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 .
DETAILED ACTION
Claims 1-17, 20, 21, 22 have been examined.
Continued Examination Under 37 CFR 1.114
A request for continued examination 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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 11/14/25 has been entered.
Response to Arguments
Applicant's arguments with respect to the claims have been considered but are moot in view of the new ground(s) of rejection. On 11/1425, Applicant amended the independent claims. Applicant’s Remarks address these amended features. See the new rejection with the addition of Balasubramanian to a 103 that addresses these new features.
Also, the current claims are found to pass 101 with the particular trained scoring models based on particular parameters, different internal software applications and selecting between different external communication channels and then sending via a particularly selected external communication channel.
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, 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.
Claims 1-4, 6, 8-14, 16, 20-22 are rejected under 35 U.S.C. 103 as being unpatentable over Angeli (20220210106) in view of Balasubramanian “Bala” (20240070210).
Claims 1, 11, 21. Examiner notes that Internal executable resource is interpreted as an app/software application/service (see Applicant Spec at [0015]). Examiner notes external services in Applicant Spec at “[31]… External services can include search engine services, social networks, and/or sponsored content distribution services, to list a few examples.”.
Angeli discloses an apparatus comprising at least one processor and at least one non- transitory computer-readable storage medium storing instructions that, when executed by the at least one processor, cause the apparatus to:
for each external service candidate communication of a plurality of external service candidate communications ([52]),
generate one or more candidate selection scores, specific to the external service candidate communication, associated with a candidate element of one or more candidate elements using one or more trained candidate selection scoring models (see score at [57, 201], note that at [201] that the score is for a specific communication “[201]… a confidence score or metric indicating a confidence that a communication”; see semantic meaning and/or topics and content which reads on text elements or keywords at [200, 201]; Examiner notes based on Applicant Spec at [16] that candidate element can be interpreted as text element or keyword).
Angeli further discloses wherein each candidate element of the one or more candidate elements is associated with two or more internal executable resources of a plurality of internal executable resources (note that channel/service/platform can also refer to app/application/api, see app and channels/service/platform at [31, 40] and also see application and forum at [151] to note variety of apps; note that there are multiple communications and that these communications can use multiples applications like first party applications and third party applications “[36]… In at least one example, the communication can be sent via a first communication channel, which can comprise a text communication, an email communication, a communication sent via a first-party application, a communication sent via a third-party application, a social media communication, and/or the like” and also first and thirs party apps at [33]; and note that context and terms are analyzed across different services/platforms/apps and then based on that context/term the communications channel can be switched “[0024] Techniques described herein thus support optimized switching between communication channels and/or services/platforms based on context. The contextual analysis, in one example, can be through model interpretation of temporal terms. Messages from different services/platforms can also be consolidated on one interface, and, in some examples, can be accessible via a software developer kit (SDK) platform and/or application programming interface (API)… techniques described herein allow authorization triggers to be set such that certain communications are automatically routed to certain workers/devices based on context in the communication”.; wide range of apps; hence, Angelli discloses multiple apps and that a keyword/text like coupon can be associated with multiple different apps).
Angeli further discloses generate one or more candidate selection contexts specific to the external service candidate communication (see [52] and context; also see context at [200] and note that the context is specific to a communication “[200]… the context determination component 118 can determine one or more of…associated with a communication”), wherein each candidate selection context comprises a mapping of the candidate element with an internal executable resource of a plurality of internal executable resources and represents a predicted relevance of the candidate element to the internal executable resource (see “optimize” and picking the “appropriate” or “better” channel/platform/app of many based on context including topic, content [21]; also see parsing for context based on vocabulary of the communication so that an appropriate application can be selected or used [23]), wherein the candidate selection context is generated based at least in part on communication corpus metadata and internal executable resource data, wherein the internal executable resource data comprises interaction events representing electronic interactions performed by client computing devices with respect to one or more internal executable resources of the plurality of internal executable resources (First, Examiner notes that based on Applicant Spec above, the current feature for internal executable resource can be interpreted broadly as a range of software applications or services; And, as presently claimed, Angeli discloses these features as follows: see [52] and context, see [43, 52] for interaction events and also context and topic and classifier; so Angeli clearly determines the context of communications that are sent; see metadata at [42, 54, 96]; for internal executable resource, see service/platform/communication channels at [42, 54, 96]; note in [20] that “contextual communication id described” and that a platform or service or channel or “an application (eg first-party, third party, etc)” is used and selected based on the context and in [21] that based on context data that a channel or application or platform is switched to and used, and “[0024] Techniques described herein thus support optimized switching between communication channels and/or services/platforms based on context. The contextual analysis, in one example, can be through model interpretation of temporal terms. Messages from different services/platforms can also be consolidated on one interface, and, in some examples, can be accessible via a software developer kit (SDK) platform and/or application programming interface (API).” And note application/app at [23] And “[40]… communications that originate from the merchant computing device 106 can be associated with any of the communication channels described above (e.g., a text communication, an email communication, a communication sent via a first-party application, a communication sent via a third-party application, a social media communication, and/or the like)… In some examples, the context determination component 118 can recommend which communication channel(s) and/or platform(s) for sending communications and/or can automatically switch between different communication channel(s) and/or platform(s) to optimize communication as described herein.”; and see forum or application context at [151], and see application and application context at [152], also see application and user input and user interaction and context at [167]; also, in further regards to matching/mapping, Angeli discloses machine learning and scoring features for context and the suggested content to present, see score at [207, 213, 57]; hence Angeli clearly discloses analyzing context and switch/adapting/using from among many an application/service/channel/platform that better matches/maps/correlates with the context analyzed and the specific interaction events and metadata and context; also Angeli further discloses candidate elements as text elements or keywords, see Applicant Spec at [16] that candidate element can be interpreted as text element or keyword, and candidate element as semantic meaning and/or topics and content and the context these are used in at [200, 201]; note that choosing channel /platform also includes application [21]).
Examiner notes Applicant Spec at [66, 148] for the following features. Angeli further discloses and wherein relevance is determined based at least in part on first interaction events representing first electronic interactions with first renderable content associated with the first internal executable resource (Angeli shows tracking user profile and user action history and preferences and intent based on action and also preferred app/channel/internal service: “[62] Customer profiles can store customer data including…customer information (e.g., name, phone number, address, banking information, identifier(s)… customer preferences (e.g., learned or customer-specified), purchase history data (e.g., identifying one or more items purchased (and respective item information),…a customer profile can include merchant preferences with respect to which communication channel(s) and/or platform(s) they prefer to use for communication, which identifier(s) associated with communication channel(s) they prefer to use for communication, etc….historical communication data associated with a customer (e.g., communications sent, communications received, responses, response time, etc.) can be associated with a customer profile.” And “[167]… user's interactions with the user interface 720 are analyzed using, e.g., natural language processing techniques, to determine context or intent of the user, which may be treated in a manner similar to “direct” user input.”).
Angeli does not expliclity disclose wherein the predicted relevance is determined based at least in part on first interaction events representing first electronic interactions with first renderable content. However, Bala discloses tracking shopper history [38] and a user database with user information and user shopping preferences [39]. And, Bala discloses content relevance based on tracking history of a user or users and other related items based on that history (“[0042] …The content suggesting engine 322 may be configured to retrieve content items related to the first item, and/or additional content items related to second items that are often bought together (by this user or other users) with the first item.”). And, Bala discloses it is desirable to have data covering all parts of a user content related journey (“[3]… But existing content providers might not be aware of such correlations, and/or might not have enough high-quality data covering all parts of the user journey (from seeing an content item to an action event) to provide guidance to content providers.”). And Bala discloses a variety of tracking metrics related to content interaction with previous content in order to generate best content to present [51, 58, 81] and these interaction data from previous content are used to predict a relevance or indicate a likelihood [59, 61, 62, 81]. Therefore, it would have been obvious to one having ordinary skill in the art at the time the invention was made to add Bala’s predict relevance based on interaction events with content to Angeli’s user preferences based on user history and interaction. One would have been motivated to do this in order to better present content that is likely to be interacted with.
Angeli further discloses determine, based at least in part on the candidate communication corpus, the communication corpus metadata and the internal executable resource data, an external service of a plurality of external services to which the external service candidate communication should be transmitted ([52, 54]; also for corpus see channel/platform selection based on context at [52] and [21, 41] shows that context includes topic or content of the communication and [203] shows that context includes keywords, also not obvious statement for using specifics of context options preceding ; for metadata see channel/platform selection and metadata at [54], and for internal executable/app/channel/platform data see [62] and customer profile data on channel used by customer and historical communication data associated with the customer, also note data on merchant and/or customer preferred channel is internal executable/app/channel data).
Angeli further discloses determine one or more renderable content identifiers based at least in part on the one or more candidate selection contexts (Examiner notes Applicant Spec at [47] for renderable content identifier. Based on Applicant Spec an id/URL can be provided as the renderable content id. And, based on this feature, this can be provided based on context. And, Angeli discloses at [48] providing a content id as URL/link based on context data, “[48]… context data can be used by the communication management component 116 to recommend actions to be performed in association with a responsive communication, such as… embedding a link… the context data can be used to generate a recommendation to attach or associate an object (e.g., receipt, feedback, link, etc.) with a communication and/or associate data”. Also, note other tokens/identifiers associated with the communication at [37-39, 70, 91]).
Angeli further discloses generate the external service candidate communication based at least in part on the one or more candidate selection scores, the one or more candidate selection contexts, and the determined external service to which the external service candidate communication should be transmitted (see create at [47, 204, 208]; see channel/external service selection at [52, 54], see score at [57, 213]). Angeli further discloses wherein the external service candidate communication comprises the candidate element ( Note Applicant Spec at [16] that candidate element can be interpreted as text element or keyword; Angeli discloses that the candidate element can be included in the external communication, that is that the candidate communication for external sending can include particular keyword/text elements: “[47]… such as changing <subject/object/temporal expression> to <temporal expression/preposition>.”, “[48]… That is, in some examples, the context data can be used to generate a recommendation to attach or associate an object (e.g., receipt, feedback, link, etc.) with a communication and/or associate data associated with an event/interaction with a communication.”, “[49]… For example, if an incoming communication is associated with a request to reschedule an appointment, the context data associated with the incoming communication can be used by the communication management component 116 to generate a response or a recommendation for a response that includes an alternative appointment date or time…. to generate a response or recommendation for a response that includes a link to purchase an item from the merchant 104.”; “[0099] In some examples, the context data can be utilized to generate a response or a recommendation for response. For example, if an incoming communication is associated with a request to reschedule an appointment, the context data associated with the incoming communication can be used by the communication management component 116 to generate a response or a recommendation for a response that includes an alternative appointment date or time”, “[0204] In some examples, the context data can be utilized to generate a response or a recommendation for response, or even provide an array of responses to choose from…. In some examples, automatically or semi-automatically generated responses can be associated with attachments, coupons, and/or embedded functionality (e.g., deeplinks, hyperlinks, etc.)”, Examiner notes that in the preceding citations, the candidate element is recommended based on the context and the candidate elements disclosed include subjects, receipts, feedback, response variations, coupons, dates, times, objects, etc.; Examiner further notes scores related to communications being of particular types like transaction related and then particular keywords like particular items or responses can be used as the candidate elements: “[207]… when a communication is associated with a confidence score that satisfies a threshold indicating that the communication is transaction-related,…[0208] In some examples, one or more communications can be exchanged between the customer and the merchant (e.g., via the service provider) to identify particular item(s) to be associated with a transaction. In some examples, at least some of the one or more communications can be handled by a bot or virtual assistant. In some examples, as described above, context data can be utilized to generate responses or recommendations for responses, or even provide an array of responses to choose from to facilitate the one or more communications.”) and the one or more renderable content identifiers (Examiner notes Applicant Spec at [47] for renderable content identifier. Based on Applicant Spec an id/URL can be provided as the renderable content id. And, based on this feature, this can be provided based on context. And, Angeli discloses at [48] providing a content id as URL/link based on context data, “[48]… context data can be used by the communication management component 116 to recommend actions to be performed in association with a responsive communication, such as… embedding a link… the context data can be used to generate a recommendation to attach or associate an object (e.g., receipt, feedback, link, etc.) with a communication and/or associate data”. Also, note other tokens/identifiers associated with the communication at [37-39, 70, 91]).
Angeli further discloses transmit the external service candidate communication to the external service (see send at Fig. 3, item 318).
Claim 2, 12. Angeli further discloses the apparatus of claim 1, wherein at least one candidate selection score of the one or more candidate selection scores represents a classification of the candidate element associated with the at least one candidate selection score, wherein the classification is generated based at least in part on semantic similarities between the candidate element and one or more previously classified candidate elements (see semantic, classify, score at [201]).
Claim 3, 13. Angeli further discloses the apparatus of claim 2, wherein a candidate selection scoring model of the one or more trained candidate selection scoring models is trained using a candidate communication corpus, and wherein the semantic similarities between the candidate element associated with the at least one candidate selection score and the one or more previously classified candidate elements are determined using the candidate selection scoring model (see train and model and previous communications at [54]).
Claim 4, 14. Angeli further discloses the apparatus of claim 3, wherein determining the semantic similarities between the candidate element and the one or more previously classified candidate elements comprises vectorizing the candidate element and the one or more previously classified candidate elements using the candidate selection scoring model (see semantic at [46] and vector at [47], see score, classifier, vector at [57]).
Claim 6, 16. Angeli further discloses the apparatus of claim 2, wherein generating the at least one candidate selection score comprises: selecting a previously classified candidate element of the one or more previously classified candidate elements (see train and model and previous communications and classes at [54], see train and classifiers and score at [57]), wherein the selected previously classified candidate element comprises a degree of semantic similarity with the candidate element that is highest among the one or more previously classified candidate elements (see probabilistic semantic which is interpreted as highest probability or highest degree [44, 46]); and assigning a classification label to the candidate element matching a classification label associated with the selected previously classified candidate element (see semantic and label at [44, 46]).
Claim 8. Angeli further discloses the apparatus of claim 1, wherein the candidate element is extracted from one or more raw text objects of a candidate communication corpus (see pars at [22] which is interpreted to read on raw text).
Claim 9. Angeli further discloses the apparatus of claim 8, wherein the one or more candidate selection contexts are generated based at least in part on communication corpus metadata that associates the one or more raw text objects with one or more internal executable resources of the plurality of internal executable resources (see content of communications, channels/platforms of communications, metadata at [54]; also see metadata/service at [42, 96] ).
Claim 10, 20. Angeli further discloses the apparatus of claim 8, wherein the one or more candidate selection contexts are generated based at least in part on communication corpus metadata that associates the one or more raw text objects of the candidate communication corpus to one or more of the interaction events of the internal executable resource data (see content of communication and metadata and platform/service at [54] and see preference and metadata at [42, 96]).
Claim 22. Angeli further discloses the apparatus of claim 1, wherein a candidate selection score of the one or more candidate selection scores represents a programmatically generated ranking that renderable content associated with the one or more renderable content identifiers and transmitted for presentation within an interface (note ranking which reads on score and ranking to pick the best communication channel for that particular context and user [54]; also see score at [213] and sending different content based on the score; for renderable content identifier see link/URL at [48]). While Angeli discloses the preceding and ranking, Angeli does not explicitly disclose likelihood or will result in an electronic interaction with the renderable content subsequent to transmission of the external service candidate communication to the external service, and wherein the plurality of external services comprises one or more search engine services or sponsored content distribution services. Angeli also discloses purchasing items [49]. However, Balasubramanian discloses machine learning [6] and a candidate selection score of one or more candidate selection scores represents a programmatically generated likelihood that renderable content associated with the candidate element transmitted for presentation within an interface will result in an electronic interaction with the renderable content subsequent to transmission of the external service candidate communication to the external service [62]. Balasubramanian also discloses to incite purchase of item [62]. Balasubramanian also further discloses wherein the plurality of external services comprises one or more search engine services or sponsored content distribution services (see search engine at [1] or see third party system as advertiser with advertisements at [23]). Therefore, it would have been obvious to one having ordinary skill in the art at the time the invention was made to add Balasubramanian likelihood and response and external service as search engine or advertiser to Angeli’s ranking of most appropriate channel and content for that particular user. One would have been motivated to do this in order to better present content of interest or better incite purchase (as seen with incite purchase in both prior art).
Claims 5, 7, 15, 17 are rejected under 35 U.S.C. 103 as being unpatentable over Angeli (20220210106) in view of Balasubramanian (20240070210) in view of Cavallari (20220075961).
Claims 5, 15. Angeli discloses the apparatus of claim 2, wherein generating the at least one candidate selection score comprises calculating a similarity between a candidate element context of the candidate element and one or more candidate element contexts of the one or more previously classified candidate elements (See similarity algorithm at [42, 96]; see score and semantic at [201]). Angeli does not explicitly disclose cosine similarity. However, Cavallari discloses using machine learning and content (Abstract) and semantics similarity ([26]) and similarity and cosine similarity [27]. Therefore, it would have been obvious to one having ordinary skill in the art at the time the invention was made to add Cavallari’s cosine similarity to Angeli’s similarity. One would have been motivated to do this in order to better asses similarity.
Claims 7, 17. Angeli does not explicitly disclose apparatus of claim 2, wherein generating the at least one candidate selection score comprises assigning a classification label indicating creation of a new classification category to the candidate element in response to determining that none of the one or more previously classified candidate elements has a degree of semantic similarity with the candidate element that satisfies a predetermined similarity threshold. However, Angeli discloses score and classifiers at ([57, 201]) and topic and create at [200, 216]. And, Cavallari discloses using machine learning and content (Abstract) and using semantic meaning to create new labels when needed [21]. Therefore, it would have been obvious to one having ordinary skill in the art at the time the invention was made to add Cavallari’s creating new labels to Angeli’s labels. One would have been motivated to do this in order to place labels when a new label is needed.
Conclusion
The following prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
aa) Balasubramanian [62] and Bilenko [24, 29] disclose machine learning and likelihood of response and score;
The other cited prior art discloses machine learning and content creation.
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/ARTHUR DURAN/Primary Examiner, Art Unit 3621 1/12/26