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 .
Acknowledgements
Claim 12 is cancelled.
Claims 1-11 and 13-21 are pending.
Applicant provided information disclosure statement.
Allowable Subject Matter
Claims 4, 5, 17, 18 are allowable if rewritten to include all of the limitations of the base claim and any intervening claims, and if the independent claims were amended in such a way as to overcome the rejection(s) under 35 U.S.C. 101, set forth in this Office action. The closest prior art to these claims include Venkataraman (US11978475B1) in further view of Surendran (US20090198654A1) who teaches multiple instance learning procedure in further view of Molahalli (US20220012633A1) who teaches a degree of attention with respect to machine learning. However, with respect to exemplary claim 4, 5, 17, 18, the closest prior art of record, either alone or taken in combination with any other references of record, do not anticipate or render obvious the claimed functionality of claim 4, 5, 17, 18.
Claims 6, 7, 8, 9, 10, 19, 20, and 21 are allowable if rewritten to include all of the limitations of the base claim and any intervening claims, and if the independent claims were amended in such a way as to overcome the rejection(s) under 35 U.S.C. 101, set forth in this Office action. The closest prior art to these claims include Venkataraman (US11978475B1) in further view of Surendran (US20090198654A1) who teaches multiple instance learning procedure in further view of Molahalli (US20220012633A1) who teaches a degree of attention with respect to machine learning in further view of Javeri (20220306125) who teaches a score and retraining based on the score. However, with respect to exemplary claim 6, 7, 8, 9, 10, 19, 20, and 21, the closest prior art of record, either alone or taken in combination with any other references of record, do not anticipate or render obvious the claimed functionality of claim 6, 7, 8, 9, 10, 19, 20, and 21.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(d):
(d) REFERENCE IN DEPENDENT FORMS.—Subject to subsection (e), a claim in dependent form shall contain a reference to a claim previously set forth and then specify a further limitation of the subject matter claimed. A claim in dependent form shall be construed to incorporate by reference all the limitations of the claim to which it refers.
The following is a quotation of pre-AIA 35 U.S.C. 112, fourth paragraph:
Subject to the following paragraph [i.e., the fifth paragraph of pre-AIA 35 U.S.C. 112], a claim in dependent form shall contain a reference to a claim previously set forth and then specify a further limitation of the subject matter claimed. A claim in dependent form shall be construed to incorporate by reference all the limitations of the claim to which it refers.
Claims 19-21 are rejected under 35 U.S.C. 112(d) or pre-AIA 35 U.S.C. 112, 4th paragraph, as being of improper dependent form for failing to further limit the subject matter of the claim upon which it depends, or for failing to include all the limitations of the claim upon which it depends. Claim 19 does not make reference to a claim previous set forth, but rather to claim 20 which is after claim 19. Claim 20 is dependent on claim 19 and claim 21 is dependent on claim 20 and they do not cure the deficiency.
Applicant may cancel the claim(s), amend the claim(s) to place the claim(s) in proper dependent form, rewrite the claim(s) in independent form, or present a sufficient showing that the dependent claim(s) complies with the statutory requirements.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-11 and 13-21 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more than the judicial exception itself.
Regarding Step 1 of subject matter eligibility for whether the claims fall within a statutory category (See MPEP 2106.03), claims 1-11 and 13-21 are directed to non-transitory computer-readable storage medium, computing device, and method.
Regarding step 2A-1, Claims 1-11 and 13-21 recite a Judicial Exception. Exemplary independent claim 1 and similarly claims 13 and 14 recite the limitations of
obtaining a to-be-identified target feature vector, wherein the target feature vector is determined based on interaction content of a target user for a target service, and content restored based on the target feature vector is different from the interaction content; identifying the target feature vector based on a pre-trained risk speech identification model, to obtain an identification result for the target feature vector, wherein the risk speech identification model is trained based on a feature vector corresponding to a target risk speech, and the feature vector corresponding to the target risk speech is obtained by filtering a first feature vector corresponding to historical interaction content with a risk in the target service based on a pre-trained risk speech filtering model; and determining, based on the identification result, whether a risk speech exists in the interaction content, to determine whether there is a risk in triggering execution of the target service.
These limitations, as drafted, are a process that, under its broadest reasonable interpretation cover concepts of obtaining, identifying, filtering, and determining data. The claim limitations fall under the abstract idea grouping of mental process, because the limitations can be performed in the human mind, or by a human using a pen and paper. For example, but for the language of computing device and non-transitory computer-readable storage medium, the claim language encompasses simply obtaining vector data, identifying vector data with respect to a model, obtaining identification result, filtering vector data, and determining whether a risk exists with respect to the vector data. These are mere data manipulation steps that do not require a computer. A user is able to obtain data make determining that include determining a risk.
The claims recite determining a risk and also recite target service which deal with interactions with users as seen in para 00100-00105 in the Applicant’s Specification. These make the claims fall in the abstract idea grouping of certain methods of organizing human activity (mitigating a risk and interactions between people). It is clear the limitations recite these abstract idea groupings, but for the recitations of generic computer components. The mere nominal recitations of generic computer components does not take the limitations out of the mental process and certain methods of organizing human activity grouping. The claims are focused on the combination of these abstract idea processes.
Regarding step 2A-2- This judicial exception is not integrated into a practical application, and the claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception.
The claim recites the additional elements of computing device, processor, storage, and non-transitory computer-readable storage medium.
These components are recited at a high level of generality, and merely automate the steps. Each of the additional limitations is no more than mere instructions to apply the exception using a generic computer component.
The combination of these additional elements is no more than mere instructions to apply the exception using a generic computer components or software. Accordingly, even in combination, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea.
Further, the claims do not provide for recite any improvements to the functioning of a computer, or to any other technology or technical field; applying or using a judicial exception to effect a particular treatment or prophylaxis for a disease or medical condition; applying the judicial exception with, or by use of, a particular machine; effecting a transformation or reduction of a particular article to a different state or thing; or applying or using the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole is more than a drafting effort designed to monopolize the exception.
The dependent claims have the same deficiencies as their parent claims as being directed towards an abstract idea, as the dependent claims merely narrow the scope of their parent claims. For example, the dependent claims further describe additional variables such as a second and third feature vector. In addition, the dependent claims further recite additional variables such as degree of attention and target score.
Regarding step 2B the claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because claim 1 recites
Method, however method is not considered an additional element
Claims 4 and 17 recite multiple instance learning algorithm
Claim 13 recites computing device, processor, and storage
Claim 14 recites processor and non-transitory computer-readable storage medium
When looking at these additional elements individually, the additional elements are purely functional and generic the Applicant specification states a general purpose computer in para 00157.
When looking at the additional elements in combination, the Applicant’s specification merely states a general purpose computer as seen in para 00157. The computer components add nothing that is not already present when the steps are considered separately. See MPEP 2106.05
Looking at these limitations as an ordered combination and individually adds nothing additional that is sufficient to amount to significantly more than the recited abstract idea because they simply provide instructions to use generic computer components, recitations of generic computer structure to perform generic computer functions that are used to "apply" the recited abstract idea. Thus, the elements of the claims, considered both individually and as an ordered combination, are not sufficient to ensure that the claim as a whole amounts to significantly more than the abstract idea itself.
Since there are no limitations in these claims that transform the exception into a patent eligible application such that these claims amount to significantly more than the exception itself, claims 1-11 and 13-21are rejected under 35 U.S.C. 101.
Claim Rejections - 35 USC § 102
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.
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claim(s) 1, 2, 3, 13, 14, 15, and 16 is/are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Venkataraman (US11978475B1).
Regarding claim 1, and similarly claim 13 and 14, Venkataraman teaches
A data processing method, comprising (See abstract-Systems, apparatuses, methods, and computer program products are disclosed for predicting an emotion based on speech and text derived from the speech. ) This shows a method.
A computing device, wherein the device comprises: a processor; and a storage, configured to store computer-executable instructions, wherein when the executable instructions are executed, the processor is enabled to perform a data processing method, the method comprising (See abstract-Systems, apparatuses, methods, and computer program products are disclosed for predicting an emotion based on speech and text derived from the speech.) (See figure 1 and 2) This shows a system with devices. It includes memory and a processor. (See col. 9-10-The processor 202 may be configured to execute software instructions stored in the memory 204 or otherwise accessible to the processor (e.g., software instructions stored on a separate storage device 106, as illustrated in FIG. 1 ))
A non-transitory computer-readable storage medium, wherein the computer-readable storage medium is configured to store computer-executable instructions, and when the executable instructions are executed by a processor, the processor is caused to implement a data processing method (See figure 1 and 2) (See col. 9-10-Memory 204 is non-transitory and may include, for example, one or more volatile and/or non-volatile memories. In other words, for example, the memory 204 may be an electronic storage device (e.g., a computer readable storage medium). The memory 204 may be configured to store information, data, content, applications, software instructions, or the like, for enabling the apparatus to carry out various functions in accordance with example embodiments contemplated herein.) This teaches memory.
obtaining a to-be-identified target feature vector, wherein the target feature vector is determined based on interaction content of a target user for a target service The system obtains a target feature vector such as audio hidden vectors and context hidden vectors from the BLSTMs. The target feature vector can also correspond to the final feature vectors as seen in item 534 of fig. 5. The vectors are determined based interaction content of a target user such as customer/agent for a target service such as bank services as seen in the background section. (See col. 1-2).
and content restored based on the target feature vector is different from the interaction content Examiner interprets this to mean the contents of the interaction are hidden for the privacy of the target user. Venkataraman teaches this since fig. 5B teaches the vectors are hidden and the final feature vectors are based on the hidden vectors. Hidden vectors do now show their contents. This is also in line with Applicant’s interpretation as seen in para 0028 of Specifications.
identifying the target feature vector based on a pre-trained risk speech identification model, to obtain an identification result for the target feature vector. The target feature vectors such as audio hidden, context hidden, or final vectors are based on the pre-trained risk speech identification model which corresponds to the BLSTM. The BLSTM is pre-trained since it already has been trained on historical interaction data as seen in col. 16. The BLSTM can determine risk in the customer speech such as if they are angry as seen in fig. 3. Angry customers are a risk to the business.
wherein the risk speech identification model is trained based on a feature vector corresponding to a target risk speech The BLSTM is trained on previous interactions/conversations which correspond to a feature vector (See col. 16- Further, the predicted emotion may be utilized or compiled into training data 430. The training data 430 may be utilized, along with the text and audio, to refine and/or retrain any of the BLSTM networks as described herein) (fig. 3 also shows previous interaction/conversation data)
and the feature vector corresponding to the target risk speech is obtained by filtering a first feature vector corresponding to historical interaction content with a risk in the target service based on a pre-trained risk speech filtering model The previous interactions/conversations (i.e. feature vector) also had to go through fig. 5c in order for the system to obtain them with their values as seen in previous data in fig. 3. They went through a filtering process as seen in item 536. Max-pool layer corresponds to a filtering layer that is part of the BLSTM model (i.e. pre-trained risk speech filtering model). BLSTM model corresponds to both filtering model and identification model since the art teaches multiple BLSTM models.
and determining, based on the identification result, whether a risk speech exists in the interaction content, to determine whether there is a risk in triggering execution of the target service. (See fig. 3) Examiner interprets the identification result to be a predicted emotion of the customer. Figure 3 shows that a risk speech exists since the predicted emotion is angry. This means there will be risk to the bank if the customer continues down the path to obtain bank services so a resolution needs to enacted (i.e. action).
Regarding claim 2, and similarly claim 15, Venkataraman further teaches
wherein the determining, based on the identification result, whether a risk speech exists in the interaction content, to determine whether there is a risk in triggering execution of the target service comprises: upon determining, based on the identification result, that a risk speech exists in the interaction content, determining a second feature vector corresponding to the risk speech in the target feature vector; (See col. 14-15-Turning to FIG. 3 , a graphical user interface (GUI) 302 is provided that illustrates what an agent sees after a prediction is made. As noted previously, the agent may interact with the emotion prediction system 102 by directly engaging with input-output circuitry 208 of an apparatus 200 comprising a system device 104 of the emotion prediction system 102. In such an embodiment, the GUI shown in FIG. 3 may be displayed to the agent by the apparatus 200.) This shows after a prediction of a risk is made such as customer being angry, the system determines a second feature vector such as the previous calls of the customer. These are also feature vectors since they were previously determined.
stopping executing the target service, and generating risk prompt information corresponding to the second feature vector, to notify, based on the risk prompt information, the target user that a risk speech exists in the interaction content. (See figure 3) This shows the system will stop the servicing with the customer and transfer them to an agent. Figure 3 also shows risk prompt information such as the customer is angry. Figure 3 purpose is to notify the agent.
Regarding claim 3, and similarly claim 16, Venkataraman further teaches
wherein before the identifying the target feature vector based on a pre-trained risk speech identification model, to obtain an identification result for the target feature vector, the method further comprises: obtaining the first feature vector corresponding to the historical interaction content with a risk in the target service, wherein content restored based on the first feature vector is different from the historical interaction content with a risk; The system is able to receive historical data of the customer’s previous calls as seen in figure 3. The historical data corresponds to the first feature vector. This data would compromise hidden vectors which correspond to content restored as taught in the independent claim.
filtering the first feature vector based on the pre-trained risk speech filtering model, to obtain a third feature vector, and determining the feature vector corresponding to the target risk speech based on the third feature vector; The first feature vector is filtered using max-pooling layer of the BLSTM. The filter featured vector corresponds to the third feature vector (i.e. final vector with reduced dimensions).
and training a risk speech identification model based on the feature vector corresponding to the target risk speech, to obtain the trained risk speech identification model. The BLSTM system which is the speech identification model is trained with this data such as historical data as seen in fig. 4B and col. 16.
Claim Rejections - 35 USC § 103
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.
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.
Claim(s) 11 is/are rejected under 35 U.S.C. 103 as being unpatentable over Venkataraman (US11978475B1) in further view of Son (20210227184).
Regarding claim 11, Venkataraman further teaches
wherein the obtaining a to-be-identified target feature vector comprises: obtaining the interaction content of the target user for the target service; and dividing the interaction content into a plurality of pieces of sub-content, and separately coding… to obtain a plurality of the to-be-identified target feature vector. (See fig. 4A) This shows obtaining interaction data such as speech capturing. The content is divided into sub-content such as acoustic features and speech to text and separately coded into vectors such as audio hidden vectors and text hidden vectors.
However it is not clear that the speech captured is coded with respect to preset coding rule, however Son teaches based on a preset coding rule (See para 0209- extracts physical characteristics such as the voice of the visitor from the input data 1 based on the Mel-frequency cepstrum (MFCC)/Linear Predictive Coding (LPC) algorithm,) LPC algorithm is a preset coding rule.
Venkataraman and Song are analogous art because they are from the same problem solving area analyzing user’s speech/voice. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have combined Venkataraman’ invention by incorporating the method of Son because Venkataraman would also be able to implement LPC when coding the speech captured by the user. This would make the system of Venkataraman more sophisticated since LPC improves the way the system recognizes speech (i.e. what the user is saying) which would help make the emotion predictions more accurate.
Conclusion
The prior art made of record and not relied upon considered pertinent to Applicant’s disclosure.
Surendran (US20090198654A1) who teaches multiple instance learning procedure.
Molahalli (US20220012633A1) who teaches a degree of attention with respect to machine learning.
Javeri (20220306125) who teaches a score and retraining based on the score.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to MUSTAFA IQBAL whose telephone number is (469)295-9241. The examiner can normally be reached Monday Thru Friday 9:30am-7:30 CST.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Beth Boswell can be reached at (571) 272-6737. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/MUSTAFA IQBAL/Primary Examiner, Art Unit 3625
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