Product design and product produced according to design

Abstract

The invention provides a method of generating a product concept, comprising identifying from an attitudinal data asset factors relating to a concept to be created, providing the factors to a generativ

Description

The present invention relates to a method of product design and a product produced according to the design. The invention also relates to process design and a process optimised according to the process design. The use of consumer insight data is well-known and typically such data is used the world over to enable producers of products and services to generate and modify products and services to suit their potential markets. From the perspective of a supplier, e.g. a company or person producing a service or product for sale the generation of product ideas can be achieved in any number of known ways. One typical route that relies upon the use of insight data is generally as follows. The following description is with reference to a company producing a product although this is for the purpose of explanation only. It will be understood that the process and method apply to a company or individual involved in generating a process as well. Initially, referring to Figure 1 which shows schematically a product design process, a company management might instruct 2 its marketing team to create or refine a concept, asset, advertisement, brand or design for a new consumer product or service which has not previously been revealed to the market. For example, the instruction in respect of the new product might be: “We want to create a new beer brand that appeals to men aged 18-35 in Texas”. Conventionally, the company’s marketing team creates 4 a draft document containing text, images, video, design specifications, taglines, branding assets etc, setting out details of the potential new product. This document with the various product descriptions and classifications is defined as a “concept” in a product or process lifecycle. Prior to commercial launch of the product, the company typically tests the market appeal of the proposed new product, its proposed advertising and / or its proposed packaging. This is usually done in by testing 6 the concept in one or more consumer insights surveys. The company identifies the kind of consumers to whom it wants the new product to appeal, e.g. in the case of the product specified above, this could be: “males, aged 18-35, based in Texas, who like drinking beer.” The surveys then generated by an insight team, which could be internal to the company or acting externally as an agent, would be targeted at individuals with those characteristics. The survey would typically reveal the concept to consumers in the group and measure and gather data relating to the responses from group members. This gathered data set might be referred to as the “Insights”. The Insights are gathered together and are typically displayed 8 within a report for explaining to the marketing team of the company how consumers responded to the concept and how the concept compares to other stimuli within the same genre of product or service. The company’s marketing team is then able to review the report and manually update 10 and refine the concept to improve the market appeal of the new product before commercial launch. Depending on timescales and project budgets, the process of steps 6 to 10 can be repeated any number of times or alternatively, the output of the improved concept after manual modification based on the insight report is output 12 as a product (or process) design. The improved new product design 12 is provided to a manufacturing process 14, either directly automatically or via human interaction and the product is manufactured for launch to the public. This is a well know sequence of steps that works well and produces product designs for manufacture in which the originating individual or company can have some degree of commercial confidence. However, improvements in the process are desired. According to a first aspect of the present invention, there is provided a method of product concept design, the method comprising: receiving a product concept; identifying themes and their relevance based on an attitudinal data asset in relation to the received product concept; providing the product concept and the identified themes and their relevance as an input to generative Al to produce the optimised product concept design. The invention provides a method of product design or product concept design which enables optimisation of the concept or design using generative Al. However, the method goes well beyond this since it relies not only on the use of generative Al but also factors derived from an attitudinal data asset in relation to the received product concept. The factors could typically be themes from the collected data that forms the attitudinal data asset that correspond to improved or worsening performance. By testing the outputs of the process the effect of the use of the factors, such as the themes and their relevance, significantly enhances and improves the perception and reception of products or concepts so generated. In one example, the output is provided as a design directly to a manufacturing facility enabling a streamlined and simplified robust method for product design optimisation. Preferably, the generated design is output to a manufacturing facility enabling the simplified manufacture of an optimised product. The method can apply to concepts related to any sort of product, but typically would relate to food and drink packaging (including alcohol and tobacco), household, health and personal care products, apparel and accessories, veterinary products, consumer electronics and technology products, telecommunications and social media services, gaming products and services, automotives, travel services, and entertainment and media. According to a second aspect of the present invention, there is provided a method of generating a product concept, the method comprising identifying from an attitudinal data asset factors relating to a concept to be created; providing the factors to a generative Al and producing the concept with the generative Al. According to a third aspect of the present invention, there is provided a method of generating a product design for manufacture, the method comprising identifying a target for use of the product to be manufactured; extracting themes from a data set to appeal to the target; provide the extracted themes as an input to a generative language model prompt; execute the language model prompt to generate a product design based on the inputs to the generative language prompt. Embodiments of the present invention will now be described in detail with reference to the accompanying drawings, in which: Figure 1 is a schematic flow chart showing a known process of concept design and manufacture in relation to a product concept; Figure 2 is a schematic flow chart showing an exemplary process of concept design and modification and manufacture in relation to a product concept; Figure 3 is a schematic flow chart showing an exemplary process of concept creation in relation to a product concept; Figures 4A to 4C show exemplary outputs from the processes of Figures 1 and 2; Figures 5 and 6 show results for the product concepts shown in Figures 4A to 4C; and Figure 7 is a schematic view of a manufacturing facility. The present inventors have recognised that an improved method of product design and product manufacture can be achieved using a combination of market gathered insight data with a generative Al system. Whereas, the use of generative Al as a standalone concept is known, the present inventors have created a product design and manufacturing method and system that goes beyond this by combining the use of themes extracted from an attitudinal data set and / or market insight data with generative Al. Results, discussed and shown below, demonstrate that the output in terms of subsequent user interaction exceeds what would be expected simply from the use of Al or insight data using known methods of concept generation and subsequent manufacture of the generated product designs. The improvements in terms of technically measurable output goes beyond the sum of the improvements expected from using, individually, extracted themes / insight data or generative Al. The output of the process goes beyond the mere sum of the combination of the improvements, demonstrating a synergistic technical effect. Manufactured products that are produced according to designs or concepts generate din the specified manner represent a technical improvement on the products manufactured using either of the two methodologies alone. Furthermore, although the description thus far has been in relation to the production of products, it applies similarly to processes. For example, a technical manufacturing process can itself be thought of as a concept in the manner defined herein. The improved process will now be described in detail with reference to the example of Figure 2. The novel method and process disclosed herein, in a general sense, removes the necessity for generating an insight report (as described above with reference to Figure 1). Providing manual modification of a concept based on interpretation of the report, can be avoided entirely. Thus, steps 8 and 10 from Figure 1 can be avoided and instead a step of providing an automatic language model prompt is used, to be described in greater detail below. Referring to Figure 2, again creation of a concept 16 is instructed and at step 18 the concept is created, analogous to steps 2 and 4 of the method of Figure 1. Consumer insight testing is preferably performed at step 20 and the outputs provided to an automatic language model prompt modification stage 22. The automatic language model prompt stage 22 will typically use generative Al together with an existing attitudinal data asset by identifying factors such as themes and their relevance. The themes 21 are provided as an input to the automatic language model prompt 22. Optionally, results of consumer insight testing can also be provided as inputs to the automatic language model prompt. A more detailed example will be given below, but the skilled person will understand that what is being used is a combination of generative Al with intelligence from an existing data asset to improve significantly the output from the generative Al alone. The process 22 of executing the automatic language model prompt modification can be repeated any desired number of times in response to updating of consumer insight testing 20 or data set derived themes. Referring again to Figure 2, again it can be seen that the output from the concept creation stage 18 can be provided as an optimised product design 24 for onward forwarding directly to manufacture 26, or if desired the cycle of stages 18, 20 and 22 a can be repeated. The process for optimised product design can be performed remotely, say in the cloud, or it can be performed locally to a manufacturing facility. In either case it is possible, and well known to a skilled person how the required design details for manufacture can be provided in an appropriate format to the manufacturing facility. This could depend on the type of product to be manufactured but could typically include a data file specifying any or all of the features, components, ingredients, or parameters etc of the product to be manufactured. These can of course vary depending on what the product is as will be understood by a skilled person. A further example is described with reference to Figure 3. In this example, instead of modifying an initial concept created in a conventional way, the method is used to create a new concept itself based on, amongst other factors, the category and brand. Like the example of Figure 2, improved product is produced using a combination of market gathered insight data with a generative Al system. In this example, the concept itself is generated without initial human design input. Referring to the Figure 3, at 28 a user is prompted to identify a relevant cohort of data to leverage. Typically, but depending on product, this could include a country, category, brand and other basic demographics (such as age and gender). This information is used to extract 30 response themes from an existing data asset, as well as their corresponding attitudinal metrics. The user can then optionally screen 32 the themes for appropriateness, and the selection is injected into a language model prompt 34. The prompt itself is an instruction to generate a new concept or idea, with the same parameters as the user defined scope, and to ensure that the selected themes are either emphasised or de-emphasised as appropriate. Other attributes relevant to the concept, such as the tone of voice can also be captured and injected into the prompt at step 34. The prompt 34 is then arranged and configured, based on generative Al and the input themes determined from the existing data set to generate the concept 36 in a specified or selected format. The user can optionally (manually) adjust any of the inputs, e.g. at step 32, that are used to generate the concept. Alternatively, or as well, if the user so desires manual modifications can be made to the generated concept 36 itself. A user is able manually to specify desired parameters or qualities associated with the concept, either to be included within and / or excluded from the concept to be generated. The concept as generated at step 36 may typically be simply a description and so this is then preferably provided to an image generation API for creation of an accompanying image. Regarding manual modifications, an example could be, if, say a concept or design for a snack that is designed to offer a delayed energy release is generated, but the owner or company for whom the snack concept or product is being designed does not want the word "protein" to be explicitly used in the generated concept. In such a case, the prompt can be manually amended to instruct this, e.g. "Do not use the term “protein” in the concept". Although not shown in Figure 3, it will be understood that insight testing can be performed on the generated concept and this used as an input again to the automatic language model prompt. It will be understood that the concept as prepared can be used itself as an input to the process described above with reference to Figure 2, i.e. at step 18 in Figure 2. The present method utilizes an existing data insight data set including optionally attitudinal data asset by identifying themes from collected data that correspond with improved or worsening performance, and injects those themes (and their relevance) into a language model prompt, which is an instruction designed to generate or improve a concept or product that maximises (or minimises) the identified themes. It will be appreciated from the description above, that the approach can be used to either generate a new concept, where the scope of data queried corresponds with the user defined country, category and brand, or to optimise a tested concept, where the data used is that from a previous round of testing. Figures 4A to 4C show exemplary outputs of designs for products (lubricants in this example) all obtained using the same generative Al engine. The designed outputs include an actual product container in each case but also an environment and background consistent with the inputs provided and the themes / insight data used. Figure 4A, “Euphorphase”, is a simple design that has been produced simply using generative Al, with a simple prompt. No contextual data has been used, either manually input or in any way generated automatically, Figure 4B, “KY Harmony”, which appears as a more refined product and as will be explained below quantitatively performs better in terms of appeal to potential users was generated using the same generative Al engine in combination with five existing concepts also used as input to the engine. The produced design and concept is more appealing than that produced only by the use of the engine with the prompt. Figure 4C, “Durex Elements, Natural Harmony”, shows a final example in which as well as the data used to generate the concept of Figure 4B, factors or themes from an existing insight database were used to further improve the generated product design and concept. In the example shown the themes could be, say, “nature”, “water”, “simplicity”, “purity”, etc. Although these are in themselves subjective themes within the context of this example, the idea of use of themes is clear and would be well understood by a skilled person. The inventors have recognised that by using themes from an existing attitudinal data set the output of a generative Al system in terms of user experience and appreciation can be improved. In the particular, example of the coupling of such a process with manufacture a simple and reliable way of producing user-optimised product is provided. Themes can be identified by taking individual verbatim responses against existing concepts, applying a clustering algorithm to groups responses of a similar nature, and then separately using OpenAI to identify a theme that represents the cluster of verbatims. Examples includes "Packaging or branding concerns" or "Lack of detailed information". Another source of themes can be category trackers that track various attributes specific to a category, and how respondents prioritise them (for example "Price", "Packaging" etc). The three products / concepts were then examined in an insight study against a target group and results, shown in Figures 5 and 6, gathered. The results in respect of the three examples, labelled A to C in the bar charts of Figures 5 and 6, show that the Durex Elements, Natural Harmony concept, created using the method of the present invention performed better than the other two in all categories apart from uniqueness. The results are substantially consistent across all measured parameters. For example, considering Overall Appeal (Figure 5), the results show that the Durex Elements, Natural Harmony concept was 5% improved compared to the norm. For relevance, the Durex Elements, Natural Harmony concept was 4% improved compared to the norm, and for Behaviour Change, it was 9% improved compared to the norm. Significantly, the results for the Durex Elements, Natural Harmony concept as compared to the KY Harmony concept was also positive. For overall appeal, the relative improvement with respect to the norm as compared to KY harmony’s performance with respect to the norm was an improvement of 0.6 as compared to 0.4, i.e., a 50% improvement in the difference. For relevance, the relative improvement with respect to the norm as compared to KY Harmony’s performance with respect to the norm was an improvement of 0.4 as compared to 0.1, i.e., a factor of 4 (0.4 / 0.1) relative improvement. Overall, the use of a method of product design that combines generative Al with bespoke consumer insight enables significantly improved products to be designed and produced. Figure 7 shows schematically a system for product production using the method described herein. The system 38 comprises a user terminal 40 coupled either to a communications network 42 such as the internet or alternatively directly to a manufacturing facility 44. The user terminal 40 can be a computer arranged to run software to execute the method of product design described herein. Alternatively, it can be an interface arranged to couple to functionality hosted remotely, such as in the network 42. Either way the output of the process constitutes an optimised concept or product design (24 in figure 2, or 36 in Figure 3), is preferably communicated to a manufacturing facility shown schematically 44. It will be appreciated that the process has a technical output itself in the form of the optimised product design, which is preferably communicated directly to the manufacturing facility or alternatively can be stored as an optimised design for manufacture at a desired time. Embodiments of the present invention have been described with particular reference to the examples illustrated. However, it will be appreciated that variations and modifications may be made to the examples described within the scope of the present 5 invention.

Claims

1. A method of product concept modification, the method comprising receiving a product concept;identifying factors from on an attitudinal data asset in relation to the received product concept;providing the product concept and the factors as an input to generative Al to produce a modified product concept.

2. A method according to claim 1, comprising conducting consumer insight testing on the received product concept and providing output from the consumer insight testing to the generative Al together with the identified factors from the attitudinal data asset.

3. A method according to claim 1 or 2, comprising, providing the modified product concept as an input to a manufacturing process.

4. A method according to claim 1, in which the method is performed in the cloud remotely from a user terminal.

5. A method of generating a product concept, the method comprising identifying from an attitudinal data asset factors relating to a concept to be created;providing the factors to a generative Al and producing the concept with the generative Al.

6. A method according to claim 5, in which the factors comprise themes for emphasis or de-emphasis in the generated product concept.

7. A method according to claim 5 or 6, comprising providing the generated concept to image generation application to generate corresponding imagery associated with the concept.

8. A method according to any of claims 5 to 7, in which, upon generation of the concept, manual input is provided to specify desired parameters or qualities associated with the concept.5 9. A method of product manufacture comprising, generating a product or conceptdesign according to a product concept produced or modified by the method of any of claims 1 to 8; andmanufacturing the product according to the design.o10. A method according to claim 9, comprising generating a data file specifying the generating a product or concept design, the data file specifying any or all of features, components, ingredients, or parameters of the product to be manufactured.14

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