TWM625393U - Personalized recommendation device for insurance products - Google Patents
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Abstract
本新型為揭露一種保險商品的個人化推薦裝置,其包含訓練裝置與運行裝置。訓練裝置提供複數個待訓練模組,且用於根據使用者歷史行為資訊與保險商品資訊對上述複數個待訓練模組進行訓練,以獲得複數個以訓練模組與複數個已訓練資料,並儲存於資料與模型資料庫中。運行模組訊號連接訓練模組,用於根據推薦演算法需求而從資料與模型資料庫儲存的複數個已訓練模組與複數個已訓練資料選取至少一訓練模型與至少一已訓練資料,並基於選取的至少一已訓練資料與輸入資訊而運行選取的至少一已訓練模組,進而產生至少一多層次保險推薦結果。The present invention discloses a personalized recommendation device for insurance products, which includes a training device and a running device. The training device provides a plurality of modules to be trained, and is used for training the above-mentioned plurality of modules to be trained according to the user's historical behavior information and insurance product information, so as to obtain a plurality of training modules and a plurality of trained data, and Stored in the data and model database. The running module signal is connected to the training module for selecting at least one training model and at least one training data from the plurality of trained modules and the plurality of trained data stored in the data and model database according to the requirements of the recommended algorithm, and The selected at least one trained module is run based on the selected at least one trained data and the input information, thereby generating at least one multi-level insurance recommendation result.
Description
本新型是有關於一種保險商品個人化推薦裝置,特別是涉及一種具有依據使用者行為,以多層次保險推薦結果推薦適合消費者的險種、保險商品類型、給付項目額度與保險商品的個人化推薦裝置。The invention relates to a device for personalizing insurance products, in particular to a device for recommending insurance products, insurance product types, payment item amounts and insurance products suitable for consumers with multi-level insurance recommendation results based on user behavior. device.
現今社會結構在改變,且人生風險無處不在,使得保險的重要性更加地提升,但目前保險商品的推薦大多都是消費者主動聯絡各保險公司的業務專員,或是使用事先根據族群等預先分類的業務推薦組合或大部分的人所選擇的保單組合,但是消費者無法完全地理解此保險商品推薦的內容,進而去做保險商品的選擇。Nowadays, the social structure is changing, and life risks are everywhere, which makes the importance of insurance even more important. However, most of the current insurance product recommendations are made by consumers who actively contact the business specialists of various insurance companies, or use advance information based on ethnic groups, etc. The classified business recommendation combination or the policy combination selected by most people, but consumers cannot fully understand the recommended content of the insurance product, and then make the choice of the insurance product.
另外有一種推薦保險商品的方式,為引導消費者輸入各種資訊來對應其需求後,例如性別、年齡、職業,以及設計情境問卷等等,藉此推薦消費者相對應需求的至少一種保險商品,與其保險給付項目建議額度,以供消費者挑選。此一推薦保險商品的方式雖可以盡量引導消費者理解自身需求,但仍然無法使消費者理解其推薦組合的次序與消費者需求之間的關係,消費者還是無法選擇最適合自己需求的保險商品。Another way to recommend insurance products is to guide consumers to input various information to correspond to their needs, such as gender, age, occupation, and design contextual questionnaires, etc., thereby recommending at least one insurance product that corresponds to consumers’ needs. Suggest the amount of its insurance benefit items for consumers to choose. Although this method of recommending insurance products can guide consumers to understand their own needs as much as possible, it still cannot make consumers understand the relationship between the order of recommended combinations and consumers’ needs, and consumers still cannot choose the most suitable insurance products for their needs. .
本新型的目的在於提供一種保險商品的個人化推薦裝置,係根據接收的推薦演算法需求與輸入資訊,輸出至少一多層次保險推薦結果,其中,此保險商品的個人化推薦裝置包含訓練裝置以及運行模組。訓練裝置提供有複數個待訓練模組,且用於根據使用者歷史行為資訊與保險商品資訊對複數個待訓練模組進行訓練,以獲得複數個已訓練模型與複數個已訓練資料,並儲存於資料與模型資料庫中。運行模組訊號連接該訓練裝置,用於根據推薦演算法需求而從資料與模型資料庫儲存的複數個已訓練模組與複數個已訓練資料選取至少一個訓練模型與選取至少一個已訓練資料,並基於選取的至少一個已訓練資料與輸入資訊而運行選取的至少一個已訓練模組,進而產生上述的多層次保險推薦結果。其中,上述的多層次的保險推薦結果為第一層次推薦結果與第X層次推薦結果,其中X大於或等於2。The purpose of the present invention is to provide a personalized recommendation device for insurance products, which outputs at least one multi-level insurance recommendation result according to the received recommendation algorithm requirements and input information, wherein the personalized recommendation device for insurance products includes a training device and Run the mod. The training device provides a plurality of modules to be trained, and is used to train the plurality of modules to be trained according to the user's historical behavior information and insurance product information, so as to obtain a plurality of trained models and a plurality of trained data, and store them in the Data and Model Database. The running module signal is connected to the training device for selecting at least one training model and selecting at least one training data from a plurality of trained modules and a plurality of trained data stored in the data and model database according to the requirements of the recommended algorithm, and run the selected at least one trained module based on the selected at least one trained data and the input information, thereby generating the above-mentioned multi-level insurance recommendation result. The above-mentioned multi-level insurance recommendation results are the first-level recommendation results and the X-th level recommendation results, where X is greater than or equal to 2.
可選地,上述的多層次保險推薦結果更包括第J層推薦結果,其中J不等於X。Optionally, the above-mentioned multi-level insurance recommendation result further includes the J-th level recommendation result, where J is not equal to X.
可選地,上述的第一層次推薦結果包括至少一個推薦險種,第二層次推薦結果包括上述的推薦險種的至少一種推薦保險商品類型,第三層次推薦結果為上述的推薦險種中的推薦保險商品類型的至少一理賠給付項目額度。Optionally, the above-mentioned first-level recommendation result includes at least one recommended insurance type, the second-level recommendation result includes at least one recommended insurance product type of the above-mentioned recommended insurance type, and the third-level recommendation result is the recommended insurance in the above-mentioned recommended insurance types. The amount of at least one claim payment item of the commodity type.
可選地,上述的多層次推薦結果更包含第四層推薦結果,此第四層推薦結果為至少一個推薦保險商品。Optionally, the above-mentioned multi-level recommendation result further includes a fourth-level recommendation result, and the fourth-level recommendation result is at least one recommended insurance product.
可選地,上述的保險商品的個人化推薦裝置更包含使用者行為收集裝置,用以透過使用者裝置收集使用者行為,且使用者行為歷史資訊由使用者行為決定。Optionally, the above-mentioned personalized recommendation device for insurance products further includes a user behavior collection device for collecting user behavior through the user device, and the user behavior history information is determined by the user behavior.
可選地,複數個使用者係具有一唯一識別,並關聯於該使用者歷史行為資訊。Optionally, a plurality of users have a unique identification and are associated with the user's historical behavior information.
可選地,上述的保險商品的個人化推薦裝置更包含前端裝置訊號連接該運行裝置,用於接收推薦演算法需求與輸入資訊。Optionally, the above-mentioned personalized recommendation device for insurance products further includes a front-end device signal connected to the running device for receiving recommendation algorithm requirements and input information.
可選地,上述的保險商品的個人化推薦裝置更包含該資料與模型資料庫,訊號連接該運行裝置與該訓練裝置。Optionally, the above-mentioned personalized recommendation device for insurance products further includes the data and model database, and the signal connects the running device and the training device.
可選地,上述的保險商品的個人化推薦裝置更包含使用者歷史行為資料庫訊號連接該訓練裝置,用於儲存使用者歷史資訊,並傳送使用者行為歷史資訊給訓練裝置,以及保險商品資料庫,訊號連接訓練裝置,用於儲存複數個保險商品資訊。Optionally, the above-mentioned personalized recommendation device for insurance products further includes a user history behavior database signal connected to the training device for storing user history information, and transmitting the user behavior history information to the training device and insurance product data. The library, the signal is connected to the training device, is used to store the information of a plurality of insurance products.
綜合以上所述,本新型的保險商品的個人化推薦裝置可以讓使用者根據依照個人的需求選擇保險商品,且更能理解其推薦組合的次序與其自身需求之間的關係,以供使用者選擇最適合需求的保險商品。Based on the above, the personalization recommendation device for insurance products of the present invention allows users to select insurance products according to their personal needs, and can better understand the relationship between the order of recommended combinations and their own needs for users to choose. The best insurance product for your needs.
為使所屬技術領域之通常知識者進一步了解本新型創作的技術特徵、內容與優點及其所能達成之功效,以下茲以適當實施例配合圖式之表達形式詳細說明本新型的內容,實施例僅為示意及輔助說明本新型創作之用,非侷限本新型創作於實際實施例上的權利範圍。In order to make those skilled in the art further understand the technical features, contents and advantages of the novel creation and the effects that can be achieved, the following describes the contents of the novel in detail with appropriate embodiments in conjunction with the representations of the drawings. It is only for the purpose of illustrating and assisting the description of the present invention, and is not intended to limit the scope of rights of the present invention in the actual embodiments.
有鑑於先前技術的問題,本新型主要提供一種可以根據使用者自身需求的保險商品的個人化推薦裝置,且能夠讓使用者理解其推薦組合的次序與其自身需求之間的關係,以供使用者選擇最適合需求的保險商品。In view of the problems of the prior art, the present invention mainly provides a personalized recommendation device for insurance products according to the user's own needs, and enables the user to understand the relationship between the order of the recommended combination and his own needs, so that the user can Choose the insurance product that best suits your needs.
請參閱圖1,圖1為根據本新型實施例之一種保險商品的個人化推薦裝置 300 的功能方塊示意圖。上述的保險商品的個人化推薦裝置 300,係根據接收的推薦演算法需求與輸入資訊,輸出至少一個多層次保險推薦結果,上述的保險商品的個人化推薦裝置 300 包含訓練裝置 310 以及運行裝置 310。上述的訓練裝置 320 提供有複數個待訓練模組,且用於根據使用者歷史行為資訊與保險商品資訊對上述的複數個待訓練模組進行訓練,以獲得複數個已訓練模型與複數個已訓練資料,並儲存於資料與模型資料庫 330 中。運行裝置 310 訊號連接該訓練裝置 320,用於根據上述的推薦演算法需求而從上述的資料與模型資料庫 330 儲存的上述的複數個已訓練模組與上述的複數個已訓練資料選取至少一個訓練模型與選取至少一個已訓練資料,並基於選取的該至少一個已訓練資料與上述的輸入資訊而運行選取的上述的至少一個已訓練模組,進而產生上述的多層次保險推薦結果。Please refer to FIG. 1 . FIG. 1 is a functional block diagram of a personalization recommendation device 300 for insurance products according to an embodiment of the present invention. The above-mentioned personalized recommendation device 300 for insurance products outputs at least one multi-level insurance recommendation result according to the received recommendation algorithm requirements and input information. The above-mentioned personalized recommendation device 300 for insurance products includes a training device 310 and an operation device 310. . The above-mentioned training device 320 is provided with a plurality of modules to be trained, and is used for training the above-mentioned plurality of modules to be trained according to the user's historical behavior information and insurance product information, so as to obtain a plurality of trained models and a plurality of trained models. The training data is stored in the data and
此外,上述的多層次保險推薦結果包括第一層次推薦結果第一層次推薦結果為至少一推薦險種,而第二層次推薦結果包括上述推薦險種的至少一種推薦保險商品類型。In addition, the above-mentioned multi-level insurance recommendation result includes a first-level recommendation result. The first-level recommendation result is at least one recommended insurance type, and the second-level recommendation result includes at least one recommended insurance product type of the above-mentioned recommended insurance type.
進一步地說明,上述的第一層次推薦結果包括至少一推薦險種,例如意外險、醫療險、癌症險、失能險、壽險、重大傷病險等。上述的第二層次推薦結果包括上述推薦險種的至少一種推薦保險商品類型,以意外險為例,有醫療實支的意外險或是住院有補助的意外險等。接著,更可以包含第三層次推薦結果,此第三層次推薦結果為上述的推薦險種中之推薦保險商品類型的至少一理賠給付項目額度,例如意外身故保險金為500萬,或是住院日額補助3000元等。此外,更進一步包含第四層次推薦結果之推薦保險商品,上述的第四層次推薦結果係根據上述的險種、上述的保險商品類型與上述理賠給付項目額度而產生的,例如某一保險公司的意外險安心保方案等。上述僅為舉例說明,並不以此為限制。To further illustrate, the above-mentioned first-level recommendation result includes at least one recommended insurance type, such as accident insurance, medical insurance, cancer insurance, disability insurance, life insurance, major injury and illness insurance, and the like. The above-mentioned second-level recommendation result includes at least one recommended insurance product type of the above-mentioned recommended insurance types. Taking accident insurance as an example, accident insurance with medical expenses or accident insurance with subsidies for hospitalization, etc. Then, it can further include a third-level recommendation result, where the third-level recommendation result is the amount of at least one claim payment item of the recommended insurance product type in the above-mentioned recommended insurance types, for example, the accidental death insurance benefit is 5 million, or the hospitalization day A subsidy of 3,000 yuan, etc. In addition, it further includes the recommended insurance products of the fourth-level recommendation results. The above-mentioned fourth-level recommendation results are generated according to the above-mentioned types of insurance, the above-mentioned insurance product types and the above-mentioned claims and payment items. For example, an accident of an insurance company is generated. Insurance plans, etc. The above is only an example, not a limitation.
在另一實施例中,上述的不同層次之間可以任意組合搭配而產生上述的多層次保險推薦結果,例如為第一層次的險種與第四層次的保險商品組成的多層次保險推薦結果,或是為第一層次的險種、第二層次的推薦保險商品類型及第四層次的推薦保險商品組成的多層次推薦結果。In another embodiment, the above-mentioned different levels can be arbitrarily combined and matched to generate the above-mentioned multi-level insurance recommendation result, for example, a multi-level insurance recommendation result composed of a first-level insurance type and a fourth-level insurance product, Or a multi-level recommendation result consisting of the first-level insurance types, the second-level recommended insurance product types, and the fourth-level recommended insurance products.
再者,上述的的保險商品的個人化推薦裝置 300 更包含使用者行為收集裝置,用以透過使用者裝置收集使用者行為,且上述的使用者行為歷史資訊由上述的使用者行為決定。在實際實施情況中,當使用者利用使用者裝置 100 點擊、瀏覽或選擇投保條件、保險商品、險種、保險類型等資訊時,這些點擊、瀏覽或選擇的行為皆會傳送至與使用者裝置 100 訊號連接的使用者行為收集裝置 600,並在經適當資料處理與轉換後,儲存至使用者歷史行為資料庫 204,換句話說,使用者歷史行為資料庫 400 中所儲存的使用者歷史行為資訊係由使用者行為決定。而且,每一個使用者係具有唯一識別,並與使用者歷史行為資訊產生關連。Furthermore, the above-mentioned personalized recommendation device 300 for insurance products further includes a user behavior collection device for collecting user behavior through the user device, and the above-mentioned user behavior history information is determined by the above-mentioned user behavior. In an actual implementation, when the user uses the
訓練裝置 320 中則提供有多個待訓練模型,並且,訓練裝置 320 會根據儲存在使用者歷史行為資料庫 400 中的使用者歷史行為資訊與儲存在一保險商品資料庫 500 中的保險商品資訊,而對複數個待訓練模型進行訓練,以獲得複數個已訓練模型與複數個已訓練資料,並儲存於資料與模型資料庫 330 中,以待發生推薦演算法需求時,用於產生至少一個人化多層次保險推薦結果。A plurality of models to be trained are provided in the training device 320 , and the training device 320 will, according to the user historical behavior information stored in the user
上述的輸入資訊包含一使用者資訊、一裝置資訊與上述的使用者歷史行為資訊。其中,上述的裝置資訊為使用者裝置類型,例如為手機、平板腦或是電腦等。上述的複數個使用者資訊包含性別、年齡、財務狀況、工作類別、家庭組成、交通習慣、生活習慣、休閒興趣以及保險特定需求其中至少一項。接著,前端裝置 200 將上述的至少一個保險推薦結果至使用者裝置 100,並提供給使用者。The above-mentioned input information includes a user information, a device information and the above-mentioned user historical behavior information. The above-mentioned device information is the type of the user's device, such as a mobile phone, a tablet, or a computer. The above-mentioned plurality of user information includes at least one of gender, age, financial status, work type, family composition, transportation habits, living habits, leisure interests, and insurance specific needs. Next, the front-end device 200 sends the above at least one insurance recommendation result to the
更進一步地的說明,透過輸入資訊產生個人化的保險推薦結果。以上述的複數個使用者資訊為基礎,能夠瞭解使用者的基本保險需求,在藉由上述的使用者歷史行為資訊預測使用者的潛在需求,使得運行裝置 310 可據此產生至少一個人化多層次保險推薦結果,且此保險推薦結果能夠更接近使用者的自身需求。To further illustrate, generate personalized insurance recommendation results by entering information. Based on the above-mentioned plurality of user information, the basic insurance needs of the user can be understood, and the potential needs of the user can be predicted based on the above-mentioned user historical behavior information, so that the operating device 310 can generate at least one personalized multi-level based on the above. Insurance recommendation results, and the insurance recommendation results can be closer to the user's own needs.
舉例而言,使用者透過使用者裝置 100 輸入個人資訊,為一男性、年齡35歲、已婚且育有一子、職業為外送員等資訊,上述的保險商品的個人化推薦裝置 300 依據上述的個人資訊從資料與模型資料庫 330 中取得最適合的資料與模型做運行後,產生至少一個人化多層次保險推薦結果,依序會先推薦適合的一個或多個的險種,例如意外險、醫療險、癌症險、失能險、壽險以及重大傷疾病險 ( 第一層次推薦結果,僅為舉例說明,不以此為限制 ) 等,再者,在每個險種裡再推薦一個或多個保險商品類型,假設使用者因職業的原因選擇了意外險的選項,其保險商品類型有基礎的意外險、有醫療實支的意外險、有住院補助的意外險、外勤族常需加保的交通意外險以及常出遊的意外險 ( 第二層次推薦結果,僅為舉例說明,不以此為限制 ) 等。接著,再為保險商品類型推薦一個或多個給付項目額度,例如意外險身故保險金為五百萬元、住院日額3000元、住院實支實付10萬 ( 第三層次推薦結果,僅為舉例說明,不以此為限制 ) 等,並且在每個保險商品類型裡依照使用者的個人資訊推薦商品。For example, the user inputs personal information through the
依照上述的舉例,使用者可依據自己職業的原因選擇意外險的選項 ( 第一層次推薦結果,僅為舉例說明,不以此為限制 ),以了解各種意外險的內容,例如外勤族常需加保的交通意外險,且使用者育有一子,可能經常陪伴小孩出遊,也可以了解常出遊的意外險 ( 第二層推薦結果,僅為舉例說明,不以此為限制 )。此外,此保險商品的個人化推薦裝置 300 也會依據此使用者的需求直接推薦一個或多個某保險公司的保險商品 ( 第四層次推薦結果,僅為舉例說明,不以此為限制 ),使用者可依照當時自己的經濟能力及偏好做選擇。According to the above example, users can choose the option of accident insurance according to their own occupational reasons (the first-level recommendation results are for example only, not limited), so as to understand the content of various accident insurances, such as the frequent occurrence of field workers. The traffic accident insurance that needs to be insured, and the user has a child, may accompany the child on trips often, and can also learn about the accident insurance for frequent trips (the second-level recommendation results are for example only, not limited). In addition, the personalized recommendation device 300 for insurance products will also directly recommend one or more insurance products of a certain insurance company according to the needs of the user (the fourth-level recommendation result is for illustration only and not limited thereto), Users can choose according to their own financial ability and preferences at that time.
除此之外,使用者透過使用者裝置 100 於上述至少一個人化多層次保險推薦結果中選擇險種、保險商品類型、調整給付項目額度與保險商品,並配合歷史使用者行為資訊,例如使用者選擇的投保條件、險種、保險類型、給付項目、保險公司、保險規格屬性、調整給付額度以及諮詢與留言等資訊,以上資訊會傳送至使用者收集裝置並經過適當資料處理與轉換後,儲存於使用者歷史行為資料庫 400 中,以作為不斷地的調整上述的訓練模型之用途。In addition, through the
綜上所述,本新型的保險商品個人化推薦裝置 300,可提供給使用者根據自身的條件與需求產生一多層次保險推薦結果,且每個保險商品類型裡則有其推薦商品的排序,讓使用者可以理解其推薦結果的次序與消費者需求之間的關係,讓使用者可以選擇最適合自己需求的保險商品。除此之外,使用者歷史行為資料會儲存於使用者歷史行為資料庫中,以作為不斷地的調整上述的訓練模型之用途,以強化本新型的保險商品個人化推薦裝置對於使用者的需求的準確度。To sum up, the novel insurance product personalization recommendation device 300 can provide users with a multi-level insurance recommendation result according to their own conditions and needs, and each insurance product type has its recommended product ranking. It allows users to understand the relationship between the order of their recommended results and consumer needs, so that users can choose the most suitable insurance products for their needs. In addition, the user's historical behavior data will be stored in the user's historical behavior database for the purpose of continuously adjusting the above-mentioned training model, so as to strengthen the needs of the new type of insurance product personalization recommendation device for users accuracy.
本新型在本文中僅以較佳實施例揭露,然任何熟習本技術領域者應能理解的是,上述實施例僅用於描述本新型,並非用以限制本新型所主張之專利權利範圍。舉凡與上述實施例均等或等效之變化或置換,皆應解讀為涵蓋於本新型之精神或範疇內。因此,本新型之保護範圍應以下述之申請專利範圍所界定者為準。The present invention is only disclosed in preferred embodiments herein. However, any person skilled in the art should understand that the above-mentioned embodiments are only used to describe the present invention and are not intended to limit the scope of the claimed patent rights of the present invention. All changes or substitutions that are equal or equivalent to the above embodiments should be construed as being covered within the spirit or scope of the present invention. Therefore, the protection scope of this new model shall be defined by the following patent application scope.
100:使用者裝置 200:前端裝置 300:保險商品的個人化推薦裝置 310:運行裝置 320:訓練裝置 330:資料與模型資料庫 400:使用者歷史行為資料庫 500:保險商品資料庫 600:使用者行為收集裝置100: User device 200: Front end device 300: Personalized recommendation device for insurance products 310: Running Device 320: Training Device 330: Data and Model Database 400: User history behavior database 500: Insurance product database 600: User behavior collection device
提供的附圖用以使本新型所屬技術領域具有通常知識者可以進一步理解本發明,並且被併入與構成本新型之說明書的一部分。附圖示出了本新型的示範實施例,並且用以與本新型之說明書一起用於解釋本發明的原理。以下為本新型各圖的簡單說明: 圖1為根據本新型實施例之一種保險商品的個人化推薦裝置的功能方塊示意圖。 The accompanying drawings are provided so that those skilled in the art to which the present invention pertains may further understand the present invention, and are incorporated in and constitute a part of the description of the present invention. The drawings illustrate exemplary embodiments of the invention, and together with the description of the invention serve to explain the principles of the invention. The following is a brief description of each figure of this new model: FIG. 1 is a functional block diagram of a personalization recommendation device for insurance products according to an embodiment of the present invention.
100:使用者裝置 100: User device
200:前端裝置 200: Front end device
300:保險商品的個人化推薦裝置 300: Personalized recommendation device for insurance products
310:運行裝置 310: Running Device
320:訓練裝置 320: Training Device
320:資料與模型資料庫 320: Data and Model Database
400:使用者歷史行為資料庫 400: User history behavior database
500:保險商品資料庫 500: Insurance product database
600:使用者行為收集裝置 600: User behavior collection device
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