a system cause the system (a non-transitory processor-readable storage medium having stored therein program code of one or more software programs, wherein the program code when executed by at least one processing device causes the at least one processing device to perform steps of, Shukla [0089-0096]) to:
obtain multiple records from a database, wherein a record among the multiple records includes multiple properties, wherein a property among the multiple properties includes a large document, and wherein the large document is unstructured or semi-structured (Shukla [0026] discloses "The document database 108, as discussed above, is configured to store and record information relating to documents to be analyzed by the enterprise repair center 102A (e.g., by the machine learning-based troubleshooting system 112A thereof)” (i.e., obtaining multiple records from the database). “Such information may include documents themselves (e.g., tech support call and chat logs, sales documents, articles, etc.) as well as metadata associated with the documents” (i.e., records with properties). Shukla [0062] discloses “In various use case scenarios, there is a need to process large amounts of unstructured text data. Various embodiments are described below with respect to use cases in support contexts where there are significant technical challenges associated with effectively utilizing textual and/or audio transcripts (e.g., call logs, chat logs, etc.) of conversations between customers or other end-users and support agents as they work through resolving a problem”). Interactions between customers and support agents (or downstream repair technicians in the case of hardware and software troubleshooting of IT assets) may be stored word for word in an unstructured manner." (i.e., properties include a large document, and the large document is unstructured or semi-structured));
receive an input indicating a task to perform associated with the multiple records (Shukla [0024] discloses “Numerous other operating scenarios involving a wide variety of different types and arrangements of processing nodes are possible”, Shukla [0026] discloses “The document database 108, as discussed above, is configured to store and record information relating to documents to be analyzed by the enterprise repair center” (i.e., a task to performed). “Such information may include documents themselves (e.g., tech support call and chat logs, sales documents, articles, etc.) as well as metadata associated with the documents” (i.e., associated with multiple records));
perform, a two-step process, using an artificial intelligence and the input, wherein the two-step process includes performing the task associated with the multiple records to obtain multiple outputs and structuring the multiple records as a consequence of performing the task, by: performing the task by: providing the input to the artificial intelligence (Shukla [0005], [0034] discloses a two step process of generating an annotated structured dataset from unstructured patient data of a plurality of patients. … classifying selected unclassified unstructured text fragments according to the medical classification term, and classifying non-selected unclassified unstructured text fragments as not satisfying the medical classification term, and iterating the searching, and/or the presenting of the subset, until no unclassified unstructured text fragments are obtained by the search engine (i.e., step one) … annotated structured dataset is created by the classification of unclassified unstructured text fragments into the medical classification term (i.e., step two). Shukla [0034-0035] discloses that the annotated structured dataset is created by the classification of unclassified unstructured text fragments into the medical classification term. The process is iterated for each medical classification term, optionally one medical classification term at a time. The single medical classification term may include a search set of semantically similar terms with common meaning. … the annotated dataset is used as a training dataset for training an artificial intelligence model for classifying previously unseen unstructured text fragments of previously unseen patient data into one or more of the medical classification terms (i.e., structuring the multiple records as a consequence of performing the task). Shukla [0055] “the process includes steps 200 through 206. These steps are assumed to be performed by the machine learning-based troubleshooting system 112A utilizing the document parsing module 114A, the recursive information extraction module 116A, and the troubleshooting action recommendation module 118A, or by the machine learning-based document processing service 112B utilizing the document parsing module 114B, the recursive information extraction module 116B and the document summary generation module 118B”. Shukla [0057] discloses “INPUT comprises a call or chat support log, the Doc2Vec model may be trained on a set of historical call or chat support logs (e.g., 100,000 call logs)” (i.e., providing the input to the artificial intelligence));
obtaining from the artificial intelligence the multiple outputs (Shukla [0056] discloses “In each of the two or more iterations, the machine learning-based information extraction model provides as output a portion of the unstructured text data extracted from the document and a relevance score associated with the portion of the unstructured text data extracted from the document in that iteration.” (i.e., obtaining from the artificial intelligence the outputs)),
Shukla does not teach
wherein an output corresponds to a record among the multiple records,
wherein the output includes structured information not included in the multiple records; structuring the multiple records as the consequence of performing the task by storing the output including the structured information not included in the multiple records as multiple particular properties associated with the multiple records;
receive a query associated with the unstructured information; and increase a speed of a processor providing an answer to the query associated with the unstructured information by performing a search of the structured information.
However, Barkan teaches
wherein an output corresponds to a record among the multiple records (providing unstructured patient data (i.e., records out of multiple patients) as in Fig. 1, 102 create updated annotated patient data (i.e., an output corresponds to a record among the multiple records) as in Fig. Fig. 1, 116, 120), Barkan [0073-0076], [0105-0108]),
wherein the output includes structured information not included in the multiple records; structuring the multiple records as the consequence of performing the task by storing the output including the structured information not included in the multiple records as multiple particular properties associated with the multiple records (generating an annotated structured dataset from unstructured patient data of a plurality of patients. FIG. 2, which is a block diagram of a system for generating an annotated structured dataset from unstructured patient data of multiple patients (i.e., output includes structured information not included in the multiple records), Barkan [0003-0005], [0027]);
receive a query associated with the unstructured information; and increase a speed of a processor providing an answer to the query associated with the unstructured information by performing a search of the structured information (The searching, and/or the presenting of the subset of retrieved unclassified unstructured text fragments is iterated until no unclassified unstructured text fragments obtained by the search engine remain (i.e., receive a query associated with the unstructured information)… all unstructured text fragments have been classified into the medical classification category, or have been identified as not to be classified into the medical classification category. The annotated structured dataset is created by the classification of unclassified unstructured text fragments into the medical classification term (i.e., performing a search of the structured information a query associated with the structured information), Barkan [0034]).
Therefore, it would have been obvious to one ordinary skill in the art before the effective filing date of the claimed invention was made having the teachings of Shukla and Barkan before him/her, to modify Shukla with the teaching of Barkan’s system and a method of generating an annotated structured dataset. One would have been motivated to do so for the benefit of generating targeted annotation of data for efficient and faster processing of training data for artificial intelligence (AI) applications affects performance of the AI model (Barkan, Abstract, [0002]).
Regarding claim 2. Shukla as modified teaches, comprising instructions to:
receive the input indicating the task (Shukla [0024], [0026]),
wherein the task comprises at least one of:
summarizing the record to obtain the output including a summary (Shukla [0055], [0067], [0089], [0097]);
extracting indicated information from the record to obtain the output including the indicated information (Shukla [0039]);
generating a document based on the large document to obtain the output including the document (Shukla [0045-0046]),
wherein the document describing content of the record is smaller than the record (Shukla [0045-0046]); and
enable access to the database based on the property, thereby enabling an efficient understanding of content of the large document without consuming the large document (Shukla [0030], [0062]).
Regarding claim 4. Shukla as modified teaches, comprising instructions to:
prior to performing the task on the multiple records in the database, obtain a subset of the multiple records (Shukla [0058]),
wherein the subset of the multiple records includes fewer records than the multiple records (Shukla [0065], [0085]);
perform the task on the subset of the multiple records to obtain a subset of results, wherein performing the task on the subset of the multiple records is faster than performing the task associated with the multiple records (Shukla [0065], [0085]);
provide the subset of results to a user to inspect (Shukla [0068], [0070]);
receive from the user a modification to the input to obtain a modified input (Shukla [0039]); and
perform, using the artificial intelligence and the modified input, the task associated with the multiple records to obtain the output (Shukla [0039], [0057]).
Regarding claim 5. Shukla as modified teaches, comprising instructions to:
receive the input indicating the task, (Barkan [0078])
wherein the task comprises generating a document describing content of the record (Barkan [0105]), and
wherein the document describing the content of the record is smaller than the record (Shukla [0045]); and
enable access to the database based on the property, thereby enabling an efficient understanding of content of the large document without consuming the large document (Shukla [0045]).
Regarding claim 6. Shukla as modified teaches, comprising instructions to:
receive the input indicating the task, wherein the task includes creating a summary, extracting information from the record, or generating a document based on the large document (Shukla [0002], [0038-0039], [0042-0047]);
receive a second input including a natural language input further specifying type of information to obtain from the record (Shukla [0057]); and
based on the natural language input and the task, perform the task (Shukla [0057]).
Regarding claim 7. Shukla as modified teaches, comprising instructions to:
determine whether the record in the database is modified after the task is performed; upon determining that the record in the database is modified after the task is performed, automatically repeat a performance of the task to obtain a second output (Shukla [0004], [0039]); and
store the second output as the property in the database (Shukla [0026], [0047]).
Regarding claims 8-13, the medium steps of claims 1, 2 and 4-7 substantially encompass the method recited in claims 8-13. Therefore, claims 8-13 are rejected for at least the same reason as claims 1, 2 and 4-7 above.
Regarding claim 14. Shukla discloses a system comprising: at least one hardware processor; and at least one non-transitory memory storing instructions, which, when executed by the at least one hardware processor, cause the system (a non-transitory processor-readable storage medium having stored therein program code of one or more software programs, wherein the program code when executed by at least one processing device causes the at least one processing device to perform steps of, Shukla [0089-0096]) to:
although claim 14 directed to a system, it is similar in scope to claim 1. The medium steps of claim 1 substantially encompass the system recited in claim 14. Therefore; claim 14 is rejected for at least the same reason as claim 1 above.
Regarding claims 15 and 17-20, the medium steps of claims 2 and 4-7 substantially encompass the system recited in claims 15 and 17-20. Therefore, claims 15 and 17-20 are rejected for at least the same reason as claims 2 and 4-7 above.
Claims 3 and 16 are rejected under 35 U.S.C. 103 as being unpatentable over Shukla et al. (US Patent Publication No. 2022/0082095 A1, ‘Shukla’, hereafter) in view of Barkan et al. (US Patent Publication No. 2021/0225466 A1, ‘Barkan’, hereafter) and further in view of Agarwal et al. (US Patent No. 11,947,916, ‘Agarwal’, hereafter).
Regarding claim 3. Shukla and Barkan do not teach, computer-readable storage medium of claim 2, wherein the document includes a social media post.
However, Agarwal teaches
computer-readable storage medium of claim 2, wherein the document includes a social media post (At operation, the system may, using a topic model, generate from a document corpus, respective sets of topic terms describing predicted topics for each of a plurality of sentences from the document corpus. For example, the document corpus may include one or more free-text entry complaints, social media posts, fraud complaints, customer reviews, and the like, Agarwal, Col 5:53-67, Col 9:65 – Col 10:3).
Therefore, it would have been obvious to one ordinary skill in the art before the effective filing date of the claimed invention was made having the teachings of Shukla, Barkan and Agarwal before him/her, to further modify Shukla with the teaching of Agarwal’s dynamic topic definition generator. One would have been motivated to do so for the benefit of provide topic sentences summarizing topics determined within a corpus of documents. These summaries may be used by customer service associates, analysts, or other users to quickly determine both topics discussed and contexts of those topics over a large corpus of text (i.e., quickly interpreting high volumes of information) (Agarwal, Abstract, Col 2:7-20).
Regarding claims 16, the medium steps of claim 3 substantially encompass the system recited in claim 16. Therefore, claim 16 is rejected for at least the same reason as claim 3 above.
Conclusion
THIS ACTION IS MADE FINAL. 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 HASANUL MOBIN whose telephone number is (571)270-1289. The examiner can normally be reached on 9:30AM to 6:00PM EST M-F.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Charles Rones can be reached at 571-272-4085. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300. Information regarding the status of an application may be obtained from the Patent Application Information Retrieval (PAIR) system. Status information for published applications may be obtained from either Private PAIR or Public PAIR. Status information for unpublished applications is available through Private PAIR only. For more information about the PAIR system, see http://pair-direct.uspto.gov. Should you have questions on access to the Private PAIR system, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative or access to the automated information system, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/HASANUL MOBIN/
Primary Examiner, Art Unit 2168
Prosecution Insights