CTNF 18/464,905 CTNF 98440 DETAILED ACTION Notice of Pre-AIA or AIA Status 07-03-aia AIA 15-10-aia The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA. Claim Rejections - 35 USC § 101 07-04-01 AIA 07-04 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. Step 1: Claims 1-7 are directed to a process. Claims 8-20 are directed to a machine or an article of manufacture. With respect to claim(s) 1, 8, and 15: 2A Prong 1 : The claim(s) recite(s) an abstract idea. Specifically: preprocessing/preprocess the client interaction data into a format consumable […] ( Mental process – A person can preprocess client interaction data into a specific format via mind or by the use of a pen and paper as physical aid – see MPEP § 2106.04(a)(2)(III)) generating/generate […] a plurality of sentiment scores for a client associated with the client interaction data ; ( Mental process – A person can evaluate client data and mentally determine sentiment scores for a client – see MPEP § 2106.04(a)(2)(III)) If claim limitations, under their broadest reasonable interpretation, cover performance of the limitations as a mental process, but for the recitation of generic computer components, then the claim limitations fall within the mathematical or mental process grouping of abstract ideas. Accordingly, the claim “recites” an abstract idea. 2A Prong 2 : The additional elements recited in the claim(s) do not integrate the abstract idea into a practical application, individually or in combination. Additional elements: (Claim 1) A method comprising : (Mere instructions to apply an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea – see MPEP 2106.05(f).) (Claim 8) A system comprising at least one computer including a processor, wherein the at least one computer is configured to : (Mere instructions to apply an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea – see MPEP 2106.05(f).) (Claim 15) A non-transitory computer readable storage medium, including instructions stored thereon, which instructions, when read and executed by one or more computer processors, cause the one or more computer processors to perform steps comprising : (Mere instructions to apply an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea – see MPEP 2106.05(f).) receiving/receive, from a plurality of data channels, client interaction data ; (Mere data gathering – Adding insignificant extra-solution activity of mere data gathering to the judicial exception – see § MPEP2106.05(g).) […] by one or more machine learning models ; (Mere instructions to apply an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea – see MPEP 2106.05(f).) ingesting/ingest, by a sentiment scorer machine learning model, the client interaction data ; (Mere data gathering – Adding insignificant extra-solution activity of mere data gathering to the judicial exception – see § MPEP2106.05(g).) […] by the sentiment scorer machine learning model, […] (Mere instructions to apply an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea – see MPEP 2106.05(f).) ingesting/ingest, by a health scorer machine learning model, the plurality of sentiment scores ; (Mere data gathering – Adding insignificant extra-solution activity of mere data gathering to the judicial exception – see § MPEP2106.05(g).) […] by the health scorer machine learning model and […] (Mere instructions to apply an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea – see MPEP 2106.05(f).) outputting/output […] based on the plurality of sentiment scores, a weighted health index score for the client associated with the client interaction data . (Adding insignificant extra-solution activity to the judicial exception – see § MPEP2106.05(g).) Since the claim as a whole, looking at the additional elements individually and in combination, does not contain any other additional elements that are indicative of integration into a practical application, the claim is directed to an abstract idea. 2B : The claim(s) do(es) not include additional elements that are sufficient to amount to significantly more than the judicial exception. Additional elements: (Claim 1) A method comprising : (Mere instructions to apply an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea – see MPEP 2106.05(f).) (Claim 8) A system comprising at least one computer including a processor, wherein the at least one computer is configured to : (Mere instructions to apply an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea – see MPEP 2106.05(f).) (Claim 15) A non-transitory computer readable storage medium, including instructions stored thereon, which instructions, when read and executed by one or more computer processors, cause the one or more computer processors to perform steps comprising : (Mere instructions to apply an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea – see MPEP 2106.05(f).) receiving/receive, from a plurality of data channels, client interaction data ; (Simply appending well-understood, routine, conventional activities previously known to the industry, specified at a high level of generality, to the judicial exception (WURC)- see MPEP § 2106.05(d)(ll)(i) - Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information).) […] by one or more machine learning models ; (Mere instructions to apply an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea – see MPEP 2106.05(f).) ingesting/ingest, by a sentiment scorer machine learning model, the client interaction data ; (Simply appending well-understood, routine, conventional activities previously known to the industry, specified at a high level of generality, to the judicial exception (WURC)- see MPEP § 2106.05(d)(ll)(i) - Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information).) […] by the sentiment scorer machine learning model, […] (Mere instructions to apply an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea – see MPEP 2106.05(f).) ingesting/ingest, by a health scorer machine learning model, the plurality of sentiment scores ; (Simply appending well-understood, routine, conventional activities previously known to the industry, specified at a high level of generality, to the judicial exception (WURC)- see MPEP § 2106.05(d)(ll)(i) - Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information).) […] by the health scorer machine learning model and […] (Mere instructions to apply an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea – see MPEP 2106.05(f).) outputting/output […] based on the plurality of sentiment scores, a weighted health index score for the client associated with the client interaction data . (Simply appending well-understood, routine, conventional activities previously known to the industry, specified at a high level of generality, to the judicial exception (WURC)- see MPEP § 2106.05(d)(ll)(i) - Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information).) Considering the additional elements individually and in combination, and the claim as a whole, the additional elements do not provide significantly more than the abstract idea. Therefore, the claim is not patent eligible . With respect to claim(s) 2, 9, and 16: 2A Prong 2 : The additional elements recited in the claim(s) do not integrate the abstract idea into a practical application, individually or in combination. Additional elements: ingesting/ingest, by the health scorer machine learning model, a data channel identifier for each sentiment score of the plurality of sentiment scores , (Mere data gathering – Adding insignificant extra-solution activity of mere data gathering to the judicial exception – see § MPEP2106.05(g).) wherein the data channel identifier indicates a data channel from which sentiment data that a corresponding one of the plurality of sentiment scores is based on was received . (Mere instructions to apply an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea – see MPEP 2106.05(f).) 2B : The claim(s) do(es) not include additional elements that are sufficient to amount to significantly more than the judicial exception. Additional elements: ingesting/ingest, by the health scorer machine learning model, a data channel identifier for each sentiment score of the plurality of sentiment scores , (Simply appending well-understood, routine, conventional activities previously known to the industry, specified at a high level of generality, to the judicial exception (WURC)- see MPEP § 2106.05(d)(ll)(i) - Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information).) wherein the data channel identifier indicates a data channel from which sentiment data that a corresponding one of the plurality of sentiment scores is based on was received . (Mere instructions to apply an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea – see MPEP 2106.05(f).) Since the claim does not recite additional elements that either integrate the judicial exception into a practical application, nor provide significantly more than the judicial exception, the claim is not patent eligible. Therefore, the claim is not patent eligible . With respect to claim(s) 3, 10, and 17: 2A Prong 2 : The additional elements recited in the claim(s) do not integrate the abstract idea into a practical application, individually or in combination. Additional elements: ingesting/ingest, by the health scorer machine learning model, an age for each sentiment score of the plurality of sentiment scores , (Mere data gathering – Adding insignificant extra-solution activity of mere data gathering to the judicial exception – see § MPEP2106.05(g).) wherein the age indicates a time when the sentiment data that a corresponding one of the plurality of sentiment scores is based on was collected . (Mere instructions to apply an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea – see MPEP 2106.05(f).) 2B : The claim(s) do(es) not include additional elements that are sufficient to amount to significantly more than the judicial exception. Additional elements: ingesting/ingest, by the health scorer machine learning model, an age for each sentiment score of the plurality of sentiment scores , (Simply appending well-understood, routine, conventional activities previously known to the industry, specified at a high level of generality, to the judicial exception (WURC)- see MPEP § 2106.05(d)(ll)(i) - Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information).) wherein the age indicates a time when the sentiment data that a corresponding one of the plurality of sentiment scores is based on was collected . (Mere instructions to apply an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea – see MPEP 2106.05(f).) Since the claim does not recite additional elements that either integrate the judicial exception into a practical application, nor provide significantly more than the judicial exception, the claim is not patent eligible. Therefore, the claim is not patent eligible . With respect to claim(s) 4, 11, and 18: 2A Prong 2 : The additional elements recited in the claim(s) do not integrate the abstract idea into a practical application, individually or in combination. Additional elements: providing/provide, for each data channel identifier, a legitimacy score, wherein the legitimacy score indicates a weight assigned to the data channel identifier . (Mere instructions to apply an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea – see MPEP 2106.05(f).) 2B : The claim(s) do(es) not include additional elements that are sufficient to amount to significantly more than the judicial exception. Additional elements: providing/provide, for each data channel identifier, a legitimacy score, wherein the legitimacy score indicates a weight assigned to the data channel identifier . (Mere instructions to apply an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea – see MPEP 2106.05(f).) Since the claim does not recite additional elements that either integrate the judicial exception into a practical application, nor provide significantly more than the judicial exception, the claim is not patent eligible. Therefore, the claim is not patent eligible . With respect to claim(s) 5, 12, and 19: 2A Prong 2 : The additional elements recited in the claim(s) do not integrate the abstract idea into a practical application, individually or in combination. Additional elements: providing a plurality of age weights, wherein each of the plurality of age weights corresponds to a corresponding age window of a plurality of age windows . (Mere data gathering – Adding insignificant extra-solution activity of mere data gathering to the judicial exception – see § MPEP2106.05(g).) 2B : The claim(s) do(es) not include additional elements that are sufficient to amount to significantly more than the judicial exception. Additional elements: providing a plurality of age weights, wherein each of the plurality of age weights corresponds to a corresponding age window of a plurality of age windows . (Simply appending well-understood, routine, conventional activities previously known to the industry, specified at a high level of generality, to the judicial exception (WURC)- see MPEP § 2106.05(d)(ll)(i) - Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information).) Since the claim does not recite additional elements that either integrate the judicial exception into a practical application, nor provide significantly more than the judicial exception, the claim is not patent eligible. Therefore, the claim is not patent eligible . With respect to claim(s) 6 and 13: 2A Prong 1 : The claim(s) recite(s) an abstract idea. Specifically: and wherein the age for each sentiment score of the plurality of sentiment scores is assigned an age weight that corresponds to the corresponding age window into which each sentiment score of the plurality of sentiment scores falls . ( Mental process – A person can mentally assign a weight that corresponds to a window (e.g., range) in which the data falls – see MPEP § 2106.04(a)(2)(III)) 2A Prong 2 : The additional elements recited in the claim(s) do not integrate the abstract idea into a practical application, individually or in combination. Additional elements: wherein the age for each sentiment score of the plurality of sentiment scores falls into one of the plurality of age windows , (Mere instructions to apply an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea – see MPEP 2106.05(f).) 2B : The claim(s) do(es) not include additional elements that are sufficient to amount to significantly more than the judicial exception. Additional elements: wherein the age for each sentiment score of the plurality of sentiment scores falls into one of the plurality of age windows , (Mere instructions to apply an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea – see MPEP 2106.05(f).) Since the claim does not recite additional elements that either integrate the judicial exception into a practical application, nor provide significantly more than the judicial exception, the claim is not patent eligible. Therefore, the claim is not patent eligible . With respect to claim(s) 7 and 14: 2A Prong 1 : The claim(s) recite(s) an abstract idea. Specifically: weighting/weight […] the weighted health index score based on the age weight that corresponds to the corresponding age window into which each sentiment score of the plurality of sentiment scores falls and the legitimacy score for each data channel identifier for each sentiment score of the plurality of sentiment scores . ( Mental process – A person can weight an index score based on two values in the mind or by using a pen and paper as a physical aid – see MPEP § 2106.04(a)(2)(III)) 2A Prong 2 : The additional elements recited in the claim(s) do not integrate the abstract idea into a practical application, individually or in combination. Additional elements: […] by the health scorer machine learning model, […] (Mere instructions to apply an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea – see MPEP 2106.05(f).) 2B : The claim(s) do(es) not include additional elements that are sufficient to amount to significantly more than the judicial exception. Additional elements: […] by the health scorer machine learning model, […] (Mere instructions to apply an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea – see MPEP 2106.05(f).) Since the claim does not recite additional elements that either integrate the judicial exception into a practical application, nor provide significantly more than the judicial exception, the claim is not patent eligible. Therefore, the claim is not patent eligible . With respect to claim(s) 20: 2A Prong 1 : The claim(s) recite(s) an abstract idea. Specifically: and wherein the age for each sentiment score of the plurality of sentiment scores is assigned an age weight that corresponds to the corresponding age window into which each sentiment score of the plurality of sentiment scores falls ( Mental process – A person can mentally assign a weight that corresponds to a window (e.g., range) in which the data falls in – see MPEP § 2106.04(a)(2)(III)) weighting […] the weighted health index score based on the age weight that corresponds to the corresponding age window into which each sentiment score of the plurality of sentiment scores falls and the legitimacy score for each data channel identifier for each sentiment score of the plurality of sentiment scores . ( Mental process – A person can weight an index score based on two values in the mind or by using a pen and paper as a physical aid – see MPEP § 2106.04(a)(2)(III)) 2A Prong 2 : The additional elements recited in the claim(s) do not integrate the abstract idea into a practical application, individually or in combination. Additional elements: wherein the age for each sentiment score of the plurality of sentiment scores falls into one of the plurality of age windows , (Mere instructions to apply an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea – see MPEP 2106.05(f).) […] by the health scorer machine learning model, […] (Mere instructions to apply an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea – see MPEP 2106.05(f).) 2B : The claim(s) do(es) not include additional elements that are sufficient to amount to significantly more than the judicial exception. Additional elements: wherein the age for each sentiment score of the plurality of sentiment scores falls into one of the plurality of age windows , (Mere instructions to apply an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea – see MPEP 2106.05(f).) […] by the health scorer machine learning model, […] (Mere instructions to apply an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea – see MPEP 2106.05(f).) Since the claim does not recite additional elements that either integrate the judicial exception into a practical application, nor provide significantly more than the judicial exception, the claim is not patent eligible. Therefore, the claim is not patent eligible . Claim Rejections - 35 USC § 103 07-20-aia AIA 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. 07-21-aia AIA Claim s 1, 8, and 15 are rejected under 35 U.S.C. 103 as being unpatentable over NI (US 20200089767 A1) in view of HABIBABADI (US 12073947 B1), hereafter NI and HABIBABADI respectively . Regarding Claim 1: NI teaches: A method comprising: receiving, from a plurality of data channels, client interaction data ; (NI [0046] teaches: "Data sources (i.e., from a plurality of data channels ) 202 may comprise agent chat sessions 204, virtual agent chat sessions 206, verbatim responses 208, social media feeds 210, emails 212, and/or voice data 214. Agent chat sessions 204 may comprise messages provided (i.e., receiving ) by the customer via a chat session with support staff, customer service representatives, etc." Examiner’s note: Under BRI, client interaction data can be interpreted as the chat sessions, verbatim responses, social media feeds, emails, and/or voice data.) preprocessing the client interaction data into a format consumable by one or more machine learning models ; (NI [0048] teaches: "Data acquisition engine 216 may comprise a speech-to-text converter 218 and an audio analyzer 220. Speech-to-text converter 218 may be configured to convert (i.e., preprocessing ) the voice data to text (i.e., the client interaction data into a format consumable by one or more machine learning models ) in accordance to a particular language (e.g., English, French, Spanish, etc.).") ingesting, by a sentiment scorer machine learning model, the client interaction data ; (NI [0052] teaches: "Batch scoring engine 242 may be configured to generate a sentiment classification and/or score for the type of data provided thereto (i.e., ingesting ). Batch scoring engine 242 may utilize natural language processing and/or machine-learning techniques to generate the score." NI [0053] teaches: "Batch scoring engine 242 may be further configured to output (e.g., via a GUI) the sentiment classification and/or score for the statements uttered by a user after the communication session (e.g., a chat or phone call) has ended." Examiner’s note: Under BRI, ingesting […] the client interaction data can be interpreted as the batch scoring engine receiving data for generating the classification score.) generating, by the sentiment scorer machine learning model, a plurality of sentiment scores for a client associated with the client interaction data ; (NI [0053] teaches: "Batch scoring engine 242 may be further configured to output (e.g., via a GUI) the sentiment classification and/or score for the statements uttered by a user after the communication session (e.g., a chat or phone call) has ended.") However, NI is not relied upon for teaching, but HABIBABADI teaches: ingesting, by a health scorer machine learning model, the plurality of sentiment scores ; (HABIBABADI [col. 3, lines 66-67] and [col. 4, lines 1-6] teaches: "a meta-learner (i.e., a health scorer machine learning model) may be an ensemble of machine learning models that is trained to output a health score for an entity based on a variety of different types of input features related to the entity. For example, text results that are confirmed to be associated with an entity, as well as other data known to be related to the entity, may be used to provide inputs to one or more machine learning models of the meta-learner in order to determine the health score. The machine learning models may include, for example, one or more sentiment models that are trained to output sentiment scores [...].") outputting, by the health scorer machine learning model and based on the plurality of sentiment scores, a weighted health index score for the client associated with the client interaction data . (HABIBABADI [col. 4, lines 6-8] teaches: "The machine learning models may include, for example, one or more sentiment models that are trained to output sentiment scores [...]." HABIBABADI [col. 4, lines 16-21] teaches: "The meta-learner may utilize outputs from a plurality of individual models in the ensemble to determine a health score for an entity represented by the input data, such as a numerical (e.g., decimal) value between 0 and 1 that indicates an overall health of the entity.") Accordingly, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention, having the teachings of NI and HABIBABADI before them, to include HABIBABADI's meta-learner in NI's multi-channel customer sentiment determination system. One would have been motivated to make such a combination in order to improve the automated health scoring process by producing a result that more accurately reflects an overall health of the entity based on available electronic data, and overcome deficiencies in existing automated techniques in order to produce a health score that is more indicative of the holistic health of an entity (HABIBABADI [col. 5, lines 6-16]). Regarding Claim 8: The claim recites similar limitations as corresponding claim 1 and is rejected for similar reasons as claim 1 using similar teachings and rationale. NI also teaches: A system comprising at least one computer including a processor, wherein the at least one computer is configured to: (NI [0150] teaches: "A system is described herein. The system includes: at least one processor circuit; and at least one memory that stores program code configured to be executed by the at least one processor circuit […].") Regarding Claim 15: The claim recites similar limitations as corresponding claim 1 and is rejected for similar reasons as claim 1 using similar teachings and rationale. NI also teaches: A non-transitory computer readable storage medium, including instructions stored thereon, which instructions, when read and executed by one or more computer processors, cause the one or more computer processors to perform steps comprising : (NI [0140] teaches: "Processor circuit 1602 may execute program code stored in a computer readable medium") 07-21-aia AIA Claim s 2-3, 9-10, and 16-17 are rejected under 35 U.S.C. 103 as being unpatentable over NI in view of HABIBABADI as applied respectively above to claims 1, 8, and 15, and further in view of BURNETT (US 12242892 B1), hereafter BURNETT . Regarding Claim 2: NI in view of HABIBABADI teaches the elements of claim 1 as outlined above. HABIBABADI further teaches: ingesting, by the health scorer machine learning model, a data […] for each sentiment score of the plurality of sentiment scores, […] (HABIBABADI [col. 4, lines 6-8] teaches: "The machine learning models may include, for example, one or more sentiment models that are trained to output sentiment scores [...]." HABIBABADI [col. 4, lines 16-21] teaches: "The meta-learner may utilize outputs from a plurality of individual models in the ensemble to determine a health score for an entity represented by the input data, such as a numerical (e.g., decimal) value between 0 and 1 that indicates an overall health of the entity." HABIBABADI [col. 7, lines 33-40] teaches: "Meta-learner 116 may include model(s) 115, and may be trained to output a health score for an entity based on outputs from model(s) 115 in response to inputs to model(s) 115 (e.g., inputs that are based on text results matched to the entity by scoring engine 112 and, in some embodiments, based on one or more additional inputs related to the entity).") NI in view of HABIBABADI is not relied upon for teaching, but BURNETT teaches: […] a data channel identifier for each sentiment score of the plurality of sentiment scores, […] (BURNETT [col. 21, lines 66-67] and [col. 22, lines 1-5] teaches: "The forwarder 202 may additionally or alternatively modify data received, prior to forwarding the data to the data retrieval subsystem 304. Illustratively, the forwarder 202 may “tag” metadata for each data block, such as by specifying a source, source type, or host associated with the data, or by appending one or more timestamp or time ranges to each data block." BURNETT [col. 22, lines 36-39] teaches: "the data retrieval subsystem 304 may append metadata to the input data, such as a source, source type, or host associated with the data." BURNETT [col. 197, lines 44-45] teaches: "The sentiment analyzer 6006 can be a component in a data processing pipeline that performs sentiment analysis [...]." BURNETT [FIG. 3A] teaches that data retrieval subsystem 304 and streaming data processor 308 are components of intake system 210. BURNETT [FIG. 60] teaches that the sentiment analyzer 6006 is a component of the streaming data processor 308. BURNETT [col. 199, lines 43-45] teaches: "FIG. 71 is a flow diagram illustrative of an embodiment of a routine 7100 implemented by the streaming data processor 308 to perform sentiment analysis [...]." BURNETT [col. 21, lines 32-42] teaches: "Thereafter, a streaming data processor 308 may obtain data from the intake ingestion buffer 306, process the data according to one or more rules, and republish the data to either the intake ingestion buffer 306 (e.g., for additional processing) or to the output ingestion buffer 310, such that the data is made available to downstream components or systems. In this manner, the intake system 210 may repeatedly or iteratively process data according to any of a variety of rules, such that the data is formatted for use on the data intake and query system 108 or any other system." Examiner's note: BURNETT [col. 22, lines 36-39] teaches that the data retrieval system may append metadata to input data such as a source or source type (i.e., data channel identifier). The data is obtained by the streaming data processor containing the sentiment analyzer 6006 which can perform sentiment analysis. The streaming processor 308 can republish the data to downstream components or systems.) […] wherein the data channel identifier indicates a data channel from which sentiment data that a corresponding one of the plurality of sentiment scores is based on was received . (BURNETT [col. 22, lines 36-39] teaches: "the data retrieval subsystem 304 may append metadata to the input data, such as a source, source type, or host associated with the data.") Accordingly, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention, having the teachings of NI, HABIBABADI, and BURNETT before them, to include BURNETT’s metadata “tagging” or appending in NI and HABIBABADI's multi-channel customer sentiment determination system. BURNETT [col. 22, lines 36-39] teaches that input data can be republished for downstream components or systems, and thus by implementing “tagging” or appending metadata such as source and source type to NI’s data (see NI [0046]), and republishing for downstream processes, the “tagged” metadata would be available for processing by HABIBABADI’s meta-learner. The meta-learner disclosed in HABIBABADI can output health scores based on the sentiment score outputs of the machine learning models, but also based on additional inputs related to the entity, as described in HABIBABADI [col. 7, lines 33-40]. A person having ordinary skill in the art would recognize that these additional inputs to HABIBABADI’s meta-learner can be BURNETT’s “tagged” or appended metadata to NI’s input data. One would have been motivated to make such a combination in order to facilitate subsequent processing steps and handle forwarding of network data when consuming vast amounts of data from a large number of data sources (BURNETT [col. 21, lines 63-65] and [col. 22, lines 1-14]). Regarding Claim 3: NI in view of HABIBABADI and BURNETT teaches the elements of claim 2 as outlined above. HABIBABADI further teaches: ingesting, by the health scorer machine learning model, […] (HABIBABADI [col. 3, lines 66-67] and [col. 4, lines 1-6] teaches: "a meta-learner may be an ensemble of machine learning models that is trained to output a health score for an entity based on a variety of different types of input features related to the entity. For example, text results that are confirmed to be associated with an entity, as well as other data known to be related to the entity, may be used to provide inputs to one or more machine learning models of the meta-learner in order to determine the health score. The machine learning models may include, for example, one or more sentiment models that are trained to output sentiment scores [...].") BURNETT further teaches: ingesting […] an age for each sentiment score of the plurality of sentiment scores, […] (BURNETT [col. 22, lines 1-5] teaches: “Illustratively, the forwarder 202 may “tag” metadata for each data block, such as by specifying a source, source type, or host associated with the data, or by appending one or more timestamp or time ranges to each data block.” BURNETT [col. 22, lines 36-39] teaches: "the data retrieval subsystem 304 may append metadata to the input data, such as a source, source type, or host associated with the data." BURNETT [col. 21, lines 32-42] teaches: "Thereafter, a streaming data processor 308 may obtain data from the intake ingestion buffer 306, process the data according to one or more rules, and republish the data to either the intake ingestion buffer 306 (e.g., for additional processing) or to the output ingestion buffer 310, such that the data is made available to downstream components or systems. In this manner, the intake system 210 may repeatedly or iteratively process data according to any of a variety of rules, such that the data is formatted for use on the data intake and query system 108 or any other system." BURNETT [col. 199, lines 43-45] teaches: “FIG. 71 is a flow diagram illustrative of an embodiment of a routine 7100 implemented by the streaming data processor 308 to perform sentiment analysis.” Examiner’s note: Under BRI, ingesting can be interpreted as the republished data containing the appended timestamp or time range (i.e., an age ) along with the outputs of the sentiment analyzer (i.e., for each sentiment score of the plurality of the sentiment scores ) being intake by any downstream component or system, as described in BURNETT [col. 21, lines 32-42].) wherein the age indicates a time when the sentiment data that a corresponding one of the plurality of sentiment scores is based on was collected . (BURNETT [col. 22, lines 1- 5] teaches: “Illustratively, the forwarder 202 may “tag” metadata for each data block, such as by specifying a source, source type, or host associated with the data, or by appending one or more timestamp or time ranges to each data block.” BURNETT [col. 97, lines 43-49] teaches: "The metadata fields can become part of or stored with the event. Note that while the time-stamp metadata field can be extracted from the raw data of each event, the values for the other metadata fields may be determined by the indexing system 212 or indexing node 404 based on information it receives pertaining to the source of the data separate from the machine data.") Regarding Claim 9: NI in view of HABIBABADI teaches the elements of claim 8 as outlined above. Additionally, the claim recites similar limitations as corresponding claim 2 and is rejected for similar reasons as claim 2 using similar teachings and rationale. Regarding Claim 10: NI in view of HABIBABADI and BURNETT teaches the elements of claim 9 as outlined above. Additionally, the claim recites similar limitations as corresponding claim 3 and is rejected for similar reasons as claim 3 using similar teachings and rationale. Regarding Claim 16: NI in view of HABIBABADI teaches the elements of claim 15 as outlined above. Additionally, the claim recites similar limitations as corresponding claims 2 and 9 and is rejected for similar reasons as claims 2 and 9 using similar teachings and rationale. Regarding Claim 17: NI in view of HABIBABADI and BURNETT teaches the elements of claim 16 as outlined above. Additionally, the claim recites similar limitations as corresponding claims 3 and 10 and is rejected for similar reasons as claims 3 and 10 using similar teachings and rationale . 07-21-aia AIA Claims 4, 11, and 18 are r ejected under 35 U.S.C. 103 as being unpatentable over N I in view of HABIBABADI and BURNETT as applied respectively above to claims 3, 10, and 17, and further in view of CHATTERJEE (US 10162900 B1), hereafter CHATTERJEE. R egarding Claim 4: NI in view of HABIBABADI and BURNETT teaches the elements of claim 3 as outlined above. NI in view of HABIBABADI and BURNETT is not relied upon for teaching, but CHATTERJEE teaches: providing, for each data channel identifier, a legitimacy score, wherein the legitimacy score indicates a weight assigned to the data channel identifier . (CHATTERJEE [col. 8, lines 14-17] teaches: "Article—any electronic message collected from news web sites, Twitter tweets, Social Media sites such as Facebook, product review sites, blog sites, internal corporate communications, call center logs, etc." CHATTERJEE [col. 18, lines 1-5] and [col. 18, lines 12-15] teaches: "FIG. 3E is a block diagram illustrating the article quality scoring module 160. The article quality scoring module 160 includes a text quality scoring component 161, a source quality scoring component 162 and an author quality scoring component 163. [...] The source quality scoring component 162 is configured to assign a score based on the relative quality of the source dependent on the source type. For example, if the source type is news, it gives more weight to the New York Times than the Weekly World News. The author quality scoring component 163 is configured to, depending on source type, give more weight to higher reputation authors. For example, for social media tweets, it prefers articles from posters with larger followings. For review sites, it prefers posters listed as verified purchasers and/or with higher numbers of useful reviews.") Accordingly, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention, having the teachings of NI, HABIBABADI, BURNETT, and CHATTERJEE before them, to include CHATTERJEE’s article quality scoring module in NI, HABIBABADI, and BURNETT’s multi-channel customer sentiment determination system. One would have been motivated to make such a combination in order to assign a score based on the relative quality of the source dependent on the source type (CHATTERJEE [col. 18, lines 13-15]). Regarding Claim 11: NI in view of HABIBABADI and BURNETT teaches the elements of claim 10 as outlined above. Additionally, the claim recites similar limitations as corresponding claim 4 and is rejected for similar reasons as claim 4 using similar teachings and rationale. Regarding Claim 18: NI in view of HABIBABADI and BURNETT teaches the elements of claim 17 as outlined above. Additionally, the claim recites similar limitations as corresponding claims 4 and 11 and is rejected for similar reasons as claims 4 and 11 using similar teachings and rationale . 07-21-aia AIA Claim s 5-7, 12-14, and 19-20 are rejected under 35 U.S.C. 103 as being unpatentable over NI in view of HABIBABADI, BURNETT, and CHATTERJEE as applied respectively above to claims 4, 11, and 18, and further in view of VIRTUE (US 20240046323 A1), hereafter VIRTUE . Regarding Claim 5: NI in view of HABIBABADI, BURNETT, and CHATTERJEE teaches the elements of claim 4 as outlined above. NI in view of HABIBABADI, BURNETT, and CHATTERJEE is not relied upon for teaching, but VIRTUE teaches: providing a plurality of age weights, wherein each of the plurality of age weights corresponds to a corresponding age window of a plurality of age windows . (VIRTUE [0055] teaches: "In some embodiments, the decaying function can have a pre-defined half-life (e.g., 500, 520, 540, etc.) and corresponding decay weight (e.g., 0.5, 0.8, etc.) (i.e., providing a plurality of weights ). For example, assuming a half-life of 540 days, if a rating was created a year and a half ago, its decay weight can be 0.5. In some embodiments, the recommendation score for the entity can be determined based on a decaying function as follows:" Examiner's note: Each decay weight corresponds to a pre-defined half-life. Under BRI, wherein each of the plurality of age weights corresponds to a corresponding age window of a plurality of age windows can be interpreted as the pre-defined half-life with their corresponding decay weights. Additionally, age windows can be interpreted as the ranges denoted by 500, 520, 540, which determine the corresponding decay weight to be provided.) Accordingly, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention, having the teachings of NI, HABIBABADI, BURNETT, CHATTERJEE, and VIRTUE before them, to include VIRTUE’s decaying function in NI, HABIBABADI, BURNETT, and CHATTERJEE’s multi-channel customer sentiment determination system. One would have been motivated to make such a combination in order to determine a score based on how long ago was a user review or rating created (VIRTUE [0055]). Regarding Claim 6: NI in view of HABIBABADI, BURNETT, and CHATTERJEE teaches the elements of claim 4 as outlined above. NI in view of HABIBABADI, BURNETT, and CHATTERJEE is not relied upon for teaching, but VIRTUE teaches: wherein the age for each sentiment score of the plurality of sentiment scores falls into one of the plurality of age windows, and wherein the age for each sentiment score of the plurality of sentiment scores is assigned an age weight that corresponds to the corresponding age window into which each sentiment score of the plurality of sentiment scores falls . (VIRTUE [0054] teaches: "The sentiment value can be determined based on a generally known sentiment classifier. For example, the sentiment classifier can evaluate text-based feedback in an unsolicited recommendation to determine a sentiment value. The sentiment value can range from −1 to 1 where a value of −1 indicates very negative sentiment and a value of 1 indicates very positive sentiment. In some embodiments, the unsolicited recommendation is assigned a score of 1 if its sentiment value is between a first pre-defined range (e.g., between −1 and −0.6). In some embodiments, the unsolicited recommendation is assigned a score of 2 if its sentiment value is between a second pre-defined range (e.g., between −0.6 and 0)." VIRTUE [0055] teaches: "In some embodiments, ratings and reviews provided under a legacy rating scheme can be decayed. For example, in some embodiments, a decaying function can be applied to all ratings and reviews associated with a best numerical (or star) rating. For example, the decaying function can be applied to all ratings and reviews associated with a numerical (or star) rating of 5 with 5 being the best rating. In some embodiments, the decaying function can have a pre-defined half-life (e.g., 500, 520, 540, etc.) and corresponding decay weight (e.g., 0.5, 0.8, etc.) (i.e., providing a plurality of weights). For example, assuming a half-life of 540 days, if a rating was created a year and a half ago, its decay weight can be 0.5. In some embodiments, the recommendation score for the entity can be determined based on a decaying function as follows:" Examiner's note: All ratings associated with a best numerical rating or star get decayed. Under BRI, wherein the age for each sentiment score of the plurality of sentiment scores falls into one of the plurality of age windows can be interpreted as the ratings and reviews that get a decay weight applied that corresponds to a pre-defined half-life.) Accordingly, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention, having the teachings of NI, HABIBABADI, BURNETT, CHATTERJEE, and VIRTUE before them, to include VIRTUE’s decaying function and pre-defined half-life with corresponding decay weights in NI, HABIBABADI, BURNETT, and CHATTERJEE’s multi-channel customer sentiment determination system. One would have been motivated to make such a combination in order to determine a score based on how long ago was a user review or rating created (VIRTUE [0055]). Regarding Claim 7: NI in view of HABIBABADI, BURNETT, CHATTERJEE, and VIRTUE teaches the elements of claim 6 as outlined above. VIRTUE further teaches: weighting, by the health scorer machine learning model, the weighted health index score based on the age weight that corresponds to the corresponding age window into which each sentiment score of the plurality of sentiment scores falls and the [...] score [...] for each sentiment score of the plurality of sentiment scores . (VIRTUE [0054] teaches: "The sentiment value can be determined based on a generally known sentiment classifier. For example, the sentiment classifier can evaluate text-based feedback in an unsolicited recommendation to determine a sentiment value. The sentiment value can range from −1 to 1 where a value of −1 indicates very negative sentiment and a value of 1 indicates very positive sentiment. In some embodiments, the unsolicited recommendation is assigned a score of 1 if its sentiment value is between a first pre-defined range (e.g., between −1 and −0.6). In some embodiments, the unsolicited recommendation is assigned a score of 2 if its sentiment value is between a second pre-defined range (e.g., between −0.6 and 0)." VIRTUE [0055] teaches: "In some embodiments, ratings and reviews provided under a legacy rating scheme can be decayed. For example, in some embodiments, a decaying function can be applied to all ratings and reviews associated with a best numerical (or star) rating. For example, the decaying function can be applied to all ratings and reviews associated with a numerical (or star) rating of 5 with 5 being the best rating. In some embodiments, the decaying function can have a pre-defined half-life (e.g., 500, 520, 540, etc.) and corresponding decay weight (e.g., 0.5, 0.8, etc.) (i.e., providing a plurality of weights). For example, assuming a half-life of 540 days, if a rating was created a year and a half ago, its decay weight can be 0.5. In some embodiments, the recommendation score for the entity can be determined based on a decaying function as follows: ∑ w e i g h t n * s c o r e n * d e c a y ∑ w e i g h t n * d e c a y Examiner’s note: Under BRI, the weighted health index score can be interpreted as the recommendation score resulting from the decaying function discloses in VIRTUE [0054], wherein the recommendation score is computed based on the decay (i.e., based on age weight that corresponds to the corresponding age window into which each sentiment score of the plurality of sentiments scores falls ) and the s c o r e n (i.e., the […] score […] for each sentiment score of the plurality of sentiment scores ).) CHATTERJEE further teaches: weighting […] based on […] the legitimacy score for each data channel identifier for each sentiment score of the plurality of sentiment scores . (CHATTERJEE [col. 18, lines 12-15] teaches: "The source quality scoring component 162 is configured to assign a score based on the relative quality of the source dependent on the source type. For example, if the source type is news, it gives more weight to the New York Times than the Weekly World News. The author quality scoring component 163 is configured to, depending on source type, give more weight to higher reputation authors. For example, for social media tweets, it prefers articles from posters with larger followings. For review sites, it prefers posters listed as verified purchasers and/or with higher numbers of useful reviews.") Regarding Claim 12: NI in view of HABIBABADI, BURNETT, and CHATTERJEE teaches the elements of claim 11 as outlined above. Additionally, the claim recites similar limitations as corresponding claim 5 and is rejected for similar reasons as claim 5 using similar teachings and rationale. Regarding Claim 13: NI in view of HABIBABADI, BURNETT, and CHATTERJEE teaches the elements of claim 11 as outlined above. Additionally, the claim recites similar limitations as corresponding claim 6 and is rejected for similar reasons as claim 6 using similar teachings and rationale. Regarding Claim 14: NI in view of HABIBABADI, BURNETT, CHATTERJEE, and VIRTUE teaches the elements of claim 13 as outlined above. Additionally, the claim recites similar limitations as corresponding claim 7 and is rejected for similar reasons as claim 7 using similar teachings and rationale. Regarding Claim 19: NI in view of HABIBABADI, BURNETT, and CHATTERJEE teaches the elements of claim 18 as outlined above. Additionally, the claim recites similar limitations as corresponding claims 5 and 12 and is rejected for similar reasons as claims 5 and 12 using similar teachings and rationale. Regarding Claim 20: NI in view of HABIBABADI, BURNETT, and CHATTERJEE teaches the elements of claim 18 as outlined above. Additionally, the claim recites similar limitations as corresponding claims 6, 7, 13, and 14 and is rejected for similar reasons as claims 6, 7, 13, and 14 using similar teachings and rationale. Conclusion Any inquiry concerning this communication or earlier communications from the examiner should be directed to Alvaro S Laham Bauzo whose telephone number is (571)272-5650. The examiner can normally be reached Mon-Fri 7:30 AM - 11:00 AM | 1:00 PM - 5:30 PM ET. Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice. If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Usmaan Saeed can be reached on (571) 272-4046. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300. Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000. /A.S.L./Examiner, Art Unit 2146 /USMAAN SAEED/Supervisory Patent Examiner, Art Unit 2146 Application/Control Number: 18/464,905 Page 2 Art Unit: 2146 Application/Control Number: 18/464,905 Page 3 Art Unit: 2146 Application/Control Number: 18/464,905 Page 4 Art Unit: 2146 Application/Control Number: 18/464,905 Page 5 Art Unit: 2146 Application/Control Number: 18/464,905 Page 6 Art Unit: 2146 Application/Control Number: 18/464,905 Page 7 Art Unit: 2146 Application/Control Number: 18/464,905 Page 8 Art Unit: 2146 Application/Control Number: 18/464,905 Page 9 Art Unit: 2146 Application/Control Number: 18/464,905 Page 10 Art Unit: 2146 Application/Control Number: 18/464,905 Page 11 Art Unit: 2146 Application/Control Number: 18/464,905 Page 12 Art Unit: 2146 Application/Control Number: 18/464,905 Page 13 Art Unit: 2146 Application/Control Number: 18/464,905 Page 14 Art Unit: 2146 Application/Control Number: 18/464,905 Page 15 Art Unit: 2146 Application/Control Number: 18/464,905 Page 16 Art Unit: 2146 Application/Control Number: 18/464,905 Page 17 Art Unit: 2146 Application/Control Number: 18/464,905 Page 18 Art Unit: 2146 Application/Control Number: 18/464,905 Page 19 Art Unit: 2146 Application/Control Number: 18/464,905 Page 20 Art Unit: 2146 Application/Control Number: 18/464,905 Page 21 Art Unit: 2146 Application/Control Number: 18/464,905 Page 22 Art Unit: 2146 Application/Control Number: 18/464,905 Page 23 Art Unit: 2146 Application/Control Number: 18/464,905 Page 24 Art Unit: 2146 Application/Control Number: 18/464,905 Page 25 Art Unit: 2146 Application/Control Number: 18/464,905 Page 26 Art Unit: 2146 Application/Control Number: 18/464,905 Page 27 Art Unit: 2146 Application/Control Number: 18/464,905 Page 28 Art Unit: 2146 Application/Control Number: 18/464,905 Page 29 Art Unit: 2146 Application/Control Number: 18/464,905 Page 30 Art Unit: 2146 Application/Control Number: 18/464,905 Page 31 Art Unit: 2146 Application/Control Number: 18/464,905 Page 32 Art Unit: 2146
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