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 .
Claim Rejections – 35 USC § 103
1. 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 (i.e., changing from AIA to pre-AIA ) 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.
2. 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.
3. Claims 1-20 are rejected under 35 U.S.C. 103 as being unpatentable over Farafonova et al. (US 20240388655 A1 hereinafter, Farafonova ‘655) in view of Serban (US 20230008822 A1 hereinafter, Serban ‘ 822).
Regarding claim 8; Farafonova ‘655 discloses a system (Fig. 2, Computing Device 202), comprising:
a memory (Fig. 2, Storage Components 208),
and a processor (Fig. 2, Processor(s) 240), coupled to the memory, wherein the processor performs operations (i.e. This disclosure describes a computing device that includes a memory and one or more processors implemented in circuitry in communication with the memory. Paragraph 0009);
the operations comprising: configuring an anti-spambot application (Fig. Caller Applications 108 & 208) that incorporates mechanisms for human-computer interaction (i.e. Caller application 108 may perform call screening of an incoming call by answering the call to establish the call between computing device 102 and remote computing device 136 and by conducting a natural language conversation with the party calling from remote computing device 136 using a human-like voice with human-like vocal characteristics. Paragraph 0044);
in response to determining that a telephonic call from a caller to a callee is a spam call, switching to a mode in which the anti-spambot application initiates a conversation with the caller (i.e. As computing device 202 performs call screening of an incoming call and conducts a natural language conversation with the calling party, computing device 202 may be able to determine, based on the conversation being conducted with the calling party, whether the incoming call is a spam call. For example, computing device 202 may determine based on the pattern of utterances received from the calling party, keywords contained within the utterances received from the calling party, or any other relevant contextual information, whether the incoming call is a spam call and/or may determine the likelihood (e.g., probability) that the incoming call is a spam call. Paragraphs 0106 & 0116-0117)
selecting a script from a predetermined set of scripts based on content included in the telephonic call (i.e. If computing device 202 determines that the caller is the spam caller type, computing device 202 may determine the set of candidate replies to be: “Wrong number” and “Take me off your list”. Paragraphs 0114 & 0118);
generating synthesized speech from the script (i.e. Caller application 108 may receive, from remote computing device 136, utterances, such as words, phrases and sentences spoken by a user of remote computing device 136, and to generate natural language utterances, such as spoken words, phrases and sentences, that are sent to remote computing device 136, such as by audibly outputting the natural language utterances in the call. Paragraph 0044)
based on voice characteristics of the callee (i.e. Caller application 208 may determine the one or more candidate replies based on contextual information, such as contextual information associated with the call. Such contextual information may include previous utterances in the conversation during the call (e.g., words, phrases, and/or sentences previously spoken by the parties on the call), the vocal characteristics (e.g., intonation) of utterances received from the remote computing device. Paragraph 0089)
and conversing with the caller by using the synthesized speech (i.e. A real-time transcript 116A of the conversation taking place in the call between computing device 102 and remote computing device 136. As can be seen in the real-time transcript 116A of the conversation, caller application 108 may start off the conversation by greeting the party using remote computing device 136 and by asking for the purpose of the call (e.g., “Go ahead and say why you're calling”). Paragraph 0048).
Examiner reasonably believes that Farafonova ‘655 discloses a predetermined script as expressed above. However, Examiner cites Serban ‘822 to cure any deficiencies of Farafonova ‘655.
Serban ‘822 discloses a predetermined script (i.e. The voice samples include audio of a subscriber reading a predetermined script, such as a script including a variety of spoken words, phrases, sounds, formants, etc. Collectively, the speech from the predetermined script provides a representative sample of the subscriber's speech (e.g., vowel sounds, consonant sounds, common words or phrases, etc.). Paragraphs 0046-0047)
Farafonova ‘655 and Serban ‘822 are combinable because they are from same field of endeavor of speech systems (Serban ‘822 at “Background”).
Before the effective filing date, it would have been obvious to a person of ordinary skill in the art to modify the speech system as taught by Farafonova ‘655 by adding a predetermined script as taught by Serban ‘822. The motivation for doing so would have been advantageous eliminate or terminate unwanted and/or unsolicited nuisance phone calls because nuisance phone calls can be used in furtherance of scams, to commit fraud, or to otherwise harm or inconvenience call recipients. Therefore, it would have been obvious to combine Farafonova ‘655 with Serban ‘822 to obtain the invention as specified.
Regarding claim 9; Farafonova ‘655 discloses wherein the operations further comprising: monitoring, by the anti-spambot application, conversations in phone calls and social media, based on permission settings in a profile of the callee and sampling vocal characteristics to detect spamming information and topics in real time. (i.e. When computing device 202 performs call screening of an incoming call, computing device 202 may execute caller application 208 to send data to UI module 206 that causes UIC 204 to display GUI 414, which may be a call screening user interface that includes a real-time transcript 416 of the natural language conversation being conducted by computing device 202 with the calling party as part of the call screening. Computing device 202 may, in response to detecting that the incoming call is likely to be a spam call, output, in GUI 414, a user interface element 418 indicating the call is likely to be a spam call.. Paragraph 0047 & 0108)
Regarding claim 10; Farafonova ‘655 discloses wherein the synthesized speech is in a language and locale of the callee, the operations further comprising: evaluating a conversation based on an evaluation criteria including conversation length and reaction of the caller (i.e. Caller application 108 may, while conducting the conversation, detect a prolonged silence during the call, such as by determining that caller application 108 has not received an utterance from remote computing device 136 for a certain amount of time (e.g., 5 seconds, 10 seconds, etc.).). Paragraph 0049)
and adjusting and improving the predetermined set of scripts for future interaction with callers (i.e. Caller application 108 may, in response to detecting the prolonged silence, prompt the calling party to speak (e.g., “I'm sorry I didn't catch that. What did you say?” Caller application 108 may conduct the conversation to gather information regarding the call, such as the name of the caller and the purpose of the call. As such, if caller application 108 determines, based on the conversation that has been conducted, that the calling party has identified themselves but has not stated their purpose for the call, caller application 108 may ask for the purpose of the call (e.g., “go ahead and say why you're calling”). Paragraph 0049)
Regarding claim 11; Farafonova ‘655 discloses wherein the operations further comprising: learning human-computer interaction in previously stored conversations between callers and callees; and categorizing topics, key words, purposes, roles of caller and callee into different human-computer interaction patterns for conducting automated conversation (i.e. Executing, by the one or more processors, an on-device natural language processing model via the mobile computing device; increasing, by the one or more processors using the on-device natural language processing model, a confidence value of the on-device natural language processing model that the call data from the call satisfies the scam call threshold based on one or more matching conditions detected by the on-device natural language processing model, wherein the one or more matching conditions include one or more of: a key-word match between one or more words of a key-word list and any human language utterances within the conversation detected by the on-device natural language processing model; a phrase match with one or more phrases of a phrase list and any of the human language utterances detected within the conversation by the on-device natural language processing model; a caller sentiment match between one or more sentiment classifications on a sentiment watch list and a sentiment assessment by the on-device natural language processing model based on the conversation; and a subject matter match between one or more subject matter categories on a subject matter watch list and a subject matter categorization assessed by the on-device natural language processing model based on the conversation. Paragraph 0183)
Regarding claim 12; Farafonova ‘655 discloses wherein the operations further comprising: generating a corpus of anti-spam question-answer scripts for a plurality of topics according to human-computer interaction patterns during spam calls (i.e. In some examples, scam suspected block 825 may optionally be utilized to indicate that scam protection framework 800 suspects a possible scam call, but confidence that the established call (820) meets a scam call threshold has not yet been met. In such a case, scam protection framework 800 may display script 830 with a question for the user to ask the caller. Paragraph 0137)
translating the corpus of anti-spam question-answer scripts to a plurality of serviced languages (i.e. As shown in FIG. 1A, caller application 108 may, as part of performing call screening of an incoming call from remote computing device 136, send data to UI module 106 that causes UIC 104 to display GUI 114B that includes a real-time transcript 116A of the conversation taking place in the call between computing device 102 and remote computing device 136. As can be seen in the real-time transcript 116A of the conversation, caller application 108 may start off the conversation by greeting the party using remote computing device 136 and by asking for the purpose of the call (e.g., “Go ahead and say why you're calling”). Paragraph 0048);
and saving and updating the corpus of anti-spam question answer scripts (i.e. Computing device 202 may provide the ability for the user to report the spam call to an external computing system that may add the spam call to a spam example repository that may be used for training and testing spam detection systems. Paragraphs 0107-0108)
Regarding claim 13; Farafonova ‘655 discloses wherein the operations further comprising: defining a framework to support the anti-spambot application (i.e. Fig. 8 is a conceptual diagram illustrating a scam protection framework. Paragraph 0019)
and providing graphical user interface for managing the anti-spambot application (i.e. Fig 10 is a conceptual diagram illustrating an example GUI that includes various user selectable options based on a determination by scam protection framework that a call is a scam call. Paragraph 0021).
Regarding claim 14; Farafonova ‘655 discloses wherein the operations further comprising: defining a data structure for storing, tracking, (i.e. Computing device 102 may perform call screening to conduct a natural language conversation with the caller associated with remote computing device 136 and may store a transcript of the conversation and a recording of the conversation for later review by the user of computing device 102. Paragraph 0040),
and analyzing conversation topics and contexts (i.e. The computing device may determine whether a user of the computing device has configured the computing device to analyze call data from the call. While the call is ongoing, the computing device may analyze the call data from the call when the computing device has been configured to allow the analysis. See Abstract).
Regarding claim 1; Claim 1 contains substantially the same subject matter as claim 8. Therefore, claim 1 is rejected on the same grounds as claim 8.
Regarding claim 2; Claim 2 contains substantially the same subject matter as claim 9. Therefore, claim 2 is rejected on the same grounds as claim 9.
Regarding claim 3; Claim 3 contains substantially the same subject matter as claim 10. Therefore, claim 3 is rejected on the same grounds as claim 10.
Regarding claim 4; Claim 4 contains substantially the same subject matter as claim 11. Therefore, claim 4 is rejected on the same grounds as claim 11.
Regarding claim 5; Claim 5 contains substantially the same subject matter as claim 12. Therefore, claim 5 is rejected on the same grounds as claim 12.
Regarding claim 6; Claim 6 contains substantially the same subject matter as claim 13. Therefore, claim 6 is rejected on the same grounds as claim 13.
Regarding claim 7; Claim 7 contains substantially the same subject matter as claim 14. Therefore, claim 7 is rejected on the same grounds as claim 14.
Regarding claim 15; Claim 15 contains substantially the same subject matter as claim 8. Therefore, claim 15 is rejected on the same grounds as claim 8. However, claim 15 further discloses a computer program product, the computer program product comprising a computer readable storage medium having computer readable program code embodied therewith, the computer readable program code when executed is configured to perform operations. Paragraph 0010 of Farafonova ‘655 discloses wherein the disclosure describes a non-transitory computer-readable storage medium encoded with instructions that, when executed by one or more processors, cause the one or more processors to perform operations.
Regarding claim 16; Claim 16 contains substantially the same subject matter as claim 9. Therefore, claim 16 is rejected on the same grounds as claim 9.
Regarding claim 17; Claim 17 contains substantially the same subject matter as claim 10. Therefore, claim 17 is rejected on the same grounds as claim 10.
Regarding claim 18; Claim 18 contains substantially the same subject matter as claim 11. Therefore, claim 18 is rejected on the same grounds as claim 11.
Regarding claim 19; Claim 19 contains substantially the same subject matter as claim 12. Therefore, claim 19 is rejected on the same grounds as claim 12.
Regarding claim 20; Claim 20 contains substantially the same subject matter as claim 13. Therefore, claim 20 is rejected on the same grounds as claim 13.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to MARCUS T. RILEY, ESQ. whose telephone number is (571)270-1581. The examiner can normally be reached 9-5 M-F.
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MARCUS T. RILEY, ESQ.
Primary Examiner
Art Unit 2654
/MARCUS T RILEY/Primary Examiner, Art Unit 2654
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