DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Continued Examination Under 37 CFR 1.114
A request for continued examination (RCE) under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on has been entered.
However, all claims are either identical to or patentably indistinct from claims in the application prior to the entry of the submission under 37 CFR 1.114 and all claims could have been finally rejected on the grounds and art of record in the next Office action if they had been entered in the application prior to entry under 37 CFR1.114.
Accordingly, THIS ACTION IS MADE FINAL even though it is a first action after the filing of a request for continued examination and the submission under 37 CFR 1.114. See MPEP § 706.07(b). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
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 rejected under 35 U.S.C. § 101 are directed to an abstract idea without significantly more.
The claims do not provide significantly more than the judicial exception under the subject matter eligibility two-part statutory analysis, as provided below.
Regarding Step 1,
Step 1 addresses whether the claims are directed to one of the four statutory categories of invention, i.e., process, machine, manufacture, or composition of matter according to MPEP §2106.03. Claim 1 recites a method (process), claim 15 recites a non-transitory computer readable medium (article of manufacture), and claim 20 is a system (apparatus/machine), which all fall within one of the four statutory categories.
Regarding Step 2A [prong 1],
The claimed invention recites an abstract idea according to MPEP §2106.04. Independent claim 1, also representative of independent claims 15 and 20, for the same abstract features, is underlined below which recite the following claim limitations, as an abstract idea.
Claims 1, 15 and 20:
receiving a plurality of feedback responses regarding operation of a software application [product], wherein the feedback is free-form textual feedback;
aggregating the plurality of feedback into processed feedback responses into a summarized feedback reflecting a discrete number of observations regarding the operation of the software application [product], wherein the observations is in textual form;
identifying a subset of the observations that satisfy a relevance criterion, wherein the subset includes a first observation;
identifying the first observation of the subset of the observations for implementation;
deploying an update to the [product] based on the identified first observation.
The underlined claim limitations, under its broadest reasonable interpretation, fall under “Certain Methods of Organizing Human Activities” grouping of abstract ideas, and includes at least managing personal behavior or relationships or interactions between people (including social activities, teaching, and following rules or instructions). See MPEP §2106.04(a)(2)(II).
But for the recitation of generic implementation of computer system components, the claimed invention merely recites a process for managing personal behavior/relationships or interactions between people because the claimed steps recite obtaining feedback from users of a software product. Accordingly, since the claimed invention describes a process that falls under “Certain Methods of Organizing Human Activities” grouping, the claimed invention recites an abstract idea.
Alternatively, the underlined claim limitations recite “Mental Processes” grouping of abstract ideas, which can practically be performed in the human mind and/or with the use of a physical aid such as pen and paper. The use of a physical aid (e.g., pencil and paper) to help perform a mental step does not negate the mental nature of the limitation. The limitations recite a mental-process type abstract idea as they can be accomplished by including an observation, evaluation, judgment, and/or opinion based on receiving a plurality of feedback regarding operation of a [product], aggregating the plurality of feedback into a discrete number of observations regarding the operation of the [product], wherein the observations are in textual form, determining a subset of the observations that satisfy a relevance criterion.
Regarding Step 2A [prong 2],
The judicial exception is not integrated into a practical application according to MPEP §2106.04(d). Claims 1, 15 and 20 include the following additional elements:
A method, system and non-transitory computer readable medium having stored thereon program instructions, computing system to perform operations comprising:
memory;
one or more processors;
receiving, via user interface;
trained machine-learning model;
update to the software application.
In particular, the additional elements cited above beyond the abstract idea are recited at a high-level of generality and simply equivalent to a generic recitation and basic functionality that amount to no more than mere instructions to apply the judicial exception using generic computer technology components.
The claimed invention merely provides an abstract-idea-based-solution implemented with generic computer processes and components recited at a high-level of generality (receiving, storing, aggregating, and determining data) using computer instructions to implement the abstract idea on a computer, and merely “apply it” without any meaningful technological limits or any improvement to technology, technical field or improvement to the functioning of the computer itself.
Additionally, receiving, via a user interface, a plurality of feedback amounts to data gathering and identifying the subset of the observations for display, amounts to selecting a data source, and does not add any meaningful limitations, and since receiving, identifying, storing and transmitting data is considered one of the most basic functions of a computer, these additional elements are deemed as insignificant extra-solution activity to the judicial exception. The legal precedent in Electric Power Group and Ultramercial cited in MPEP 2106.05(g) indicate that selecting information, based on types of information and availability of information for collection, analysis and display, and requiring a request from a user to view an advertisement and restricting public access, are all insignificant extra-solution activity.
Therefore, the additional elements fail to integrate the recited abstract idea into any practical application since they do not impose any non-generic meaningful limits on practicing the abstract idea. Thus, the claimed invention is directed to an abstract idea.
Regarding Step 2B,
The claimed invention does not include additional elements that are sufficient to amount to significantly more than the judicial exception. See MPEP §2106.05.
As discussed above, the claimed additional elements recited above amounts to no more than mere instructions to implement the abstract idea by adding the words “apply it” using generic computer components and functionality. See MPEP §2106.05(h). Mere instructions to apply the judicial exception using generic computer components are insufficient to provide an inventive concept. Furthermore, the claimed additional elements merely limit the abstract idea to be executed in a computer environment, thus do nothing more than generally linking the use of a judicial exception to a particular technological environment or field of use. See MPEP §2106.05(h).
Additionally, re-evaluating the insignificant extra-solution activities listed above, it is determined that they are also well-understood, routine, and conventional, as well. See MPEP 2106.05(d). The legal precedent in Ultramercial, Versata, Symantec, TLI, and OIP Techs court decisions cited in MPEP 2106.05(d)(II) indicate that storing and retrieving information in memory, as well as receipt and transmission of information over a computer network, and updating an activity log are a well-understood, routine, and conventional functions claimed in a generic manner, as is the case here. See also Trading Techs. Int’l, Inc. v. IBG LLC, 921 F.3d 1084, 1093 (Fed. Cir. 2019) (data gathering and displaying are well-understood, routine, and conventional activities) and also buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355 (Fed. Cir. 2014) (“That a computer receives and sends the information over a network—with no further specification—is not even arguably inventive”).
Considered as an ordered combination, the additional elements are claimed at a high-level of generality and add nothing that is not already present when the steps are considered separately. The sequence of the claimed limitations is equally generic and otherwise held to be abstract since the combination of these additional elements is no more than mere instructions to apply the judicial exception using generic computer components operating in their ordinary and generic capacities of what is typically expected of computers storing and updating data, and receiving and transmitting data between generic computer devices. The claimed invention is not patent eligible because the additional elements are merely invoked as tools to execute the abstract idea and thus are insufficient to amount to an inventive concept significantly more than the judicial exception.
As for dependent claims 2-14 and 16-19 they merely further narrow and reiterate the same abstract ideas for receiving and transmitting data with the same additional elements as recited above which provide nothing more than applying the abstract idea using generic computer technology components. Furthermore dependent claims comprise the following additional elements: [user interface] components comprising a pane, a column, a popup window, or an overlay on the user interface, a database, and database structure.
These additional elements do not provide any improvement to technology, technical field or improvement to the functioning of the computer itself, and at best simply applying the abstract idea executed in a general-purpose computer environment. Therefore the dependent claims are also directed to ineligible subject matter since they do not provide significantly more than the abstract idea itself.
Thus, after considering all claim elements in Claims 1-20 both individually and as an ordered combination, it has been determined that the claimed invention as a whole, is not enough to transform the abstract idea into a patent-eligible invention since nothing in the claim limitations provide significantly more than the abstract idea under 35 U.S.C. § 101.
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of pre-AIA 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(b) the invention was patented or described in a printed publication in this or a foreign country or in public use or on sale in this country, more than one year prior to the date of application for patent in the United States.
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale or otherwise available to the public before the effective filing date of the claimed invention.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claims 1-11, and 14-20 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Bhaskaran (US 20230031152).
Regarding Claims 1, 15, and 20,
Bhaskaran discloses:
A method, system and non-transitory computer readable medium, having stored thereon program instructions that, upon execution by a computing system, cause the computing system to perform operations (Abstract; a first interface to a messaging system that includes a plurality of channels, a second interface to a machine learning system that includes a query detection model and a reply detection model, Summary, Figs. 1-9) comprising:
receiving a plurality of feedback responses regarding operation of a software application, wherein the feedback responses are free-form textual feedback ([0122] Messages 700 may include a query (i.e., a question) asked by a first user, a reply (i.e., an answer) provided in response to the query by a second user, and/or user feedback provided by, for example, the first user in response to the reply. The user feedback may express, for example, a perceived extent of correctness of the reply, [0140-0153] User feedback may include one or more of a plurality of possible data types and/or data formats, including text. Remote network management platform 320 can request that a human review certain queries and replies (textual) in order to verify and/or correct the outputs of machine learning system. Device 860 may be configured to provide a graphical user interface that includes the query, a checkbox for selecting each of the replies, [0126] Messages may represent a set of one or more messages (textual) retrieved by remote network management platform 320);
aggregating, via a trained machine-learning model, the plurality of feedback responses into a summarized feedback reflecting a discrete number of observations regarding the operation of the software application, wherein the observations are in textual form ([0122] After obtaining the messages, remote network management platform 320 may be configured to utilize machine learning system 610 to extract and organize (summarize) the information present in the retrieved messages. Machine learning system 610 may be configured to determine, based on the messages and/or other documents, queries presented within the messages and one or more responses to each of these queries, [0003] the messages can be processed by the machine learning system to rank the messages to detect queries, and replies to the queries, and rank the replies based on relevance and correctness, and [0180]);
identifying a subset of the observations that satisfy a relevance criterion by determining that the subset includes a first observation ([0003] replies may be ranked according to a relevance, which may be quantified based on user feedback provided in response to the replies as part of the plurality of messages, [0120-0121] messages may be organized into groups or subsets, [0122] Messages 700 may include a query (i.e., a question) asked by a first user, a reply (i.e., an answer) provided in response to the query by a second user, and/or user feedback provided by, for example, the first user in response to the reply, see also [0184]); and
identifying the subset of the observations for display ([0125] Machine learning system 610 may be configured to generate output 724 based on messages, [0184] one or more replies related to each of the highest-ranked queries may be provided) and
deploying an update to the software application based on the identified first observation ([0111] The proxy servers determine the software configuration of the devices. At block 528, the exploration phase takes place. The proxy servers attempt to determine the applications executing on the devices. At block 530, further editing of the software configuration of the applications may take place. This editing may be automated and/or manual in nature based on feedback for editing software configuration of the applications).
Regarding Claim 2,
Bhaskaran discloses wherein the plurality of feedback responses are received from a plurality of users ([0002] Users associated with a computer network may utilize a messaging system to exchange information with one another, the exchange can include user feedback and conversations occurring on different channels. A message that forms part of the conversation may include a query, and a subsequent message in the conversation may include a user reply to the query, [0004] users may provide a reply that is responsive to the query).
Regarding Claim 3,
Bhaskaran discloses wherein the relevance criterion is that the subset of the observations are most prevalent within the observations ([0003] rank the one or more replies according to relevance and/or correctness. The machine learning system may search the plurality of messages, as well as other documents available within the computer network, for replies to the query. The replies may be ranked according to a relevance, which may be quantified based on user feedback provided in response to the replies as part of the plurality of messages, [0136-0137] selection of documents that are relevant to query 706 may be facilitated by similarity model. Reply detection model 712 may be configured to process documents associated with a similarity metric value that exceeds a threshold similarity value, and [0134], [0143] and [0151] grouped by topic).
Regarding Claim 4,
Bhaskaran discloses the operation of the software application relates to operation of a module of the software application, and wherein the plurality of feedback responses are obtained via a feedback component displayed in conjunction with a feedback interface ([0032] system includes the application components and software modules, [0152] In response to reception of the query and its ranked replies, device 860 may be configured to display the query and its ranked replies, [0140] The quality of user feedback may indicate, for example, a degree to which the first user that posed query 706 within message 700 is satisfied with reply 714 provided within messages).
Regarding Claim 5,
Bhaskaran discloses wherein the feedback component comprises one or more of a pane, a column, a popup window, or an overlay on the feedback interface ([0108] dependencies and relationships between configuration items may be displayed on a web-based interface, [0153] Device 860 may be configured to provide a graphical user interface that includes the query, a checkbox for selecting each of the replies, and a text box allowing for manual entry of a reply other than the machine-generated replies).
Regarding Claim 6,
Bhaskaran wherein the feedback component displays one or more per-component questions, wherein the plurality of feedback responses includes respective feedback responses to the per-component questions, and wherein at least some of the respective feedback responses are to respective per-question options ([0142] Reply filter 722 may be configured to generate output 724 based on ranking 720 and the replies generated by reply detection model 712. Output 724 may include query 706, embedding vector 726 corresponding to query 706, and ranked replies, [0140] The quality of user feedback may indicate, for example, a degree to which the first user that posed query 706 within message 700 is satisfied with reply 714 provided within messages 700 by the second user. To that end, reply ranking model 718 may be configured to perform sentiment analysis of at least the user feedback for reply).
Regarding Claim 7,
Bhaskaran wherein prior to receiving the plurality of feedback responses, reading, from a database structure, the per-component questions and the respective per-question options; and after receiving the plurality of feedback responses, writing, to the database structure, the respective feedback responses (Reading and writing to the database is considered retrieving and storing data; Fig. 2 data storage 204, see [0006] the operations may further include storing, in the archive, the query, the one or more replies, [0055] Data storage 204 may be data storage arrays that include drive array controllers configured to manage read and write access).
Regarding Claim 8,
Bhaskaran after aggregating the plurality of feedback responses into the observations, writing, to the database structure, the observations ([0006] the operations may further include storing, in the archive, the query, the one or more replies, [0055] Data storage 204 may be data storage arrays that include drive array controllers configured to manage read and write access).
Regarding Claim 9,
Bhaskaran wherein the trained machine-learning model includes a clustering model, wherein aggregating the plurality of feedback responses into the observations comprises applying the clustering model to the plurality of feedback responses in order to identify semantic clusters thereof, wherein the observations relate to the semantic clusters, and wherein the relevance criterion is that the semantic clusters are populated with at least a threshold number of the observations ([0102] each of channels 602-606 may include a plurality of messages associated therewith. These messages may be organized into groups or subsets, which may be referred to as threads, [0151] queries may be grouped by topics. In one example, the topic of the query may be determined based on the channel on which the query appeared. In another example, the topic of the query may be determined based on the embedding vector of the query belonging to a cluster containing a plurality of queries on a corresponding topic).
Regarding Claim 10,
Bhaskaran wherein the trained machine-learning model includes a similarity model, wherein aggregating the plurality of feedback responses into the observations comprises applying the similarity model to the plurality of feedback responses in order to identify similar feedback thereof, wherein the observations relate to the similar feedback, and wherein the relevance criterion is that the similar feedback has at least a threshold degree of similarity ([0159] reference embedding vector may be comparable to previously-generated embedding vectors that represent corresponding previously-answered queries in order to identify similar previously-answered queries, thereby identifying possible replies to the second query, [0160-0162] The candidate queries may be identified based on a comparison of the reference embedding vector to the embedding vectors that represent the candidate queries. An extent of similarity between the second query and a given candidate query may be quantified by determining a distance (e.g., Euclidean distance) between (i) the reference embedding vector and (ii) an embedding vector that represents the given candidate query).
Regarding Claim 11,
Bhaskaran discloses wherein the trained machine-learning model includes a sentiment analysis model, wherein aggregating the plurality of feedback responses into the observations comprises applying the sentiment analysis model to the plurality of feedback responses in order to identify sentiments therein, wherein the observations relate to the sentiments, and wherein the relevance criterion is that each of the sentiments has at least a threshold level of confidence ([0140] The quality of user feedback may indicate, for example, a degree to which the first user that posed query 706 within message 700 is satisfied with reply 714 provided within messages 700 by the second user. To that end, reply ranking model 718 may be configured to perform sentiment analysis of at least the user feedback for reply 714 to determine a sentiment score associated with reply 714. Reply ranking model 718 may be configured to perform sentiment analysis and rank user feedback, [0143] Each of ranked replies 728 may be selected based on being associated with a ranking score that exceeds a threshold ranking score, and [0174-0175]).
Regarding Claim 14,
Bhaskaran discloses wherein providing the subset of the observations for display comprises providing the subset of the observations for display on an administrative user interface, wherein the administrative user interface includes one or more of the plurality of feedback responses as well as the observations ([0140-0153] User feedback may include one or more of a plurality of possible data types and/or data formats, including text. Remote network management platform 320 can request that a human review certain queries and replies in order to verify and/or correct the outputs of machine learning system. Device 860 may be configured to provide a graphical user interface that includes the query, [0120-0121] messages may be organized into groups or subsets).
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.
Note: 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 factual inquiries set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied 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 non-obviousness.
Claims 12-13 are rejected under 35 U.S.C. 103 as being unpatentable over Bhaskaran (US 20230031152) in view of Darling (US20150019912).
Regarding Claim 12,
Bhaskaran discloses wherein the trained machine-learning model includes a model, wherein aggregating the plurality of feedback responses into the observations comprises applying the model to the plurality of feedback responses in order to identify the plurality of feedback ([0137] Reply detection model 712 may be configured to process documents associated with a similarity metric value that exceeds a threshold similarity value. Accordingly, reply detection model 712 may be configured to look for replies in documents that are likely to contain replies to query 706, and avoid processing documents that are unlikely to contain replies to query 706. Document 702 may be considered an example of a document that is likely to contain a reply to query 706 due to similarity metric 710 exceeding the threshold similarity value. Accordingly, reply detection model 712 may be configured to identify reply 716 within document 702. Similarly, additional replies may be identified within messages 700, document 702, and/or other documents).
Although Bhaskaran discloses the limitation above, including the machine learning model, and aggregating the plurality of feedback responses into an organized or summarized user feedback reflecting observations, it does not explicitly specify that the machine learning model includes using a summarization model to identify summaries of the plurality of feedback, wherein the observations relate to the summaries.
Nonetheless, Darling discloses:
applying a summarization model to identify summaries the plurality of feedback, and wherein the observations relate to the summaries (Abstract, [0020-0023] FIGS. 12 and 13 show accuracy results for the opinion of an augmented summarization system, and an opinion summarization method may be implemented by a set of pipeline components by modeling sentiment and extracting of text, and user feedback is provided regarding the accuracy, [0033] Pipelines can include Natural Language Processing (NLP) applications, such as named entity recognition, text summarization, and opinion mining (e.g., using a first pipeline component that filters out comments that do not contain opinion, and a second pipeline component that labels the comments with category labels selected from a predefined set of opinion category labels, such as categories relating to the subject matter of the opinion and/or whether it is positive or negative with respect to the subject), [0026] many summarization algorithms first run a part-of-speech (POS) tagging module, and a probabilistic approach is used, based on a learned prediction model, see also [0103-0106]).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to incorporate the feature of applying a summarization model to the plurality of feedback, wherein the observations relate to the summaries as taught by Darling into the machine-learning model of Bhaskaran. One of ordinary skill would have been motivated to incorporate the summary feature since one of ordinary skill in the art would have recognized that the results of the combination were predictable and in order to achieve the benefit of “improve with user feedback” (Darling; [0096]).
Regarding Claim 13,
Modifed Bhaskaran discloses the summarization model. Darling further discloses a transformer-based large language model that is prompted with a request to summarize the plurality of feedback responses ([0059] A summary visualization 92 displays a text sample under a category heading only if it was determined by the pipeline to both contain opinionated text and be associated with that category, [0096] By transforming these components to learnable, the sentence filter and the topic model can be transformed into a set of K+1 supervised binary classifiers [transformer-based language model] that gradually improve with user feedback. For the summarization example, a two-stage pipeline includes K+1 binary classifiers: one for filtering relevant sentences, the other K to label them as members of K categories).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to incorporate the transformer-based large language model that is prompted with a request to summarize into the machine-learning model of Bhaskaran. One of ordinary skill would have been motivated to incorporate the transformer-based large language model in order to “improve with user feedback” (Darling; [0096]).
Response to Amendment & Arguments
Applicant’s arguments and amendment have been considered, however they are found unpersuasive, as indicated below.
Claim Rejections 35 USC 101
PNG
media_image1.png
248
714
media_image1.png
Greyscale
Examiner respectfully disagrees. Observation in the specification is merely defined as any plurality of TEXTUAL information regarding a software application (PRODUCT). See Specification [0004]. Any message, reply, document, information in textual format can be an observation regarding the product (software). Bhaskaran discloses aggregating, identifying a first observation. In [0122] after obtaining the messages, remote network management platform 320 may be configured to utilize machine learning system 610 to extract and organize the information present in the retrieved messages. Obtaining, extracting, and organizing the information and messages pertain to aggregating, identifying the one or more observations. Further, in [0122] Machine learning system 610 may be configured to determine, based on the messages and/or other documents, queries presented within the messages and one or more responses to each of these queries, and in [0003] the messages can be processed by the machine learning system to rank the messages to detect queries, and replies to the queries, and rank the replies based on relevance and correctness.
Deploying of an observation to update a software application is also disclosed.
PNG
media_image2.png
192
687
media_image2.png
Greyscale
For claims 12-13, Examiner respectfully submits that Darling was not used to identify a first observation for implementation, nor for the newly added amendment of deploying an update to the software application based on the identified first observation. Bhaskaran already discloses a machine learning model however not a summarization model to identify summaries. Bhaskaran already discloses aggregating the plurality of feedback responses into an organized or summarized user feedback reflecting observations.
Darling was used only to disclose a summarization model in addition to the machine learning model, that specifically is used to identify summaries in [0020-0026] and [0033]; Augmented summarization system and opinion summarization method, modeling sentiment and extracting text, and user feedback for accuracy, including Natural Language Processing (NLP) and other summarization algorithms such as POS tagging and probabilistic based on learned prediction modeling in order to improve user feedback.
Conclusion
All claims are either identical or patentably indistinct from the previous claims in the application prior to the entry of the submission under 37 CFR 1.114 and all claims could have been finally rejected on the grounds and art of record in the next Office action if they had been entered in the application prior to entry under 37 CFR1.114.
Accordingly, THIS ACTION IS MADE FINAL even though it is a first action after the filing of a request for continued examination and the submission under 37 CFR 1.114. See MPEP § 706.07(b). 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.
The relevant prior art made of record not relied upon but considered pertinent to applicant's disclosure can be found in the current and/or previous PTO-892 Notice of References Cited.
US 20210014260 Multi-application recommendation engine for a remote network management platform.
F. Palomba et al., "Recommending and Localizing Change Requests for Mobile Apps Based on User Reviews," 2017 IEEE/ACM 39th International Conference on Software Engineering (ICSE), Buenos Aires, Argentina, 2017, pp. 106-117, doi: 10.1109/ICSE.2017.18.
Any inquiry concerning this communication or earlier communications from the Examiner should be directed to LAURA YESILDAG whose direct telephone number is (571) 270-5066 and work schedule is generally Monday-Friday, from 9:00 AM - 5:00 PM ET.
In order to receive any email communication from the Examiner, filing for official authorization for Internet Communication is required. The authorization form can be accessed at https://www.uspto.gov/sites/default/files/documents/sb0439.pdf.
Examiner interviews can be requested by telephone or are available using the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the Examiner are unsuccessful, the Examiner’s Supervisor, LYNDA JASMIN, can be reached at (571) 272-6782 for any urgent matter that needs immediate attention. Additional information regarding the status of an application may be obtained from the USPTO Patent Center. For more information about the USPTO Patent Center, please access https://patentcenter.uspto.gov/ The Patent Center is available to all users for electronic filing and management of patent applications and can be contacted for questions at 1-866-217-9197 or 571-272-4100.
/LAURA YESILDAG/Primary Examiner, Art Unit 3629